commit 925e56bb5f51139c76fc8672489ad1ab885073c2
Author: wehub-resource-sync <resource-sync@wehub.local>
Date:   Mon Jul 13 13:24:56 2026 +0800

    chore: import upstream snapshot with attribution

diff --git a/.github/ISSUE_TEMPLATE/breaking-bug-report.md b/.github/ISSUE_TEMPLATE/breaking-bug-report.md
new file mode 100644
index 0000000..e4f8083
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/breaking-bug-report.md
@@ -0,0 +1,62 @@
+---
+name: Breaking bug report
+about: Create a report about a breaking bug
+title: "[BUG: Breaking]"
+labels: 'bug: breaking'
+assignees: ''
+
+---
+
+## 🧨 Describe the Bug
+
+A clear and concise description of the breaking issue (e.g., crash, OOM, exception, etc).
+
+## 📄 Input Document
+
+Attach the PDF or input file that triggered the error.
+
+## 📤 Output Trace / Stack Trace
+
+Paste the **complete** stack trace or error output, if available.
+
+<details>
+<summary>Click to expand</summary>
+
+```
+Paste stack trace here
+```
+
+</details>
+
+## ⚙️ Environment
+
+Please fill in all relevant details:
+
+- **Marker version**: 
+- **Surya version**: 
+- **Python version**: 
+- **PyTorch version**: 
+- **Transformers version**: 
+- **Operating System** (incl. container info if relevant): 
+
+## ✅ Expected Behavior
+
+What did you expect Marker to do?
+
+## 📟 Command or Code Used
+
+Paste the **exact bash command** or **Python code** you used to run Marker:
+
+<details>
+<summary>Click to expand</summary>
+
+```bash
+# or Python code block
+your_command_here --with-flags
+```
+
+</details>
+
+## 📎 Additional Context
+
+Any other context that might help us debug this (e.g., CLI options, working directory, runtime settings).
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000..d200daa
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,24 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: "[FEAT]"
+labels: enhancement
+assignees: ''
+
+---
+
+## ✨ Is your feature request related to a problem?
+
+A clear and concise description of what the problem is. 
+
+## 💡 Describe the Solution You'd Like
+
+A concise description of what you want to happen or how you envision it working.
+
+## 📋 Alternatives Considered
+
+Any alternative solutions or workarounds you've tried.
+
+## 🧩 Additional Context
+
+Any additional context, references, or related issues.
diff --git a/.github/ISSUE_TEMPLATE/output-bug-report.md b/.github/ISSUE_TEMPLATE/output-bug-report.md
new file mode 100644
index 0000000..ed1e174
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/output-bug-report.md
@@ -0,0 +1,57 @@
+---
+name: Output bug report
+about: Create a report about poor output quality
+title: "[BUG: Output]"
+labels: 'bug: output'
+assignees: ''
+
+---
+
+## 📝 Describe the Output Issue
+
+A clear and concise description of the incorrect or unexpected output.
+
+## 📄 Input Document
+
+Attach the PDF or input file used.
+
+## 📤 Current Output
+
+Paste the Markdown or HTML that Marker generated:
+
+````markdown
+Paste output here
+`````
+
+## ✅ Expected Output
+
+Describe or paste what you expected Marker to generate.
+
+## ⚙️ Environment
+
+Please fill in all relevant details:
+
+* **Marker version**:
+* **Surya version**:
+* **Python version**:
+* **PyTorch version**:
+* **Transformers version**:
+* **Operating System**:
+
+## 📟 Command or Code Used
+
+Paste the **exact bash command** or **Python code** you used to run Marker:
+
+<details>
+<summary>Click to expand</summary>
+
+```bash
+# or Python code block
+your_command_here --with-flags
+```
+
+</details>
+
+## 📎 Additional Context
+
+Any other relevant info, configs, or assumptions.
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..98e6f0b
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,30 @@
+name: Unit tests
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [t4_gpu, ubuntu-latest, windows-latest]
+      fail-fast: false
+    env:
+      # T4 can't run bf16 in vllm; size for the 16GB card. (No-op on the
+      # CPU runners, which skip the VLM-backed tests.)
+      VLLM_DTYPE: float16
+      VLLM_GPU_TYPE: t4
+      SURYA_INFERENCE_STARTUP_TIMEOUT: "1200"
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: latest
+          enable-cache: true
+      - name: Set up Python 3.11
+        run: uv python install 3.11
+      - name: Install dependencies
+        run: uv sync --frozen --group dev
+      - name: Run tests
+        run: uv run pytest
\ No newline at end of file
diff --git a/.github/workflows/cla.yml b/.github/workflows/cla.yml
new file mode 100644
index 0000000..99217a3
--- /dev/null
+++ b/.github/workflows/cla.yml
@@ -0,0 +1,32 @@
+name: "Surya CLA Assistant"
+on:
+  issue_comment:
+    types: [created]
+  pull_request_target:
+    types: [opened,closed,synchronize]
+
+# explicitly configure permissions, in case your GITHUB_TOKEN workflow permissions are set to read-only in repository settings
+permissions:
+  actions: write
+  contents: write
+  pull-requests: write
+  statuses: write
+
+jobs:
+  CLAAssistant:
+    runs-on: ubuntu-latest
+    steps:
+      - name: "Surya CLA Assistant"
+        if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I hereby sign the CLA') || github.event_name == 'pull_request_target'
+        uses: contributor-assistant/github-action@v2.3.0
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          # the below token should have repo scope and must be manually added by you in the repository's secret
+          # This token is required only if you have configured to store the signatures in a remote repository/organization
+          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+        with:
+          path-to-signatures: 'signatures/version1/cla.json'
+          path-to-document: 'https://github.com/datalab-to/surya/blob/master/CLA.md'
+          # branch should not be protected
+          branch: 'master'
+          allowlist: VikParuchuri
\ No newline at end of file
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..defbdc7
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,23 @@
+name: Python package
+on:
+  push:
+    tags:
+      - "v*.*.*"
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: latest
+          enable-cache: true
+      - name: Set up Python 3.11
+        run: uv python install 3.11
+      - name: Build package
+        run: uv build
+      - name: Publish package
+        env:
+          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }}
+        run: uv publish
diff --git a/.github/workflows/scripts.yml b/.github/workflows/scripts.yml
new file mode 100644
index 0000000..500f27a
--- /dev/null
+++ b/.github/workflows/scripts.yml
@@ -0,0 +1,40 @@
+name: Test CLI scripts
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: t4_gpu
+    env:
+      # T4 (Turing, compute 7.5) can't run bf16 in vllm; size vllm for the 16GB
+      # card and give the cold start (image pull + model download) headroom.
+      VLLM_DTYPE: float16
+      VLLM_GPU_TYPE: t4
+      SURYA_INFERENCE_STARTUP_TIMEOUT: "1200"
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: latest
+          enable-cache: true
+      - name: Set up Python 3.11
+        run: uv python install 3.11
+      - name: Install dependencies
+        run: uv sync --frozen --group dev
+      - name: Download benchmark data
+        run: |
+          wget -O benchmark_data.zip "https://drive.google.com/uc?export=download&id=1NHrdYatR1rtqs2gPVfdvO0BAvocH8CJi"
+          unzip -o benchmark_data.zip
+      - name: Test detection
+        run: uv run surya_detect benchmark_data/pdfs/switch_trans.pdf --page_range 0
+      # Spawn the vllm server once and reuse it across the OCR/layout/table
+      # steps (--keep_server) instead of paying a cold start three times.
+      - name: Test OCR
+        run: uv run surya_ocr benchmark_data/pdfs/switch_trans.pdf --page_range 0 --keep_server
+      - name: Test layout
+        run: uv run surya_layout benchmark_data/pdfs/switch_trans.pdf --page_range 0 --keep_server
+      - name: Test table
+        run: uv run surya_table benchmark_data/pdfs/switch_trans.pdf --page_range 0 --keep_server
+      - name: Test detection folder
+        run: uv run surya_detect benchmark_data/pdfs --page_range 0
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..0bad802
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,172 @@
+private.py
+.DS_Store
+local.env
+experiments
+test_data
+training
+wandb
+notebooks
+results
+data
+slices
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..a4632cc
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,12 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.9.10
+  hooks:
+    # Run the linter.
+    - id: ruff
+      types_or: [ python, pyi ]
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format
+      types_or: [ python, pyi ]
\ No newline at end of file
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..a3209d6
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,11 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it using the following metadata."
+title: "Surya: A lightweight framework for analyzing documents and PDFs at scale"
+authors:
+  - family-names: Paruchuri
+    given-names: Vikas
+  - name: Datalab Team
+date-released: 2025-05-13
+url: https://github.com/datalab-to/surya
+version: 1.0.0
+repository-code: https://github.com/datalab-to/surya
\ No newline at end of file
diff --git a/CLA.md b/CLA.md
new file mode 100644
index 0000000..cbea049
--- /dev/null
+++ b/CLA.md
@@ -0,0 +1,24 @@
+Surya Contributor Agreement
+
+This Surya Contributor Agreement ("SCA") applies to any contribution that you make to any product or project managed by us (the "project"), and sets out the intellectual property rights you grant to us in the contributed materials. The term "us" shall mean Endless Labs, Inc. The term "you" shall mean the person or entity identified below. 
+
+If you agree to be bound by these terms, sign by writing "I have read the CLA document and I hereby sign the CLA" in response to the CLA bot Github comment. Read this agreement carefully before signing. These terms and conditions constitute a binding legal agreement.
+
+1. The term 'contribution' or 'contributed materials' means any source code, object code, patch, tool, sample, graphic, specification, manual, documentation, or any other material posted or submitted by you to the project. 
+2. With respect to any worldwide copyrights, or copyright applications and registrations, in your contribution: 
+   - you hereby assign to us joint ownership, and to the extent that such assignment is or becomes invalid, ineffective or unenforceable, you hereby grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge, royalty free, unrestricted license to exercise all rights under those copyrights. This includes, at our option, the right to sublicense these same rights to third parties through multiple levels of sublicensees or other licensing arrangements, including dual-license structures for commercial customers; 
+   - you agree that each of us can do all things in relation to your contribution as if each of us were the sole owners, and if one of us makes a derivative work of your contribution, the one who makes the derivative work (or has it made will be the sole owner of that derivative work; 
+   - you agree that you will not assert any moral rights in your contribution against us, our licensees or transferees; 
+   - you agree that we may register a copyright in your contribution and exercise all ownership rights associated with it; and 
+   - you agree that neither of us has any duty to consult with, obtain the consent of, pay or render an accounting to the other for any use or distribution of vour contribution. 
+3. With respect to any patents you own, or that you can license without payment to any third party, you hereby grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge, royalty-free license to:
+   - make, have made, use, sell, offer to sell, import, and otherwise transfer your contribution in whole or in part, alone or in combination with or included in any product, work or materials arising out of the project to which your contribution was submitted, and
+   - at our option, to sublicense these same rights to third parties through multiple levels of sublicensees or other licensing arrangements. 
+If you or your affiliates institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the contribution or any project it was submitted to constitutes direct or contributory patent infringement, then any patent licenses granted to you under this agreement for that contribution shall terminate as of the date such litigation is filed.
+4. Except as set out above, you keep all right, title, and interest in your contribution. The rights that you grant to us under these terms are effective on the date you first submitted a contribution to us, even if your submission took place before the date you sign these terms. Any contribution we make available under any license will also be made available under a suitable FSF (Free Software Foundation) or OSI (Open Source Initiative) approved license. 
+5. You covenant, represent, warrant and agree that: 
+   - each contribution that you submit is and shall be an original work of authorship and you can legally grant the rights set out in this SCA; 
+   - to the best of your knowledge, each contribution will not violate any third party's copyrights, trademarks, patents, or other intellectual property rights; and 
+   - each contribution shall be in compliance with U.S. export control laws and other applicable export and import laws.
+You agree to notify us if you become aware of any circumstance which would make any of the foregoing representations inaccurate in any respect. Endless Labs, Inc. may publicly disclose your participation in the project, including the fact that you have signed the SCA. 
+6. This SCA is governed by the laws of the State of California and applicable U.S. Federal law. Any choice of law rules will not apply.
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..59711bc
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,51 @@
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
+
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+
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+
+"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
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+"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
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+
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+If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.
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+7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
\ No newline at end of file
diff --git a/MODEL_LICENSE b/MODEL_LICENSE
new file mode 100644
index 0000000..13b388a
--- /dev/null
+++ b/MODEL_LICENSE
@@ -0,0 +1,59 @@
+                   AI PUBS OPEN RAIL-M LICENSE (MODIFIED)
+
+Version 0.1, March 2, 2023 (Modified)
+http://licenses.ai/
+
+PLEASE READ THESE TERMS CAREFULLY BEFORE USING THE MODEL OR A DERIVATIVE WORKS OF THE MODEL MADE AVAILABLE IN CONNECTION WITH THESE TERMS.  BY DOWNLOADING, REPRODUCING, DISTRIBUTING OR USING THE MODEL OR A DERIVATIVE WORK OF THE MODEL IN ANY MANNER, YOU (“YOU”) AGREE TO BE BOUND BY THESE TERMS (THE “AGREEMENT”) TO THE EXCLUSION OF ALL OTHER TERMS. YOU REPRESENT AND WARRANT THAT YOU HAVE THE AUTHORITY TO ENTER INTO THIS AGREEMENT; IF YOU ARE ENTERING INTO THIS AGREEMENT ON BEHALF OF AN ORGANIZATION OR ENTITY, REFERENCES TO AND “YOU” IN THIS AGREEMENT, REFER TO THAT ORGANIZATION OR ENTITY. IF YOU DO NOT AGREE TO ALL OF THE FOLLOWING, YOU MAY NOT DOWNLOAD, REPRODUCE, DISTRIBUTE OR USE THE MODEL OR A DERIVATIVE WORK OF THE MODEL IN ANY MANNER.
+ Section  I:  PREAMBLE
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+The “Open” nomenclature indicates that the licensed Model is be freely accessible to downstream and other users.  The “RAIL” nomenclature indicates that there are use restrictions prohibiting the use of the Model. These restrictions are intended to avoid potential misuse. This License specifies that the  use restrictions in the original License must apply to such derivatives.
+NOW THEREFORE, You and Licensor agree as follows:
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+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions, consistent with paragraph 4.a., for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Accordingly, You cannot use the Model or the Derivatives of the Model in violation of such restrictions. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, fine-tuning, updating, running, training, evaluating and/or re-parametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph 5.
+6.  The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are solely responsible for the Output you generate and its subsequent uses. No use of the Output can contravene any provision as stated in the License.
+7.  Attribution.  In connection with any Output, or use of Distribution of any Model or Derivatives of the Model, You agree to give appropriate credit and attribution to Licensor, provide a link to the original Model or Derivatives of the Model, provide a copy of this License, and identify any changes You have made to the Model or Derivatives of the Model (collectively, the “Attribution”).  The Attribution must not suggest endorsement by any Licensor.
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+Section IV: OTHER PROVISIONS
+9. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License, update the Model through electronic means, or cause modification to the Output resulting from updates to the Model based.
+10. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
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+12. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+13. Accepting Warranty or Additional Liability. While Distributing the Model or Derivatives of the Model, You may choose to charge a fee in exchange for support, warranty, indemnity, or other obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor or Licensor, and only if You agree to indemnify, defend, and hold each Contributor and the Licensor harmless for any liability incurred by, or claims asserted against, such Contributor or Licensor by reason of your accepting any such warranty or additional liability.
+14. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+END OF TERMS AND CONDITIONS
+
+Attachment A
+USE RESTRICTIONS
+As conditions to the Licenses set forth in this Agreement, You agree not to use, reproduce, modify, create or Distribute the Model, Derivatives of the Model, or Output (collectively, “Use”)  in any of the following ways:
+1. Legal:
+(a) In any way that violates any applicable national, federal, state, local or international law or regulation; or
+(b) to directly or indirectly infringe or misappropriate any third party intellectual property rights (including those of Licensor or any Contributor)
+2. Commercial:
+(a) for any purpose if You (your employer, or the entity you are affiliated with) generated more than two million US Dollars ($5,000,000) in gross revenue in the prior year, except where Your Use is limited to personal use or research purposes;
+(b) for any purpose if You (your employer, or the entity you are affiliated with) has raised more than two million US dollars ($5,000,000) in total equity or debt funding from any source, except where Your Use is limited to personal use or research purposes; or
+(c)  for any purpose if You (your employer, or the entity you are affiliated with) provides or otherwise makes available any product or service that competes with any product or service offered by or made available by Licensor or any of its affiliates.
+Commercial and broader use licenses may be available from Licensor at the following URL: https://www.datalab.to/
\ No newline at end of file
diff --git a/README.md b/README.md
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--- /dev/null
+++ b/README.md
@@ -0,0 +1,504 @@
+<p align="center">
+  <img src="static/datalab-logo.png" alt="Datalab Logo" width="150"/>
+</p>
+<h1 align="center">Datalab</h1>
+<p align="center">
+  <strong>State of the Art models for Document Intelligence</strong>
+</p>
+<p align="center">
+  <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/Code%20License-Apache--2.0-green.svg" alt="Code License"></a>
+  <a href="https://www.datalab.to/pricing"><img src="https://img.shields.io/badge/Model%20License-OpenRAIL--M-blue.svg" alt="Model License"></a>
+  <a href="https://discord.gg/KuZwXNGnfH"><img src="https://img.shields.io/badge/Discord-Join%20us-5865F2?logo=discord&logoColor=white" alt="Discord"></a>
+</p>
+<p align="center">
+  <a href="https://www.datalab.to"><img src="https://img.shields.io/badge/Homepage-datalab.to-blue" alt="Homepage"></a>
+  <a href="https://documentation.datalab.to"><img src="https://img.shields.io/badge/Docs-Read%20the%20docs-blue" alt="Docs"></a>
+  <a href="https://www.datalab.to/playground"><img src="https://img.shields.io/badge/Datalab Playground-Try%20it-orange" alt="Datalab Playground"></a>
+</p>
+
+<hr/>
+
+# Surya
+
+Surya is a 650M param OCR model with these features:
+
+- Accuracy - scores 83.3% on [olmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench) (top under 3B params)
+- Speed - throughput of 5 pages/s on an RTX 5090
+- Multilingual - scores 87.2% on an internal benchmark set of 91 languages (more [here](#multilingual))
+- Layout analysis (table, image, header, etc.) with reading order
+- Table recognition (rows + columns)
+
+We also ship smaller models for line-level text detection and ocr error detection.  It works on a range of documents (see [usage](#usage) and [benchmarks](#benchmarks)).
+
+## Try Datalab's Managed Platform
+
+Our managed platform runs both Surya, and variants of our highest accuracy model, [Chandra](https://github.com/datalab-to/chandra).
+
+Get started with **$5 in free credits** — [sign up](https://www.datalab.to/?utm_source=gh-surya) (takes under 30 seconds) or try our free [public playground](https://www.datalab.to/playground?utm_source=gh-surya).
+
+## Model Information
+
+<img src="static/images/olmocr_size_chart.png" width="700"/>
+
+
+|                            Detection                             |                                   OCR                                   |
+|:----------------------------------------------------------------:|:-----------------------------------------------------------------------:|
+|  <img src="static/images/excerpt.png" width="280"/>  |  <img src="static/images/excerpt_text.png" width="280"/> |
+
+|                               Layout                               |                       Table Recognition                       |
+|:------------------------------------------------------------------:|:-------------------------------------------------------------:|
+| <img src="static/images/excerpt_layout.png" width="280"/> | <img src="static/images/scanned_tablerec.png" width="280"/> |
+
+
+Surya is named for the [Hindu sun god](https://en.wikipedia.org/wiki/Surya), who has universal vision.
+
+## Examples
+
+Each row links to five annotated views of the same page: text-line detection, OCR, layout, reading order, and (when present) table recognition.
+
+| Name              |              Detection              |                                       OCR |                                       Layout |                                          Order |                                       Table Rec |
+|-------------------|:-----------------------------------:|------------------------------------------:|---------------------------------------------:|------------------------------------------------:|------------------------------------------------:|
+| Newspaper         | [Image](static/images/newspaper.png) | [Image](static/images/newspaper_text.png) | [Image](static/images/newspaper_layout.png) | [Image](static/images/newspaper_reading.png) |                                                  |
+| Textbook          | [Image](static/images/textbook.png)  | [Image](static/images/textbook_text.png)  | [Image](static/images/textbook_layout.png)  | [Image](static/images/textbook_reading.png)  |                                                  |
+| Tax Form          | [Image](static/images/form.png)      | [Image](static/images/form_text.png)      | [Image](static/images/form_layout.png)      | [Image](static/images/form_reading.png)      | [Image](static/images/form_tablerec.png)      |
+| Handwritten Notes | [Image](static/images/handwritten.png) | [Image](static/images/handwritten_text.png) | [Image](static/images/handwritten_layout.png) | [Image](static/images/handwritten_reading.png) | [Image](static/images/handwritten_tablerec.png) |
+| Corporate Doc     | [Image](static/images/corporate.png) | [Image](static/images/corporate_text.png) | [Image](static/images/corporate_layout.png) | [Image](static/images/corporate_reading.png) | [Image](static/images/corporate_tablerec.png) |
+
+# Commercial usage
+
+The Surya code is licensed under Apache 2.0. The model weights use a modified AI Pubs Open Rail-M license (free for research, personal use, and startups under $5M funding/revenue). For broader commercial licensing of the model weights, visit our pricing page [here](https://www.datalab.to/pricing?utm_source=gh-surya).
+
+# Installation
+
+Install with:
+
+```shell
+pip install surya-ocr
+```
+
+## Inference backend prerequisites
+
+Surya auto-spawns the server on first use, and you need `vllm` (NVIDIA GPU) or `llama.cpp` (CPU / Apple Silicon):
+
+- **NVIDIA GPU:** [Docker](https://docs.docker.com/get-docker/) plus the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
+- **CPU / Apple Silicon:** the `llama-server` binary from llama.cpp:
+  ```shell
+  brew install llama.cpp     # macOS
+  # or grab a release from https://github.com/ggml-org/llama.cpp/releases
+  ```
+
+## Upgrading from Surya v1
+
+If you have v1 code, you can migrate to this:
+
+```python
+# v2
+from surya.inference import SuryaInferenceManager
+from surya.recognition import RecognitionPredictor
+
+manager = SuryaInferenceManager()              # auto-spawns vllm or llama-server
+rec = RecognitionPredictor(manager)
+predictions = rec([image])
+```
+
+What's different:
+- `SuryaInferenceManager` replaces `FoundationPredictor`. Same manager instance is shared across `LayoutPredictor`, `RecognitionPredictor`, `TableRecPredictor`.
+- Output schemas changed: see the per-section JSON tables below. Highlights — `text_lines` → `blocks` (with `html`); layout dropped `top_k`, added `count`; table_rec dropped `is_header` / `colspan` / `rowspan` from cells.
+
+# Usage
+
+Surya 2 runs layout, OCR, and table recognition through a single VLM.  The inference manager will spawn one for you on first use; you can also point it at an existing server via `SURYA_INFERENCE_URL=http://host:port/v1`.
+
+- Inspect the settings in `surya/settings.py`.  You can override any setting via env var (e.g. `SURYA_INFERENCE_BACKEND=vllm`).
+- Text detection and OCR errors are separate models.
+
+### Server lifecycle (`--keep_server`)
+
+By default each command spawns the VLM server on startup and shuts it down on
+exit — so running several commands in a row pays the startup (and, on GPU, the
+model-load) cost every time. Pass `--keep_server` to leave the server running
+so later commands attach to it instead of re-spawning:
+
+```shell
+surya_ocr    DATA_PATH --keep_server   # spawns the server and leaves it up
+surya_layout DATA_PATH                 # attaches to the running server
+surya_table  DATA_PATH                 # ...and so on, no re-spawn
+```
+
+`--keep_server` works on every command. Stop the server when you're done
+(`docker stop` the `surya-vllm-*` container, or kill the `llama-server`
+process), or set `SURYA_INFERENCE_KEEP_ALIVE=1` to make keep-alive the default.
+
+## Interactive App
+
+I've included a streamlit app that lets you interactively try Surya on images or PDF files.  Run it with:
+
+```shell
+pip install streamlit pdftext
+surya_gui
+```
+
+## OCR (text recognition)
+
+This command will write out a json file with the detected text and bboxes:
+
+```shell
+surya_ocr DATA_PATH
+```
+
+- `DATA_PATH` can be an image, pdf, or folder of images/pdfs
+- `--images` will save images of the pages and detected blocks (optional)
+- `--output_dir` specifies the directory to save results to instead of the default
+- `--page_range` specifies the page range to process in the PDF, specified as a single number, a comma separated list, a range, or comma separated ranges - example: `0,5-10,20`.
+- `--keep_server` leaves the inference server running after the command exits so later commands reuse it (see [Server lifecycle](#server-lifecycle---keep_server)).  Available on every command.
+
+The `results.json` file contains a dict keyed by input filename (no extension). Each value is a list of page dicts. Each page dict contains:
+
+- `blocks` - per-block OCR results in reading order
+  - `label` - canonicalized layout label (e.g. `Text`, `SectionHeader`, `Table`, `Equation`, `Picture`, `Form`, `PageHeader`, ...). See `surya/layout/label.py:LAYOUT_PRED_RELABEL` for the full canonical-name set.
+  - `raw_label` - original label emitted by the model, before canonicalization
+  - `reading_order` - 0-indexed position in layout output
+  - `html` - block content as HTML (math wrapped in `<math>...</math>`, tables as `<table>...</table>`, etc.). `""` if the block was skipped
+  - `polygon` - 4-corner polygon in `[[x0,y0],[x1,y0],[x1,y1],[x0,y1]]` order
+  - `bbox` - axis-aligned `[x0, y0, x1, y1]` derived from the polygon
+  - `confidence` - mean per-token probability across the block's decode (0-1)
+  - `skipped` - true if the block was a visual label (e.g. Picture) and not OCR'd
+  - `error` - true if the block OCR call failed
+- `image_bbox` - `[0, 0, width, height]` for the page image
+
+**Performance tips**
+
+- Throughput is governed by the inference backend.  With `vllm`, raise `--max-num-seqs` / `--max-num-batched-tokens` (or `SURYA_INFERENCE_PARALLEL` on the client side) to keep more pages in flight. With `llama.cpp`, set `SURYA_INFERENCE_PARALLEL` to match `--parallel` on `llama-server`.
+- DPI can also impact throughput significantly - you can adjust the DPI settings to make the right throughput/accuracy tradeoff for your usecase.  Try going from 192 to 96 for improved throughput.
+- MTP can also impact latency/throughput - you can adjust the vllm mtp config in settings.
+
+### From python
+
+```python
+from PIL import Image
+from surya.inference import SuryaInferenceManager
+from surya.recognition import RecognitionPredictor
+
+manager = SuryaInferenceManager()
+recognition_predictor = RecognitionPredictor(manager)
+
+# Default: full-page OCR. One VLM call per page. Returns one PageOCRResult per
+# image: `.blocks` (each with label, html, polygon, bbox, confidence, ...) and
+# `.image_bbox` — the same schema as block mode.
+predictions = recognition_predictor([Image.open(IMAGE_PATH)])
+
+# Block mode: pre-run layout, then per-block OCR. Same return schema as above.
+# Auto-selected when `layout_results` is passed.
+from surya.layout import LayoutPredictor
+layout = LayoutPredictor(manager)
+layouts = layout([Image.open(IMAGE_PATH)])
+predictions = recognition_predictor([Image.open(IMAGE_PATH)], layouts)
+```
+
+
+## Text line detection
+
+This command will write out a json file with the detected bboxes.
+
+```shell
+surya_detect DATA_PATH
+```
+
+- `DATA_PATH` can be an image, pdf, or folder of images/pdfs
+- `--images` will save images of the pages and detected text lines (optional)
+- `--output_dir` specifies the directory to save results to instead of the default
+- `--page_range` specifies the page range to process in the PDF, specified as a single number, a comma separated list, a range, or comma separated ranges - example: `0,5-10,20`.
+
+The `results.json` file will contain a json dictionary where the keys are the input filenames without extensions.  Each value will be a list of dictionaries, one per page of the input document.  Each page dictionary contains:
+
+- `bboxes` - detected bounding boxes for text
+  - `bbox` - the axis-aligned rectangle for the text line in (x1, y1, x2, y2) format.  (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner.
+  - `polygon` - the polygon for the text line in (x1, y1), (x2, y2), (x3, y3), (x4, y4) format.  The points are in clockwise order from the top left.
+  - `confidence` - the confidence of the model in the detected text (0-1)
+- `vertical_lines` - vertical lines detected in the document
+  - `bbox` - the axis-aligned line coordinates.
+- `page` - the page number in the file
+- `image_bbox` - the bbox for the image in (x1, y1, x2, y2) format.  (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner.  All line bboxes will be contained within this bbox.
+
+**Performance tips**
+
+Detection is a torch model. `DETECTOR_BATCH_SIZE` defaults to an auto-picked value at runtime; override the env var to control VRAM usage on GPU and raise it on larger cards.
+
+### From python
+
+```python
+from PIL import Image
+from surya.detection import DetectionPredictor
+
+det_predictor = DetectionPredictor()
+predictions = det_predictor([Image.open(IMAGE_PATH)])
+```
+
+## Layout and reading order
+
+This command will write out a json file with the detected layout and reading order.
+
+```shell
+surya_layout DATA_PATH
+```
+
+- `DATA_PATH` can be an image, pdf, or folder of images/pdfs
+- `--images` will save images of the pages and detected text lines (optional)
+- `--output_dir` specifies the directory to save results to instead of the default
+- `--page_range` specifies the page range to process in the PDF, specified as a single number, a comma separated list, a range, or comma separated ranges - example: `0,5-10,20`.
+
+The `results.json` file contains a dict keyed by input filename (no extension). Each value is a list of page dicts. Each page dict contains:
+
+- `bboxes` - layout boxes in reading order
+  - `polygon` - 4-corner polygon `[[x0,y0],[x1,y0],[x1,y1],[x0,y1]]`
+  - `bbox` - axis-aligned `[x0, y0, x1, y1]` derived from the polygon
+  - `label` - canonicalized label. One of `Caption`, `Footnote`, `Equation`, `ListGroup`, `PageHeader`, `PageFooter`, `Picture`, `SectionHeader`, `Table`, `Text`, `Figure`, `Code`, `Form`, `TableOfContents`, `ChemicalBlock`, `Diagram`, `Bibliography`, `BlankPage`
+  - `raw_label` - original label emitted by the model
+  - `position` - 0-indexed reading order
+  - `count` - model's token estimate for OCR'ing this block (rounded to multiples of 50; used to size the per-block decode budget)
+  - `confidence` - mean per-token probability across the layout decode (0-1)
+- `image_bbox` - `[0, 0, width, height]`
+- `raw` - raw JSON the layout model emitted, for debugging
+- `error` - true if the layout call failed
+
+**Performance tips**
+
+Layout runs through the shared inference backend. Throughput tuning is the same as OCR — see Performance tips above.
+
+### From python
+
+```python
+from PIL import Image
+from surya.inference import SuryaInferenceManager
+from surya.layout import LayoutPredictor
+
+layout_predictor = LayoutPredictor(SuryaInferenceManager())
+layout_predictions = layout_predictor([Image.open(IMAGE_PATH)])
+```
+
+## Table Recognition
+
+This command will write out a json file with the detected table cells and row/column ids, along with row/column bounding boxes.  If you want to get cell positions and text, along with nice formatting, check out the [marker](https://github.com/datalab-to/marker) repo.  You can use the `TableConverter` to detect and extract tables in images and PDFs.  It supports output in json (with bboxes), markdown, and html.
+
+```shell
+surya_table DATA_PATH
+```
+
+- `DATA_PATH` can be an image, pdf, or folder of images/pdfs
+- `--images` will save annotated row + column overlays alongside the json (optional)
+- `--output_dir` specifies the directory to save results to instead of the default
+- `--page_range` specifies the page range to process in the PDF, specified as a single number, a comma separated list, a range, or comma separated ranges - example: `0,5-10,20`.
+- `--skip_table_detection` tells table recognition not to detect tables first.  Use this if your image is already cropped to a table.
+
+The `results.json` file contains a dict keyed by input filename (no extension). Each value is a list of per-table dicts. Each table dict contains:
+
+- `rows` - detected table rows in reading order
+  - `polygon` / `bbox` - row geometry (same convention as everywhere else)
+  - `row_id` - 0-indexed row id
+- `cols` - detected table columns
+  - `polygon` / `bbox` - column geometry
+  - `col_id` - 0-indexed column id
+- `cells` - geometric row × column intersections (simple mode)
+  - `polygon` / `bbox` - cell geometry
+  - `row_id`, `col_id`, `cell_id`
+- `html` - full `<table>...</table>` HTML (only populated when `predict_full` is used; handles spanning cells / header rows). `null` in simple mode.
+- `mode` - `"simple"` or `"full"`
+- `image_bbox` - the table crop bbox
+- `error` - true if the table_rec call failed
+- `raw` - raw model output, for debugging
+
+**Performance tips**
+
+Table recognition routes through the shared VLM. Throughput tuning is the same as OCR.
+
+### From python
+
+```python
+from PIL import Image
+from surya.inference import SuryaInferenceManager
+from surya.table_rec import TableRecPredictor
+
+table_rec_predictor = TableRecPredictor(SuryaInferenceManager())
+
+# Default: rows + columns only, cells derived from intersections.
+table_predictions = table_rec_predictor([Image.open(IMAGE_PATH)])
+
+# Or full HTML output (better for spanning cells / headers):
+# table_predictions = table_rec_predictor.predict_full([image])
+```
+
+## Math / equations
+
+Surya 2 handles math inline as part of full-page OCR — recognized equations
+come back inside `<math>...</math>` tags in the same HTML output as
+surrounding prose, in KaTeX-compatible LaTeX. No separate LaTeX OCR pass.
+
+# Inference Backends
+
+Layout / OCR / table_rec all share one VLM, served either by `vllm` (GPU) or `llama.cpp` (CPU / Apple Silicon). The `SuryaInferenceManager` will spawn one automatically; you can also point at a pre-running server:
+
+```bash
+# Attach to an existing vllm
+export SURYA_INFERENCE_BACKEND=vllm
+export SURYA_INFERENCE_URL=http://localhost:8000/v1
+```
+
+| Setting                           | Default                           | Notes                                                  |
+|-----------------------------------|-----------------------------------|--------------------------------------------------------|
+| `SURYA_INFERENCE_BACKEND`         | auto (vllm if NVIDIA, else llamacpp) | `vllm` \| `llamacpp` \| unset (auto)                |
+| `SURYA_INFERENCE_URL`             | (auto-spawn)                      | Attach to a running OpenAI-compatible server          |
+| `SURYA_INFERENCE_PARALLEL`        | 8                                 | Client-side concurrency to the backend                |
+| `SURYA_INFERENCE_KEEP_ALIVE`      | false                             | Leave the spawned server up after exit (cf. `--keep_server`) |
+| `SURYA_GUIDED_LAYOUT`             | true                              | JSON-schema-constrained layout decode                 |
+
+# Limitations
+
+- This is specialized for document OCR. Performance on photos or natural scenes is not the goal.
+- Layout / OCR / table_rec all need a running inference backend (vllm or llama.cpp). Detection runs purely on torch and works without it.
+
+## Troubleshooting
+
+If OCR isn't working properly:
+
+- Try increasing resolution of the image so the text is bigger.  If the resolution is already very high, try decreasing it to no more than a `2048px` width.
+- Preprocessing the image (binarizing, deskewing, etc) can help with very old/blurry images.
+- You can adjust `DETECTOR_BLANK_THRESHOLD` and `DETECTOR_TEXT_THRESHOLD` if you don't get good results.  `DETECTOR_BLANK_THRESHOLD` controls the space between lines - any prediction below this number will be considered blank space.  `DETECTOR_TEXT_THRESHOLD` controls how text is joined - any number above this is considered text.  `DETECTOR_TEXT_THRESHOLD` should always be higher than `DETECTOR_BLANK_THRESHOLD`, and both should be in the 0-1 range.  Looking at the heatmap from the debug output of the detector can tell you how to adjust these (if you see faint things that look like boxes, lower the thresholds, and if you see bboxes being joined together, raise the thresholds).
+
+# Manual install
+
+If you want to develop surya, you can install it manually with [uv](https://docs.astral.sh/uv/):
+
+```bash
+git clone https://github.com/datalab-to/surya.git
+cd surya
+uv sync --group dev      # installs runtime + dev deps
+uv run surya_ocr ...     # or `source .venv/bin/activate` to enter the venv
+```
+
+# Benchmarks
+
+Surya 2 is a single VLM that handles layout analysis, OCR (full-page or
+per-block), and table recognition in one model. We evaluate end-to-end on
+[olmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench) — the
+standard quality benchmark for document parsers.
+
+## olmOCR-bench
+
+Pareto-optimal on the size-vs-score frontier, and best in class under 3B params.
+
+| Model                       | Params    | Score    |
+|-----------------------------|----------:|---------:|
+| Infinity-Parser2-Pro        |     35.1B |     87.6 |
+| Chandra OCR 2 (Datalab)     |      4.0B |     85.9 |
+| dots.mocr                   |      3.0B |     83.9 |
+| **Surya OCR 2** (Datalab)   | **0.65B** | **83.3** |
+| LightOnOCR 2-1B \*          |      1.0B |     83.2 |
+| Chandra OCR 1 (Datalab)     |      9.0B |     83.1 |
+| olmOCR (anchored)           |      8.3B |     77.4 |
+| GOT OCR                     |      0.6B |     48.3 |
+
+\* **LightOnOCR 2-1B** uses a different benchmark methodology than the other entries (see their [release notes](https://huggingface.co/lightonai/LightOnOCR-2-1B)); the score is included for context but is not directly comparable.
+
+Comparison scores from the [olmOCR-bench dataset card](https://huggingface.co/datasets/allenai/olmOCR-bench).
+
+Surya 2, per-source pass rate on the `default` preset (8,413 tests total):
+
+| ArXiv | Base | Hdr/Ftr | TinyTxt | MultCol | OldScan | OldMath | Tables |
+|------:|-----:|--------:|--------:|--------:|--------:|--------:|-------:|
+|  88.3 | 99.7 |    92.5 |    93.7 |    82.4 |    41.8 |    81.4 |   86.6 |
+
+## Multilingual
+
+We also evaluate Surya 2 against a 91-language internal benchmark covering
+text accuracy, layout, tables, math, and reading order in documents drawn
+from each language.
+
+**Overall pass rate: 87.2% across 91 languages.** 38 of the
+91 languages score ≥ 90%; 76 score ≥ 80%.
+
+Top 15 widely-spoken languages:
+
+| Code | Language    | Score |
+|------|-------------|------:|
+| `ar` | Arabic      | 72.7% |
+| `bn` | Bengali     | 82.7% |
+| `zh` | Chinese     | 82.5% |
+| `en` | English     | 92.3% |
+| `fr` | French      | 89.3% |
+| `de` | German      | 89.7% |
+| `hi` | Hindi       | 82.2% |
+| `it` | Italian     | 93.0% |
+| `ja` | Japanese    | 86.2% |
+| `ko` | Korean      | 86.7% |
+| `fa` | Persian     | 82.3% |
+| `pt` | Portuguese  | 86.1% |
+| `ru` | Russian     | 88.8% |
+| `es` | Spanish     | 90.7% |
+| `vi` | Vietnamese  | 73.2% |
+
+See [static/docs/multilingual.md](static/docs/multilingual.md) for the full 91-language table.
+
+## Throughput
+
+Full-page OCR, 96 DPI input (~2,400 output tokens/page average), measured
+client-side against a running inference server.
+
+### RTX 5090 (vllm)
+
+`vllm/vllm-openai:v0.20.1`, single RTX 5090 (32 GB).
+
+| Concurrency | Pages/s |  Tokens/s | p50 (ms) | p95 (ms) | avg tok/page |
+|------------:|--------:|----------:|---:|---:|---:|
+|         128 |    5.35 |    12,884 | 18,915 | 42,538 | 2,410 |
+
+### Apple Silicon (llama.cpp / Metal)
+
+`llama-server` with Metal backend.
+
+| `--parallel` |  Pages/s | Tokens/s | p50 (ms) | p95 (ms) | avg tok/page | Power |
+|-------------:|---------:|---------:|---:|---:|---:|---:|
+|            8 |    0.108 |      254 | 59,313 | 129,173 | 2,360 | ~30 W |
+
+## Reproducing
+
+We score Surya 2 on olmOCR-bench by serving the model with `vllm` (or
+`llama.cpp`) and running the olmOCR-bench harness from
+[allenai/olmocr](https://github.com/allenai/olmocr), with some adjustments applied to account for our output HTML format.
+
+# Training
+
+Layout, OCR, and table recognition all share a single vision-language model
+(Qwen3.5-style architecture, ~650M params). It's trained on diverse document
+images to emit either a layout JSON or a full-page HTML output, depending on
+prompt. Text-line detection is a separate small torch model — a modified
+EfficientViT segformer trained from scratch on document line annotations.
+
+If you want help finetuning Surya on your own data, or to use our managed
+training stack, reach us at hi@datalab.to.
+
+# Thanks
+
+This work would not have been possible without amazing open source AI work:
+
+- [Qwen3-VL](https://huggingface.co/Qwen) from Alibaba
+- [vllm](https://github.com/vllm-project/vllm) and [llama.cpp](https://github.com/ggerganov/llama.cpp) for inference
+- [Segformer](https://arxiv.org/pdf/2105.15203.pdf) from NVIDIA
+- [EfficientViT](https://github.com/mit-han-lab/efficientvit) from MIT
+- [timm](https://github.com/huggingface/pytorch-image-models) from Ross Wightman
+- [transformers](https://github.com/huggingface/transformers) from huggingface
+- [CRAFT](https://github.com/clovaai/CRAFT-pytorch), a great scene text detection model
+
+Thank you to everyone who makes open source AI possible.
+
+# Citation
+
+If you use surya (or the associated models) in your work or research, please consider citing us using the following BibTeX entry:
+
+```bibtex
+@misc{paruchuri2025surya,
+  author       = {Vikas Paruchuri and Datalab Team},
+  title        = {Surya: A lightweight document OCR and analysis toolkit},
+  year         = {2025},
+  howpublished = {\url{https://github.com/datalab-to/surya}},
+  note         = {GitHub repository},
+}
diff --git a/README.wehub.md b/README.wehub.md
new file mode 100644
index 0000000..ec97d52
--- /dev/null
+++ b/README.wehub.md
@@ -0,0 +1,7 @@
+# WeHub 来源说明
+
+- 原始项目：`datalab-to/surya`
+- 原始仓库：https://github.com/datalab-to/surya
+- 导入方式：上游默认分支的最新快照
+- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准
+- 本文件仅用于记录来源，不代表 WeHub 是原项目作者
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..b81790d
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,68 @@
+[project]
+name = "surya-ocr"
+version = "0.21.1"
+description = "OCR, layout, reading order, and table recognition in 90+ languages."
+readme = "README.md"
+license = { text = "Apache-2.0" }
+authors = [
+    { name = "Vik Paruchuri", email = "vik@datalab.to" },
+]
+requires-python = ">=3.10,<4"
+keywords = ["ocr", "pdf", "text detection", "text recognition", "tables"]
+dependencies = [
+    "transformers>=5.12.1",
+    "torch>=2.7.0,<3",
+    "torchvision>=0.20.0,<1",
+    "pydantic>=2.5.3,<3",
+    "pydantic-settings>=2.1.0,<3",
+    "python-dotenv>=1.0.0,<2",
+    "pillow>=10.2.0,<11",
+    "pypdfium2==5.9.0",
+    "filetype>=1.2.0,<2",
+    "click>=8.1.8,<9",
+    "platformdirs>=4.3.6,<5",
+    "opencv-python-headless==4.11.0.86",
+    "openai>=1.55.0,<3",
+    "httpx>=0.27.0,<1",
+    "huggingface-hub>=1.5.0,<2",
+    "filelock>=3.16.0,<4",
+    "beautifulsoup4>=4.12.0,<5",
+]
+
+[project.urls]
+Repository = "https://github.com/datalab-to/surya"
+
+[project.scripts]
+surya_detect = "surya.scripts.detect_text:detect_text_cli"
+surya_ocr = "surya.scripts.ocr_text:ocr_text_cli"
+surya_layout = "surya.scripts.detect_layout:detect_layout_cli"
+surya_gui = "surya.scripts.run_streamlit_app:streamlit_app_cli"
+surya_table = "surya.scripts.table_recognition:table_recognition_cli"
+surya_screenshot = "surya.scripts.screenshot_app:main"
+
+[dependency-groups]
+dev = [
+    "pre-commit>=4.2.0",
+    "jupyter>=1.0.0",
+    "pymupdf>=1.23.8",
+    "datasets>=2.16.1",
+    "streamlit>=1.31.0",
+    "pytest>=8.3.4",
+    "pdftext>=0.5.1",
+    "tabulate>=0.9.0",
+    "flask>=3.0.0",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["surya"]
+
+[tool.ruff]
+# rf-detr is vendored byte-for-byte from upstream (Roboflow); don't lint/format it.
+# force-exclude makes the exclude apply even when pre-commit passes explicit paths.
+extend-exclude = ["surya/common/rfdetr"]
+force-exclude = true
+
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000..05ef295
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,7 @@
+[pytest]
+testpaths=tests
+pythonpath=.
+filterwarnings =
+    ignore::UserWarning
+    ignore::PendingDeprecationWarning
+    ignore::DeprecationWarning
\ No newline at end of file
diff --git a/signatures/version1/cla.json b/signatures/version1/cla.json
new file mode 100644
index 0000000..4e202b8
--- /dev/null
+++ b/signatures/version1/cla.json
@@ -0,0 +1,244 @@
+{
+  "signedContributors": [
+    {
+      "name": "rishiraj",
+      "id": 44090649,
+      "comment_id": 2170578748,
+      "created_at": "2024-06-15T19:31:20Z",
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+    },
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+      "created_at": "2026-02-24T22:09:52Z",
+      "repoId": 741297064,
+      "pullRequestNo": 487
+    },
+    {
+      "name": "Br1an67",
+      "id": 29810238,
+      "comment_id": 3979412700,
+      "created_at": "2026-03-01T07:32:18Z",
+      "repoId": 741297064,
+      "pullRequestNo": 489
+    },
+    {
+      "name": "Jkker",
+      "id": 23471930,
+      "comment_id": 4184890988,
+      "created_at": "2026-04-03T19:31:54Z",
+      "repoId": 741297064,
+      "pullRequestNo": 493
+    },
+    {
+      "name": "YizukiAme",
+      "id": 104178195,
+      "comment_id": 4224654618,
+      "created_at": "2026-04-10T14:55:26Z",
+      "repoId": 741297064,
+      "pullRequestNo": 498
+    },
+    {
+      "name": "milahu",
+      "id": 12958815,
+      "comment_id": 4386642879,
+      "created_at": "2026-05-06T09:17:07Z",
+      "repoId": 741297064,
+      "pullRequestNo": 509
+    },
+    {
+      "name": "iRonin",
+      "id": 27929,
+      "comment_id": 4604690173,
+      "created_at": "2026-06-02T16:49:40Z",
+      "repoId": 741297064,
+      "pullRequestNo": 512
+    },
+    {
+      "name": "gkriegspeedbay",
+      "id": 269034776,
+      "comment_id": 4697896223,
+      "created_at": "2026-06-13T07:41:35Z",
+      "repoId": 741297064,
+      "pullRequestNo": 519
+    }
+  ]
+}
\ No newline at end of file
diff --git a/static/datalab-logo.png b/static/datalab-logo.png
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diff --git a/static/docs/multilingual.md b/static/docs/multilingual.md
new file mode 100644
index 0000000..ca8115e
--- /dev/null
+++ b/static/docs/multilingual.md
@@ -0,0 +1,101 @@
+# Surya 2 — full 91-language results
+
+Overall pass rate: **87.2%** across 91 languages (32,055 tests). Each language
+has between ~30 and ~750 tests.
+
+Sorted alphabetically by language name. See [README](../../README.md#multilingual)
+for the curated subset.
+
+| Code | Language          | Score |
+|------|-------------------|------:|
+| `af` | Afrikaans         | 87.7% |
+| `sq` | Albanian          | 88.9% |
+| `am` | Amharic           | 74.6% |
+| `ar` | Arabic            | 72.7% |
+| `hy` | Armenian          | 90.1% |
+| `as` | Assamese          | 86.4% |
+| `az` | Azerbaijani       | 91.5% |
+| `eu` | Basque            | 85.5% |
+| `be` | Belarusian        | 98.5% |
+| `bn` | Bengali           | 82.7% |
+| `bs` | Bosnian           | 92.5% |
+| `br` | Breton            | 93.6% |
+| `bg` | Bulgarian         | 95.4% |
+| `my` | Burmese           | 88.2% |
+| `ca` | Catalan           | 86.4% |
+| `zh` | Chinese           | 82.5% |
+| `hr` | Croatian          | 92.5% |
+| `cs` | Czech             | 85.8% |
+| `da` | Danish            | 84.5% |
+| `nl` | Dutch             | 86.5% |
+| `en` | English           | 92.3% |
+| `eo` | Esperanto         | 83.1% |
+| `et` | Estonian          | 76.8% |
+| `fi` | Finnish           | 87.5% |
+| `fr` | French            | 89.3% |
+| `gl` | Galician          | 85.5% |
+| `ka` | Georgian          | 91.4% |
+| `de` | German            | 89.7% |
+| `el` | Greek             | 80.7% |
+| `gu` | Gujarati          | 83.4% |
+| `ha` | Hausa             | 89.6% |
+| `he` | Hebrew            | 90.9% |
+| `hi` | Hindi             | 82.2% |
+| `hu` | Hungarian         | 90.6% |
+| `is` | Icelandic         | 89.5% |
+| `id` | Indonesian        | 90.3% |
+| `ga` | Irish             | 92.8% |
+| `it` | Italian           | 93.0% |
+| `ja` | Japanese          | 86.2% |
+| `jv` | Javanese          | 91.1% |
+| `kn` | Kannada           | 79.2% |
+| `kk` | Kazakh            | 90.3% |
+| `km` | Khmer             | 75.0% |
+| `ko` | Korean            | 86.7% |
+| `ku` | Kurdish           | 93.9% |
+| `ky` | Kyrgyz            | 92.3% |
+| `lo` | Lao               | 72.6% |
+| `la` | Latin             | 86.1% |
+| `lv` | Latvian           | 90.3% |
+| `lt` | Lithuanian        | 85.4% |
+| `mk` | Macedonian        | 95.3% |
+| `mg` | Malagasy          | 95.0% |
+| `ms` | Malay             | 91.2% |
+| `ml` | Malayalam         | 84.7% |
+| `mr` | Marathi           | 85.9% |
+| `mn` | Mongolian         | 94.3% |
+| `ne` | Nepali            | 84.9% |
+| `no` | Norwegian         | 93.6% |
+| `or` | Oriya             | 60.0% |
+| `ps` | Pashto            | 72.0% |
+| `fa` | Persian           | 82.3% |
+| `pl` | Polish            | 91.4% |
+| `pt` | Portuguese        | 86.1% |
+| `pa` | Punjabi           | 76.5% |
+| `ro` | Romanian          | 86.9% |
+| `ru` | Russian           | 88.8% |
+| `sa` | Sanskrit          | 78.8% |
+| `gd` | Scottish Gaelic   | 92.5% |
+| `sr` | Serbian           | 94.1% |
+| `sd` | Sindhi            | 87.3% |
+| `si` | Sinhala           | 85.4% |
+| `sk` | Slovak            | 90.4% |
+| `sl` | Slovenian         | 91.4% |
+| `so` | Somali            | 97.9% |
+| `es` | Spanish           | 90.7% |
+| `su` | Sundanese         | 92.6% |
+| `sw` | Swahili           | 93.5% |
+| `sv` | Swedish           | 91.4% |
+| `ta` | Tamil             | 89.9% |
+| `te` | Telugu            | 79.2% |
+| `th` | Thai              | 76.4% |
+| `tr` | Turkish           | 85.4% |
+| `uk` | Ukrainian         | 92.1% |
+| `ur` | Urdu              | 68.7% |
+| `ug` | Uyghur            | 70.2% |
+| `uz` | Uzbek             | 88.9% |
+| `vi` | Vietnamese        | 73.2% |
+| `cy` | Welsh             | 95.1% |
+| `fy` | Western Frisian   | 90.9% |
+| `xh` | Xhosa             | 90.3% |
+| `yi` | Yiddish           | 82.5% |
diff --git a/static/fonts/.gitignore b/static/fonts/.gitignore
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+++ b/static/fonts/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
\ No newline at end of file
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diff --git a/surya/__init__.py b/surya/__init__.py
new file mode 100644
index 0000000..9898008
--- /dev/null
+++ b/surya/__init__.py
@@ -0,0 +1,10 @@
+import os
+
+# The rf-detr fast detector's DINOv2 pos-embed interpolation uses an antialiased-bicubic
+# op that has no MPS kernel. PyTorch reads PYTORCH_ENABLE_MPS_FALLBACK at `import torch`,
+# so set it here — before torch is imported — to let that one op fall back to CPU while
+# the rest of the model runs on MPS. This is only effective when surya is imported before
+# torch; when torch is already loaded, `_pick_device` (surya.common.rfdetr_torch) probes
+# the op at runtime and falls back to CPU device selection instead. setdefault so we never
+# clobber a value the user set deliberately.
+os.environ.setdefault("PYTORCH_ENABLE_MPS_FALLBACK", "1")
diff --git a/surya/common/__init__.py b/surya/common/__init__.py
new file mode 100644
index 0000000..b28b04f
--- /dev/null
+++ b/surya/common/__init__.py
@@ -0,0 +1,3 @@
+
+
+
diff --git a/surya/common/blank.py b/surya/common/blank.py
new file mode 100644
index 0000000..d11c721
--- /dev/null
+++ b/surya/common/blank.py
@@ -0,0 +1,64 @@
+"""Pixel-content heuristics for detecting blank or near-uniform image regions.
+
+Used by both the layout predictor (drop hallucinated layout blocks over empty
+space) and the recognition predictor (drop hallucinated text blocks from
+full-page OCR, decide whether an empty full-page output is a correct blank-page
+read or a failure).
+
+Two signals, combined:
+  * near-white fraction — most pixels have every RGB channel above a threshold
+  * pixel-value standard deviation — the region is essentially one color
+    (catches uniform-color fills that the white check misses)
+"""
+
+from __future__ import annotations
+
+import numpy as np
+from PIL import Image
+
+
+# Per-channel value at/above which a pixel is considered "near-white".
+# Tolerates the small noise typical of PDF renders at 96 DPI.
+BLANK_WHITE_THRESHOLD = 245
+# Fraction of pixels that must be near-white for a region to count as blank.
+BLANK_PIXEL_FRACTION = 0.99
+# Pixel-value std below which a region is "essentially one color" regardless
+# of what that color is (catches solid-fill rectangles, dark banners, etc.).
+UNIFORM_COLOR_STD = 8.0
+
+
+def near_white_fraction(
+    image: Image.Image, white_threshold: int = BLANK_WHITE_THRESHOLD
+) -> float:
+    """Fraction of pixels where every RGB channel ≥ ``white_threshold``."""
+    arr = np.asarray(image.convert("RGB"))
+    if arr.size == 0:
+        return 0.0
+    return float(np.all(arr >= white_threshold, axis=-1).mean())
+
+
+def is_blank_region(
+    image: Image.Image,
+    *,
+    white_threshold: int = BLANK_WHITE_THRESHOLD,
+    blank_pixel_fraction: float = BLANK_PIXEL_FRACTION,
+    uniform_color_std: float = UNIFORM_COLOR_STD,
+) -> bool:
+    """True iff the image is essentially blank — either mostly near-white or
+    near-uniform color. Use this on a per-block crop or a whole page.
+
+    Returns False for empty (0-pixel) crops so callers don't accidentally
+    treat a degenerate bbox as blank.
+    """
+    arr = np.asarray(image.convert("RGB"))
+    if arr.size == 0:
+        return False
+    if np.all(arr >= white_threshold, axis=-1).mean() > blank_pixel_fraction:
+        return True
+    # Per-channel std — a uniform solid color (e.g., red banner with RGB=(200,50,50))
+    # has each channel constant across pixels, but mixing channels inflates the
+    # aggregate std. Check each channel independently.
+    per_channel_std = arr.reshape(-1, arr.shape[-1]).std(axis=0)
+    if float(per_channel_std.max()) < uniform_color_std:
+        return True
+    return False
diff --git a/surya/common/compat.py b/surya/common/compat.py
new file mode 100644
index 0000000..05d027a
--- /dev/null
+++ b/surya/common/compat.py
@@ -0,0 +1,163 @@
+from typing import Optional, Iterable, List, Union, Tuple
+
+import torch
+
+
+def verify_out_features_out_indices(
+    out_features: Optional[Iterable[str]],
+    out_indices: Optional[Iterable[int]],
+    stage_names: Optional[Iterable[str]],
+):
+    """
+    Verify that out_indices and out_features are valid for the given stage_names.
+    """
+    if stage_names is None:
+        raise ValueError("Stage_names must be set for transformers backbones")
+
+    if out_features is not None:
+        if not isinstance(out_features, (list,)):
+            raise ValueError(f"out_features must be a list got {type(out_features)}")
+        if any(feat not in stage_names for feat in out_features):
+            raise ValueError(
+                f"out_features must be a subset of stage_names: {stage_names} got {out_features}"
+            )
+        if len(out_features) != len(set(out_features)):
+            raise ValueError(
+                f"out_features must not contain any duplicates, got {out_features}"
+            )
+        if out_features != (
+            sorted_feats := [feat for feat in stage_names if feat in out_features]
+        ):
+            raise ValueError(
+                f"out_features must be in the same order as stage_names, expected {sorted_feats} got {out_features}"
+            )
+
+    if out_indices is not None:
+        if not isinstance(out_indices, (list, tuple)):
+            raise ValueError(
+                f"out_indices must be a list or tuple, got {type(out_indices)}"
+            )
+        # Convert negative indices to their positive equivalent: [-1,] -> [len(stage_names) - 1,]
+        positive_indices = tuple(
+            idx % len(stage_names) if idx < 0 else idx for idx in out_indices
+        )
+        if any(idx for idx in positive_indices if idx not in range(len(stage_names))):
+            raise ValueError(
+                f"out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"
+            )
+        if len(positive_indices) != len(set(positive_indices)):
+            msg = f"out_indices must not contain any duplicates, got {out_indices}"
+            msg += (
+                f"(equivalent to {positive_indices}))"
+                if positive_indices != out_indices
+                else ""
+            )
+            raise ValueError(msg)
+        if positive_indices != tuple(sorted(positive_indices)):
+            sorted_negative = tuple(
+                idx
+                for _, idx in sorted(
+                    zip(positive_indices, out_indices), key=lambda x: x[0]
+                )
+            )
+            raise ValueError(
+                f"out_indices must be in the same order as stage_names, expected {sorted_negative} got {out_indices}"
+            )
+
+    if out_features is not None and out_indices is not None:
+        if len(out_features) != len(out_indices):
+            raise ValueError(
+                "out_features and out_indices should have the same length if both are set"
+            )
+        if out_features != [stage_names[idx] for idx in out_indices]:
+            raise ValueError(
+                "out_features and out_indices should correspond to the same stages if both are set"
+            )
+
+
+def _align_output_features_output_indices(
+    out_features: Optional[List[str]],
+    out_indices: Optional[Union[List[int], Tuple[int]]],
+    stage_names: List[str],
+):
+    """
+    Finds the corresponding `out_features` and `out_indices` for the given `stage_names`.
+
+    The logic is as follows:
+        - `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the
+        `out_indices`.
+        - `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the
+        `out_features`.
+        - `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.
+        - `out_indices` and `out_features` set: input `out_indices` and `out_features` are returned.
+
+    Args:
+        out_features (`List[str]`): The names of the features for the backbone to output.
+        out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.
+        stage_names (`List[str]`): The names of the stages of the backbone.
+    """
+    if out_indices is None and out_features is None:
+        out_indices = [len(stage_names) - 1]
+        out_features = [stage_names[-1]]
+    elif out_indices is None and out_features is not None:
+        out_indices = [stage_names.index(layer) for layer in out_features]
+    elif out_features is None and out_indices is not None:
+        out_features = [stage_names[idx] for idx in out_indices]
+    return out_features, out_indices
+
+
+def get_aligned_output_features_output_indices(
+    out_features: Optional[List[str]],
+    out_indices: Optional[Union[List[int], Tuple[int]]],
+    stage_names: List[str],
+) -> Tuple[List[str], List[int]]:
+    """
+    Get the `out_features` and `out_indices` so that they are aligned.
+
+    The logic is as follows:
+        - `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the
+        `out_indices`.
+        - `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the
+        `out_features`.
+        - `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.
+        - `out_indices` and `out_features` set: they are verified to be aligned.
+
+    Args:
+        out_features (`List[str]`): The names of the features for the backbone to output.
+        out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.
+        stage_names (`List[str]`): The names of the stages of the backbone.
+    """
+    # First verify that the out_features and out_indices are valid
+    verify_out_features_out_indices(
+        out_features=out_features, out_indices=out_indices, stage_names=stage_names
+    )
+    output_features, output_indices = _align_output_features_output_indices(
+        out_features=out_features, out_indices=out_indices, stage_names=stage_names
+    )
+    # Verify that the aligned out_features and out_indices are valid
+    verify_out_features_out_indices(
+        out_features=output_features,
+        out_indices=output_indices,
+        stage_names=stage_names,
+    )
+    return output_features, output_indices
+
+
+def find_pruneable_heads_and_indices(
+    heads: list[int],
+    n_heads: int,
+    head_size: int,
+    already_pruned_heads: set[int],
+) -> tuple[set[int], torch.LongTensor]:
+    mask = torch.ones(n_heads, head_size, dtype=torch.bool)
+
+    heads = set(heads) - already_pruned_heads
+
+    for head in heads:
+        # Shift the head index left by however many smaller heads
+        # were already removed earlier.
+        shifted_head = head - sum(1 for h in already_pruned_heads if h < head)
+        mask[shifted_head] = False
+
+    index = torch.arange(n_heads * head_size)[mask.view(-1)].long()
+    return heads, index
diff --git a/surya/common/load.py b/surya/common/load.py
new file mode 100644
index 0000000..e1c7643
--- /dev/null
+++ b/surya/common/load.py
@@ -0,0 +1,25 @@
+from typing import Optional, Any
+
+import torch
+
+from surya.settings import settings
+
+
+class ModelLoader:
+    def __init__(self, checkpoint: Optional[str] = None):
+        self.checkpoint = checkpoint
+
+    def model(
+        self,
+        device: torch.device | str | None = settings.TORCH_DEVICE_MODEL,
+        dtype: Optional[torch.dtype | str] = settings.MODEL_DTYPE,
+        attention_implementation: Optional[str] = None,
+    ) -> Any:
+        raise NotImplementedError()
+
+    def processor(
+        self,
+        device: torch.device | str | None = settings.TORCH_DEVICE_MODEL,
+        dtype: Optional[torch.dtype | str] = settings.MODEL_DTYPE,
+    ) -> Any:
+        raise NotImplementedError()
diff --git a/surya/common/order/__init__.py b/surya/common/order/__init__.py
new file mode 100644
index 0000000..c939e66
--- /dev/null
+++ b/surya/common/order/__init__.py
@@ -0,0 +1,3 @@
+from surya.common.order.predictor import OrderPredictor, load_order_predictor
+
+__all__ = ["OrderPredictor", "load_order_predictor"]
diff --git a/surya/common/order/order_ar.py b/surya/common/order/order_ar.py
new file mode 100644
index 0000000..58789da
--- /dev/null
+++ b/surya/common/order/order_ar.py
@@ -0,0 +1,137 @@
+"""Autoregressive reading-order head (inference only).
+
+Box tokens (geometry + label) cross-attend to the FULL rf-detr encoder feature map, then an AR
+decoder emits the reading-order permutation as indices into the canonically (raster) sorted box
+sequence. Constrained greedy decode -> a valid permutation (every box once, none invented).
+
+Vendored from training/models/rtdetr/order_ar.py (training code stripped). The 19-class layout
+taxonomy must match the layout detector the features come from.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+# 19-class layout taxonomy (sorted), must match the fast_layout detector's classes.
+LAYOUT_CLASSES = sorted(
+    [
+        "Caption",
+        "Footnote",
+        "Equation-Block",
+        "List-Group",
+        "Page-Header",
+        "Page-Footer",
+        "Image",
+        "Section-Header",
+        "Table",
+        "Text",
+        "Complex-Block",
+        "Code-Block",
+        "Form",
+        "Table-Of-Contents",
+        "Figure",
+        "Chemical-Block",
+        "Diagram",
+        "Bibliography",
+        "Blank-Page",
+    ]
+)
+N_LABELS = len(LAYOUT_CLASSES)
+MAX_BOXES = 128
+
+
+def box_features(boxes_1000):
+    """boxes 0-1000 [N,4] (x0,y0,x1,y1) -> [N,8] normalized (x0,y0,x1,y1,cx,cy,w,h)."""
+    b = np.asarray(boxes_1000, dtype=np.float32) / 1000.0
+    x0, y0, x1, y1 = b[:, 0], b[:, 1], b[:, 2], b[:, 3]
+    cx, cy, w, h = (x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)
+    return np.stack([x0, y0, x1, y1, cx, cy, w, h], axis=1)
+
+
+def canonical_order(boxes_1000, n_bands=24):
+    """Deterministic y-banded raster order: order[p] = original index of the box at raster pos p."""
+    b = np.asarray(boxes_1000, dtype=np.float32)
+    band_h = 1000.0 / n_bands
+    keys = [
+        (int(b[i, 1] // band_h), float(b[i, 0]), float(b[i, 1]), float(b[i, 2]))
+        for i in range(len(b))
+    ]
+    return sorted(range(len(b)), key=lambda i: keys[i])
+
+
+class ReadingOrderAR(nn.Module):
+    def __init__(
+        self,
+        d=128,
+        layers=3,
+        heads=4,
+        feat_dim=256,
+        feat_hw=28,
+        max_boxes=MAX_BOXES,
+        dropout=0.0,
+    ):
+        super().__init__()
+        self.d = d
+        self.max_boxes = max_boxes
+        self.use_feat = bool(feat_dim)
+        self.geom = nn.Linear(8, d)
+        self.lab = nn.Embedding(N_LABELS, d)
+        if self.use_feat:
+            self.feat_proj = nn.Linear(feat_dim, d)
+            self.feat_pos = nn.Parameter(torch.zeros(1, feat_hw * feat_hw, d))
+            ctx_layer = nn.TransformerDecoderLayer(
+                d, heads, d * 4, batch_first=True, dropout=dropout
+            )
+            self.ctx = nn.TransformerDecoder(ctx_layer, layers)
+        else:
+            enc_layer = nn.TransformerEncoderLayer(
+                d, heads, d * 4, batch_first=True, dropout=dropout
+            )
+            self.enc = nn.TransformerEncoder(enc_layer, layers)
+        dec_layer = nn.TransformerDecoderLayer(
+            d, heads, d * 4, batch_first=True, dropout=dropout
+        )
+        self.dec = nn.TransformerDecoder(dec_layer, layers)
+        self.bos = nn.Parameter(torch.zeros(d))
+        self.step = nn.Embedding(max_boxes + 1, d)
+        self.out = nn.Linear(d, max_boxes)
+
+    def encode(self, feats, labels, mask, fmap=None):
+        x = self.geom(feats) + self.lab(labels)
+        if self.use_feat and fmap is not None:
+            f = self.feat_proj(fmap) + self.feat_pos[:, : fmap.shape[1]]
+            x = self.ctx(x, f, tgt_key_padding_mask=~mask)
+        else:
+            x = self.enc(x, src_key_padding_mask=~mask)
+        return x
+
+    @torch.no_grad()
+    def decode(self, feats, labels, mask, fmap=None):
+        """Greedy constrained decode -> list (per batch item) of raster-position permutations."""
+        memory = self.encode(feats, labels, mask, fmap)
+        B = memory.shape[0]
+        K = mask.sum(1)
+        out = []
+        for b in range(B):
+            k = int(K[b].item())
+            mem = memory[b : b + 1, :k]
+            used = torch.zeros(k, dtype=torch.bool, device=memory.device)
+            din = [self.bos + self.step.weight[0]]
+            seq = []
+            for t in range(k):
+                x = torch.stack(din, 0).unsqueeze(0)
+                causal = torch.triu(
+                    torch.ones(t + 1, t + 1, device=memory.device, dtype=torch.bool), 1
+                )
+                h = self.dec(x, mem, tgt_mask=causal)
+                logits = self.out(h[0, -1]).clone()
+                logits[k:] = float("-inf")
+                logits[:k] = logits[:k].masked_fill(used, float("-inf"))
+                nxt = int(logits.argmax().item())
+                seq.append(nxt)
+                used[nxt] = True
+                din.append(mem[0, nxt] + self.step.weight[min(t + 1, self.max_boxes)])
+            out.append(seq)
+        return out
diff --git a/surya/common/order/predictor.py b/surya/common/order/predictor.py
new file mode 100644
index 0000000..588af02
--- /dev/null
+++ b/surya/common/order/predictor.py
@@ -0,0 +1,117 @@
+"""OrderPredictor — runs the AR reading-order head on rf-detr detections + the encoder feature map.
+
+Given, per page, the rf-detr projector feature map and the detected boxes (pixel xyxy) + labels,
+returns a reading-order position for each detection (0 = read first). Used by FastLayoutPredictor
+so layout always returns order.
+"""
+
+from __future__ import annotations
+
+import os
+from typing import List, Optional
+
+import numpy as np
+import torch
+
+from surya.common.order.order_ar import (
+    ReadingOrderAR,
+    canonical_order,
+    box_features,
+    LAYOUT_CLASSES,
+    MAX_BOXES,
+)
+
+
+class OrderPredictor:
+    def __init__(self, model_dir: str, device: str = "cpu"):
+        self.device = torch.device(device)
+        ckpt_path = os.path.join(model_dir, "order_ar.pt")
+        ck = torch.load(ckpt_path, map_location="cpu", weights_only=False)
+        self.feat_dim = int(ck.get("feat_dim", 256))
+        self.feat_hw = int(ck.get("feat_hw", 28))
+        self.res = int(ck.get("res", 448))
+        self.model = ReadingOrderAR(
+            d=int(ck.get("d", 128)),
+            layers=int(ck.get("layers", 3)),
+            feat_dim=self.feat_dim,
+            feat_hw=self.feat_hw,
+            dropout=0.0,
+        )
+        self.model.load_state_dict(ck["model"])
+        self.model.eval().to(self.device)
+
+    @torch.inference_mode()
+    def order_page(self, feature_map, boxes_xyxy, labels, width, height) -> List[int]:
+        """feature_map: [C,F,F] tensor (rf-detr projector output for this page).
+        boxes_xyxy: [N,4] pixel coords. labels: list of label strings (canonical class names).
+        Returns position[i] for each detection i (0 = read first)."""
+        n = len(boxes_xyxy)
+        if n == 0:
+            return []
+        if n == 1:
+            return [0]
+        if n > MAX_BOXES:  # fall back to raster order beyond the trained vocab width
+            return _raster_positions(boxes_xyxy)
+
+        boxes = np.asarray(boxes_xyxy, dtype=np.float32)
+        b1000 = np.empty_like(boxes)
+        b1000[:, [0, 2]] = boxes[:, [0, 2]] / max(1.0, width) * 1000.0
+        b1000[:, [1, 3]] = boxes[:, [1, 3]] / max(1.0, height) * 1000.0
+
+        order = canonical_order(b1000)  # raster pos -> original idx
+        b_raster = b1000[order]
+        lab_raster = [
+            LAYOUT_CLASSES.index(labels[p])
+            if labels[p] in LAYOUT_CLASSES
+            else LAYOUT_CLASSES.index("Text")
+            for p in order
+        ]
+
+        feats = torch.from_numpy(box_features(b_raster)).unsqueeze(0).to(self.device)
+        labs = torch.tensor(lab_raster, dtype=torch.long, device=self.device).unsqueeze(
+            0
+        )
+        mask = torch.ones(1, n, dtype=torch.bool, device=self.device)
+        # [C,F,F] -> [1, HW, C]
+        fmap = (
+            feature_map.reshape(self.feat_dim, -1)
+            .transpose(0, 1)
+            .unsqueeze(0)
+            .to(self.device, dtype=torch.float32)
+        )
+        pred = self.model.decode(feats, labs, mask, fmap)[
+            0
+        ]  # raster positions, in reading order
+
+        # raster pos p -> original idx order[p]; reading sequence of original indices:
+        reading = [order[p] for p in pred]
+        position = [0] * n
+        for rank, orig_idx in enumerate(reading):
+            position[orig_idx] = rank
+        return position
+
+
+def _raster_positions(boxes_xyxy) -> List[int]:
+    """Plain top-to-bottom, left-to-right fallback."""
+    idx = sorted(
+        range(len(boxes_xyxy)), key=lambda i: (boxes_xyxy[i][1], boxes_xyxy[i][0])
+    )
+    position = [0] * len(boxes_xyxy)
+    for rank, i in enumerate(idx):
+        position[i] = rank
+    return position
+
+
+def load_order_predictor(checkpoint: Optional[str] = None, device: str = "cpu"):
+    """Resolve + load the order predictor, or return None if no checkpoint is configured/available."""
+    from surya.common.rfdetr_torch import resolve_model_dir
+    from surya.settings import settings
+
+    ckpt = checkpoint or getattr(settings, "FAST_ORDER_MODEL_CHECKPOINT", None)
+    if not ckpt:
+        return None
+    try:
+        model_dir = resolve_model_dir(ckpt)
+        return OrderPredictor(model_dir, device=device)
+    except Exception:
+        return None
diff --git a/surya/common/polygon.py b/surya/common/polygon.py
new file mode 100644
index 0000000..80e7aec
--- /dev/null
+++ b/surya/common/polygon.py
@@ -0,0 +1,122 @@
+import copy
+from typing import List, Optional
+
+import numpy as np
+from pydantic import BaseModel, field_validator, computed_field
+import numbers
+
+
+class PolygonBox(BaseModel):
+    polygon: List[List[float]]
+    confidence: Optional[float] = None
+
+    @field_validator("polygon", mode="before")
+    @classmethod
+    def convert_bbox_to_polygon(cls, value):
+        if isinstance(value, (list, tuple)) and len(value) == 4:
+            if all(isinstance(x, numbers.Number) for x in value):
+                value = [float(v) for v in value]
+                x_min, y_min, x_max, y_max = value
+                polygon = [
+                    [x_min, y_min],
+                    [x_max, y_min],
+                    [x_max, y_max],
+                    [x_min, y_max],
+                ]
+                return polygon
+            elif all(
+                isinstance(point, (list, tuple)) and len(point) == 2 for point in value
+            ):
+                value = [[float(v) for v in point] for point in value]
+                return value
+        elif isinstance(value, np.ndarray):
+            if value.shape == (4, 2):
+                return value.tolist()
+
+        raise ValueError(
+            f"Input must be either a bbox [x_min, y_min, x_max, y_max] or a polygon with 4 corners [(x,y), (x,y), (x,y), (x,y)].  All values must be numeric. You passed {value} of type {type(value)}.  The first value is of type {type(value[0])}."
+        )
+
+    @property
+    def height(self):
+        return self.bbox[3] - self.bbox[1]
+
+    @property
+    def width(self):
+        return self.bbox[2] - self.bbox[0]
+
+    @property
+    def area(self):
+        return self.width * self.height
+
+    @computed_field
+    @property
+    def bbox(self) -> List[float]:
+        x_coords = [point[0] for point in self.polygon]
+        y_coords = [point[1] for point in self.polygon]
+        return [min(x_coords), min(y_coords), max(x_coords), max(y_coords)]
+
+    def rescale(self, processor_size, image_size):
+        # Point is in x, y format
+        page_width, page_height = processor_size
+
+        img_width, img_height = image_size
+        width_scaler = img_width / page_width
+        height_scaler = img_height / page_height
+
+        for corner in self.polygon:
+            corner[0] = int(corner[0] * width_scaler)
+            corner[1] = int(corner[1] * height_scaler)
+
+    def round(self, divisor):
+        for corner in self.polygon:
+            corner[0] = int(corner[0] / divisor) * divisor
+            corner[1] = int(corner[1] / divisor) * divisor
+
+    def fit_to_bounds(self, bounds):
+        new_corners = copy.deepcopy(self.polygon)
+        for corner in new_corners:
+            corner[0] = max(min(corner[0], bounds[2]), bounds[0])
+            corner[1] = max(min(corner[1], bounds[3]), bounds[1])
+        self.polygon = new_corners
+
+    def expand(self, x_margin: float, y_margin: float):
+        new_polygon = []
+        x_margin = x_margin * self.width
+        y_margin = y_margin * self.height
+        for idx, poly in enumerate(self.polygon):
+            if idx == 0:
+                new_polygon.append([int(poly[0] - x_margin), int(poly[1] - y_margin)])
+            elif idx == 1:
+                new_polygon.append([int(poly[0] + x_margin), int(poly[1] - y_margin)])
+            elif idx == 2:
+                new_polygon.append([int(poly[0] + x_margin), int(poly[1] + y_margin)])
+            elif idx == 3:
+                new_polygon.append([int(poly[0] - x_margin), int(poly[1] + y_margin)])
+        self.polygon = new_polygon
+
+    def intersection_area(self, other, x_margin=0, y_margin=0):
+        x_overlap = self.x_overlap(other, x_margin)
+        y_overlap = self.y_overlap(other, y_margin)
+        return x_overlap * y_overlap
+
+    def x_overlap(self, other, x_margin=0):
+        return max(
+            0,
+            min(self.bbox[2] + x_margin, other.bbox[2] + x_margin)
+            - max(self.bbox[0] - x_margin, other.bbox[0] - x_margin),
+        )
+
+    def y_overlap(self, other, y_margin=0):
+        return max(
+            0,
+            min(self.bbox[3] + y_margin, other.bbox[3] + y_margin)
+            - max(self.bbox[1] - y_margin, other.bbox[1] - y_margin),
+        )
+
+    @property
+    def center(self):
+        return [(self.bbox[0] + self.bbox[2]) / 2, (self.bbox[1] + self.bbox[3]) / 2]
+
+    def __hash__(self):
+        return hash(tuple(self.bbox))
diff --git a/surya/common/predictor.py b/surya/common/predictor.py
new file mode 100644
index 0000000..d7e6c72
--- /dev/null
+++ b/surya/common/predictor.py
@@ -0,0 +1,57 @@
+from typing import Optional
+
+import torch
+
+from surya.common.load import ModelLoader
+from surya.settings import settings
+
+
+class BasePredictor:
+    model_loader_cls = ModelLoader
+    batch_size: Optional[int] = None
+    default_batch_sizes = {"cpu": 1, "mps": 1, "cuda": 1}
+    torch_dtype = settings.MODEL_DTYPE
+
+    @property
+    def disable_tqdm(self) -> bool:
+        return self._disable_tqdm
+
+    @disable_tqdm.setter
+    def disable_tqdm(self, value: bool) -> None:
+        self._disable_tqdm = bool(value)
+
+    def __init__(
+        self,
+        checkpoint: Optional[str] = None,
+        device: torch.device | str | None = settings.TORCH_DEVICE_MODEL,
+        dtype: Optional[torch.dtype | str] = None,
+        attention_implementation: Optional[str] = None,
+    ):
+        if dtype is None:
+            dtype = self.torch_dtype
+
+        loader = self.model_loader_cls(checkpoint)
+        self.model = loader.model(device, dtype, attention_implementation)
+        self.processor = loader.processor()
+        self._disable_tqdm = settings.DISABLE_TQDM
+
+    def to(self, device_dtype: torch.device | str | None = None):
+        if hasattr(self, "model") and self.model:
+            self.model.to(device_dtype)
+            return
+        # Predictors that don't own a torch model (e.g. VLM-backed predictors that
+        # rely on an external server) treat .to() as a no-op.
+        if hasattr(self, "manager") and self.manager is not None:
+            return
+        raise ValueError("Model not loaded")
+
+    def get_batch_size(self):
+        batch_size = self.batch_size
+        if batch_size is None:
+            batch_size = self.default_batch_sizes["cpu"]
+            if settings.TORCH_DEVICE_MODEL in self.default_batch_sizes:
+                batch_size = self.default_batch_sizes[settings.TORCH_DEVICE_MODEL]
+        return batch_size
+
+    def __call__(self, *args, **kwargs):
+        raise NotImplementedError()
diff --git a/surya/common/pretrained.py b/surya/common/pretrained.py
new file mode 100644
index 0000000..6bf5606
--- /dev/null
+++ b/surya/common/pretrained.py
@@ -0,0 +1,22 @@
+from typing import Optional
+
+from transformers import PreTrainedModel
+from transformers.utils import is_flash_attn_2_available
+
+
+class SuryaPreTrainedModel(PreTrainedModel):
+    # No-op if we pass attention, so we can set attention however we want in the config
+    def _check_and_adjust_attn_implementation(
+        self, attn_implementation: Optional[str], **kwargs
+    ):
+        if attn_implementation is None:
+            try:
+                self._sdpa_can_dispatch(True)
+                attn_implementation = "sdpa"
+            except (ValueError, ImportError):
+                attn_implementation = "eager"
+
+            if self._supports_flash_attn and is_flash_attn_2_available():
+                attn_implementation = "flash_attention_2"
+
+        return attn_implementation
diff --git a/surya/common/rfdetr/__init__.py b/surya/common/rfdetr/__init__.py
new file mode 100644
index 0000000..3cf9858
--- /dev/null
+++ b/surya/common/rfdetr/__init__.py
@@ -0,0 +1,10 @@
+"""Vendored, slimmed, detection-only copy of Roboflow's RF-DETR for surya's fast detectors.
+
+Avoids a runtime dependency on the `rfdetr` package (and its heavy transitive deps:
+roboflow, rf100vl, albumentations, supervision, peft). Pure PyTorch; runs on cpu/mps/cuda.
+See `predictor.RFDetrDetector` for the inference entry point.
+"""
+
+from surya.common.rfdetr.predictor import RFDetrDetector
+
+__all__ = ["RFDetrDetector"]
diff --git a/surya/common/rfdetr/models/__init__.py b/surya/common/rfdetr/models/__init__.py
new file mode 100644
index 0000000..1db7822
--- /dev/null
+++ b/surya/common/rfdetr/models/__init__.py
@@ -0,0 +1,16 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Copied from Conditional DETR (https://github.com/Atten4Vis/ConditionalDETR)
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from surya.common.rfdetr.models.lwdetr import PostProcess, build_model
diff --git a/surya/common/rfdetr/models/backbone/__init__.py b/surya/common/rfdetr/models/backbone/__init__.py
new file mode 100644
index 0000000..6a4e834
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/__init__.py
@@ -0,0 +1,105 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from typing import Callable, Dict, List
+
+import torch
+from torch import nn
+
+from surya.common.rfdetr.models.backbone.backbone import *
+from surya.common.rfdetr.models.position_encoding import build_position_encoding
+from surya.common.rfdetr.util.misc import NestedTensor
+
+
+class Joiner(nn.Sequential):
+    def __init__(self, backbone, position_embedding):
+        super().__init__(backbone, position_embedding)
+        self._export = False
+
+    def forward(self, tensor_list: NestedTensor):
+        """ """
+        x = self[0](tensor_list)
+        pos = []
+        for x_ in x:
+            pos.append(self[1](x_, align_dim_orders=False).to(x_.tensors.dtype))
+        return x, pos
+
+    def export(self):
+        self._export = True
+        self._forward_origin = self.forward
+        self.forward = self.forward_export
+        for name, m in self.named_modules():
+            if hasattr(m, "export") and isinstance(m.export, Callable) and hasattr(m, "_export") and not m._export:
+                m.export()
+
+    def forward_export(self, inputs: torch.Tensor):
+        feats, masks = self[0](inputs)
+        poss = []
+        for feat, mask in zip(feats, masks):
+            poss.append(self[1](mask, align_dim_orders=False).to(feat.dtype))
+        return feats, None, poss
+
+
+def build_backbone(
+    encoder,
+    vit_encoder_num_layers,
+    pretrained_encoder,
+    window_block_indexes,
+    drop_path,
+    out_channels,
+    out_feature_indexes,
+    projector_scale,
+    use_cls_token,
+    hidden_dim,
+    position_embedding,
+    freeze_encoder,
+    layer_norm,
+    target_shape,
+    rms_norm,
+    backbone_lora,
+    force_no_pretrain,
+    gradient_checkpointing,
+    load_dinov2_weights,
+    patch_size,
+    num_windows,
+    positional_encoding_size,
+):
+    """
+    Useful args:
+        - encoder: encoder name
+        - lr_encoder:
+        - dilation
+        - use_checkpoint: for swin only for now
+
+    """
+    position_embedding = build_position_encoding(hidden_dim, position_embedding)
+
+    backbone = Backbone(
+        encoder,
+        pretrained_encoder,
+        window_block_indexes=window_block_indexes,
+        drop_path=drop_path,
+        out_channels=out_channels,
+        out_feature_indexes=out_feature_indexes,
+        projector_scale=projector_scale,
+        use_cls_token=use_cls_token,
+        layer_norm=layer_norm,
+        freeze_encoder=freeze_encoder,
+        target_shape=target_shape,
+        rms_norm=rms_norm,
+        backbone_lora=backbone_lora,
+        gradient_checkpointing=gradient_checkpointing,
+        load_dinov2_weights=load_dinov2_weights,
+        patch_size=patch_size,
+        num_windows=num_windows,
+        positional_encoding_size=positional_encoding_size,
+    )
+
+    model = Joiner(backbone, position_embedding)
+    return model
diff --git a/surya/common/rfdetr/models/backbone/backbone.py b/surya/common/rfdetr/models/backbone/backbone.py
new file mode 100644
index 0000000..4ba4e1f
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/backbone.py
@@ -0,0 +1,201 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Conditional DETR (https://github.com/Atten4Vis/ConditionalDETR)
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Backbone modules.
+"""
+
+import torch
+import torch.nn.functional as F
+
+from surya.common.rfdetr.models.backbone.base import BackboneBase
+from surya.common.rfdetr.models.backbone.dinov2 import DinoV2
+from surya.common.rfdetr.models.backbone.projector import MultiScaleProjector
+from surya.common.rfdetr.util.logger import get_logger
+from surya.common.rfdetr.util.misc import NestedTensor
+
+logger = get_logger()
+
+__all__ = ["Backbone"]
+
+
+class Backbone(BackboneBase):
+    """backbone."""
+
+    def __init__(
+        self,
+        name: str,
+        pretrained_encoder: str = None,
+        window_block_indexes: list = None,
+        drop_path=0.0,
+        out_channels=256,
+        out_feature_indexes: list = None,
+        projector_scale: list = None,
+        use_cls_token: bool = False,
+        freeze_encoder: bool = False,
+        layer_norm: bool = False,
+        target_shape: tuple[int, int] = (640, 640),
+        rms_norm: bool = False,
+        backbone_lora: bool = False,
+        gradient_checkpointing: bool = False,
+        load_dinov2_weights: bool = True,
+        patch_size: int = 14,
+        num_windows: int = 4,
+        positional_encoding_size: int = 0,
+    ):
+        super().__init__()
+        # an example name here would be "dinov2_base" or "dinov2_registers_windowed_base"
+        # if "registers" is in the name, then use_registers is set to True, otherwise it is set to False
+        # similarly, if "windowed" is in the name, then use_windowed_attn is set to True, otherwise it is set to False
+        # the last part of the name should be the size
+        # and the start should be dinov2
+        name_parts = name.split("_")
+        assert name_parts[0] == "dinov2"
+        # name_parts[-1]
+        use_registers = False
+        if "registers" in name_parts:
+            use_registers = True
+            name_parts.remove("registers")
+        use_windowed_attn = False
+        if "windowed" in name_parts:
+            use_windowed_attn = True
+            name_parts.remove("windowed")
+        assert len(name_parts) == 2, (
+            "name should be dinov2, then either registers, windowed, both, or none, then the size"
+        )
+        self.encoder = DinoV2(
+            size=name_parts[-1],
+            out_feature_indexes=out_feature_indexes,
+            shape=target_shape,
+            use_registers=use_registers,
+            use_windowed_attn=use_windowed_attn,
+            gradient_checkpointing=gradient_checkpointing,
+            load_dinov2_weights=load_dinov2_weights,
+            patch_size=patch_size,
+            num_windows=num_windows,
+            positional_encoding_size=positional_encoding_size,
+            drop_path_rate=drop_path,
+        )
+        # build encoder + projector as backbone module
+        if freeze_encoder:
+            for param in self.encoder.parameters():
+                param.requires_grad = False
+
+        self.projector_scale = projector_scale
+        assert len(self.projector_scale) > 0
+        # x[0]
+        assert sorted(self.projector_scale) == self.projector_scale, (
+            "only support projector scale P3/P4/P5/P6 in ascending order."
+        )
+        level2scalefactor = dict(P3=2.0, P4=1.0, P5=0.5, P6=0.25)
+        scale_factors = [level2scalefactor[lvl] for lvl in self.projector_scale]
+
+        self.projector = MultiScaleProjector(
+            in_channels=self.encoder._out_feature_channels,
+            out_channels=out_channels,
+            scale_factors=scale_factors,
+            layer_norm=layer_norm,
+            rms_norm=rms_norm,
+        )
+
+        self._export = False
+
+    def export(self):
+        self._export = True
+        self._forward_origin = self.forward
+        self.forward = self.forward_export
+
+    def forward(self, tensor_list: NestedTensor):
+        """ """
+        # (H, W, B, C)
+        feats = self.encoder(tensor_list.tensors)
+        feats = self.projector(feats)
+        # x: [(B, C, H, W)]
+        out = []
+        for feat in feats:
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=feat.shape[-2:]).to(torch.bool)[0]
+            out.append(NestedTensor(feat, mask))
+        return out
+
+    def forward_export(self, tensors: torch.Tensor):
+        feats = self.encoder(tensors)
+        feats = self.projector(feats)
+        out_feats = []
+        out_masks = []
+        for feat in feats:
+            # x: [(B, C, H, W)]
+            b, _, h, w = feat.shape
+            out_masks.append(torch.zeros((b, h, w), dtype=torch.bool, device=feat.device))
+            out_feats.append(feat)
+        return out_feats, out_masks
+
+    def get_named_param_lr_pairs(self, args, prefix: str = "backbone.0"):
+        num_layers = args.out_feature_indexes[-1] + 1
+        backbone_key = "backbone.0.encoder"
+        named_param_lr_pairs = {}
+        for n, p in self.named_parameters():
+            n = prefix + "." + n
+            if backbone_key in n and p.requires_grad:
+                lr = (
+                    args.lr_encoder
+                    * get_dinov2_lr_decay_rate(
+                        n,
+                        lr_decay_rate=args.lr_vit_layer_decay,
+                        num_layers=num_layers,
+                    )
+                    * args.lr_component_decay**2
+                )
+                wd = args.weight_decay * get_dinov2_weight_decay_rate(n)
+                named_param_lr_pairs[n] = {
+                    "params": p,
+                    "lr": lr,
+                    "weight_decay": wd,
+                }
+        return named_param_lr_pairs
+
+
+def get_dinov2_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12):
+    """
+    Calculate lr decay rate for different ViT blocks.
+
+    Args:
+        name (string): parameter name.
+        lr_decay_rate (float): base lr decay rate.
+        num_layers (int): number of ViT blocks.
+    Returns:
+        lr decay rate for the given parameter.
+    """
+    layer_id = num_layers + 1
+    if name.startswith("backbone"):
+        if "embeddings" in name:
+            layer_id = 0
+        elif ".layer." in name and ".residual." not in name:
+            layer_id = int(name[name.find(".layer.") :].split(".")[2]) + 1
+    return lr_decay_rate ** (num_layers + 1 - layer_id)
+
+
+def get_dinov2_weight_decay_rate(name, weight_decay_rate=1.0):
+    if (
+        ("gamma" in name)
+        or ("pos_embed" in name)
+        or ("rel_pos" in name)
+        or ("bias" in name)
+        or ("norm" in name)
+        or ("embeddings" in name)
+    ):
+        weight_decay_rate = 0.0
+    return weight_decay_rate
diff --git a/surya/common/rfdetr/models/backbone/base.py b/surya/common/rfdetr/models/backbone/base.py
new file mode 100644
index 0000000..631fdb0
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/base.py
@@ -0,0 +1,18 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from torch import nn
+
+
+class BackboneBase(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def get_named_param_lr_pairs(self, args, prefix: str):
+        raise NotImplementedError
diff --git a/surya/common/rfdetr/models/backbone/dinov2.py b/surya/common/rfdetr/models/backbone/dinov2.py
new file mode 100644
index 0000000..c186dcd
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/dinov2.py
@@ -0,0 +1,220 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import json
+import math
+import os
+import types
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoBackbone
+
+from surya.common.rfdetr.models.backbone.dinov2_with_windowed_attn import WindowedDinov2WithRegistersBackbone, \
+    WindowedDinov2WithRegistersConfig
+
+from surya.common.rfdetr.util.logger import get_logger
+
+logger = get_logger()
+
+size_to_width = {
+    "tiny": 192,
+    "small": 384,
+    "base": 768,
+    "large": 1024,
+}
+
+size_to_config = {
+    "small": "dinov2_small.json",
+    "base": "dinov2_base.json",
+    "large": "dinov2_large.json",
+}
+
+size_to_config_with_registers = {
+    "small": "dinov2_with_registers_small.json",
+    "base": "dinov2_with_registers_base.json",
+    "large": "dinov2_with_registers_large.json",
+}
+
+
+def get_config(size, use_registers):
+    config_dict = size_to_config_with_registers if use_registers else size_to_config
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    configs_dir = os.path.join(current_dir, "dinov2_configs")
+    config_path = os.path.join(configs_dir, config_dict[size])
+    with open(config_path, "r") as f:
+        dino_config = json.load(f)
+    return dino_config
+
+
+class DinoV2(nn.Module):
+    def __init__(
+        self,
+        shape=(640, 640),
+        out_feature_indexes=[2, 4, 5, 9],
+        size="base",
+        use_registers=True,
+        use_windowed_attn=True,
+        gradient_checkpointing=False,
+        load_dinov2_weights=True,
+        patch_size=14,
+        num_windows=4,
+        positional_encoding_size=37,
+        drop_path_rate=0.0,
+    ):
+        super().__init__()
+
+        name = f"facebook/dinov2-with-registers-{size}" if use_registers else f"facebook/dinov2-{size}"
+
+        self.shape = shape
+        self.patch_size = patch_size
+        self.num_windows = num_windows
+
+        # Create the encoder
+
+        if not use_windowed_attn:
+            assert not gradient_checkpointing, "Gradient checkpointing is not supported for non-windowed attention"
+            assert load_dinov2_weights, "Using non-windowed attention requires loading dinov2 weights from hub"
+            if drop_path_rate > 0.0:
+                logger.warning(
+                    "drop_path_rate > 0.0 is not supported for non-windowed DinoV2 backbones. "
+                    "drop_path will be ignored."
+                )
+            self.encoder = AutoBackbone.from_pretrained(
+                name,
+                out_features=[f"stage{i}" for i in out_feature_indexes],
+                return_dict=False,
+            )
+        else:
+            window_block_indexes = set(range(out_feature_indexes[-1] + 1))
+            window_block_indexes.difference_update(out_feature_indexes)
+            window_block_indexes = list(window_block_indexes)
+
+            dino_config = get_config(size, use_registers)
+
+            dino_config["return_dict"] = False
+            dino_config["out_features"] = [f"stage{i}" for i in out_feature_indexes]
+            dino_config["drop_path_rate"] = drop_path_rate
+
+            implied_resolution = positional_encoding_size * patch_size
+
+            if implied_resolution != dino_config["image_size"]:
+                logger.warning(
+                    "Using a different number of positional encodings than DINOv2, which means we're not loading DINOv2 backbone weights. This is not a problem if finetuning a pretrained RF-DETR model."
+                )
+                dino_config["image_size"] = implied_resolution
+                load_dinov2_weights = False
+
+            if patch_size != 14:
+                logger.warning(
+                    f"Using patch size {patch_size} instead of 14, which means we're not loading DINOv2 backbone weights. This is not a problem if finetuning a pretrained RF-DETR model."
+                )
+                dino_config["patch_size"] = patch_size
+                load_dinov2_weights = False
+
+            if use_registers:
+                windowed_dino_config = WindowedDinov2WithRegistersConfig(
+                    **dino_config,
+                    num_windows=num_windows,
+                    window_block_indexes=window_block_indexes,
+                    gradient_checkpointing=gradient_checkpointing,
+                )
+            else:
+                windowed_dino_config = WindowedDinov2WithRegistersConfig(
+                    **dino_config,
+                    num_windows=num_windows,
+                    window_block_indexes=window_block_indexes,
+                    num_register_tokens=0,
+                    gradient_checkpointing=gradient_checkpointing,
+                )
+            self.encoder = (
+                WindowedDinov2WithRegistersBackbone.from_pretrained(
+                    name,
+                    config=windowed_dino_config,
+                )
+                if load_dinov2_weights
+                else WindowedDinov2WithRegistersBackbone(windowed_dino_config)
+            )
+
+        self._out_feature_channels = [size_to_width[size]] * len(out_feature_indexes)
+        self._export = False
+
+    def export(self):
+        if self._export:
+            return
+        self._export = True
+        shape = self.shape
+
+        def make_new_interpolated_pos_encoding(position_embeddings, patch_size, height, width):
+
+            num_positions = position_embeddings.shape[1] - 1
+            dim = position_embeddings.shape[-1]
+            height = height // patch_size
+            width = width // patch_size
+
+            class_pos_embed = position_embeddings[:, 0]
+            patch_pos_embed = position_embeddings[:, 1:]
+
+            # Reshape and permute
+            patch_pos_embed = patch_pos_embed.reshape(
+                1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim
+            )
+            patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+            # Use bilinear interpolation without antialias
+            patch_pos_embed = F.interpolate(
+                patch_pos_embed,
+                size=(height, width),
+                mode="bicubic",
+                align_corners=False,
+                antialias=True,
+            )
+
+            # Reshape back
+            patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).reshape(1, -1, dim)
+            return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
+
+        # If the shape of self.encoder.embeddings.position_embeddings
+        # matches the shape of your new tensor, use copy_:
+        with torch.no_grad():
+            new_positions = make_new_interpolated_pos_encoding(
+                self.encoder.embeddings.position_embeddings,
+                self.encoder.config.patch_size,
+                shape[0],
+                shape[1],
+            )
+        # Create a new Parameter with the new size
+        old_interpolate_pos_encoding = self.encoder.embeddings.interpolate_pos_encoding
+
+        def new_interpolate_pos_encoding(self_mod, embeddings, height, width):
+            num_patches = embeddings.shape[1] - 1
+            num_positions = self_mod.position_embeddings.shape[1] - 1
+            if num_patches == num_positions and height == width:
+                return self_mod.position_embeddings
+            return old_interpolate_pos_encoding(embeddings, height, width)
+
+        self.encoder.embeddings.position_embeddings = nn.Parameter(new_positions)
+        self.encoder.embeddings.interpolate_pos_encoding = types.MethodType(
+            new_interpolate_pos_encoding, self.encoder.embeddings
+        )
+
+    def forward(self, x):
+        block_size = self.patch_size * self.num_windows
+        assert x.shape[2] % block_size == 0 and x.shape[3] % block_size == 0, (
+            f"Backbone requires input shape to be divisible by {block_size}, but got {x.shape}"
+        )
+        x = self.encoder(x)
+        return list(x[0])
+
+
+if __name__ == "__main__":
+    model = DinoV2()
+    model.export()
+    x = torch.randn(1, 3, 640, 640)
+    logger.info(model(x))
+    for j in model(x):
+        logger.info(j.shape)
diff --git a/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_base.json b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_base.json
new file mode 100644
index 0000000..9329afd
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_base.json
@@ -0,0 +1,24 @@
+{
+    "architectures": [
+        "Dinov2Model"
+    ],
+    "attention_probs_dropout_prob": 0.0,
+    "drop_path_rate": 0.0,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 768,
+    "image_size": 518,
+    "initializer_range": 0.02,
+    "layer_norm_eps": 1e-06,
+    "layerscale_value": 1.0,
+    "mlp_ratio": 4,
+    "model_type": "dinov2",
+    "num_attention_heads": 12,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "patch_size": 14,
+    "qkv_bias": true,
+    "torch_dtype": "float32",
+    "transformers_version": "4.31.0.dev0",
+    "use_swiglu_ffn": false
+}
diff --git a/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_large.json b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_large.json
new file mode 100644
index 0000000..ac22348
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_large.json
@@ -0,0 +1,24 @@
+{
+    "architectures": [
+        "Dinov2Model"
+    ],
+    "attention_probs_dropout_prob": 0.0,
+    "drop_path_rate": 0.0,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 1024,
+    "image_size": 518,
+    "initializer_range": 0.02,
+    "layer_norm_eps": 1e-06,
+    "layerscale_value": 1.0,
+    "mlp_ratio": 4,
+    "model_type": "dinov2",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "qkv_bias": true,
+    "torch_dtype": "float32",
+    "transformers_version": "4.31.0.dev0",
+    "use_swiglu_ffn": false
+}
diff --git a/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_small.json b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_small.json
new file mode 100644
index 0000000..6d50540
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_small.json
@@ -0,0 +1,24 @@
+{
+    "architectures": [
+        "Dinov2Model"
+    ],
+    "attention_probs_dropout_prob": 0.0,
+    "drop_path_rate": 0.0,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 384,
+    "image_size": 518,
+    "initializer_range": 0.02,
+    "layer_norm_eps": 1e-06,
+    "layerscale_value": 1.0,
+    "mlp_ratio": 4,
+    "model_type": "dinov2",
+    "num_attention_heads": 6,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "patch_size": 14,
+    "qkv_bias": true,
+    "torch_dtype": "float32",
+    "transformers_version": "4.32.0.dev0",
+    "use_swiglu_ffn": false
+}
diff --git a/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_base.json b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_base.json
new file mode 100644
index 0000000..29188e1
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_base.json
@@ -0,0 +1,50 @@
+{
+    "apply_layernorm": true,
+    "architectures": [
+        "Dinov2WithRegistersModel"
+    ],
+    "attention_probs_dropout_prob": 0.0,
+    "drop_path_rate": 0.0,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 768,
+    "image_size": 518,
+    "initializer_range": 0.02,
+    "interpolate_antialias": true,
+    "interpolate_offset": 0.0,
+    "layer_norm_eps": 1e-06,
+    "layerscale_value": 1.0,
+    "mlp_ratio": 4,
+    "model_type": "dinov2_with_registers",
+    "num_attention_heads": 12,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "num_register_tokens": 4,
+    "out_features": [
+        "stage12"
+    ],
+    "out_indices": [
+        12
+    ],
+    "patch_size": 14,
+    "qkv_bias": true,
+    "reshape_hidden_states": true,
+    "stage_names": [
+        "stem",
+        "stage1",
+        "stage2",
+        "stage3",
+        "stage4",
+        "stage5",
+        "stage6",
+        "stage7",
+        "stage8",
+        "stage9",
+        "stage10",
+        "stage11",
+        "stage12"
+    ],
+    "torch_dtype": "float32",
+    "transformers_version": "4.48.0.dev0",
+    "use_swiglu_ffn": false
+}
diff --git a/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_large.json b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_large.json
new file mode 100644
index 0000000..95b650d
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_large.json
@@ -0,0 +1,50 @@
+{
+    "apply_layernorm": true,
+    "architectures": [
+        "Dinov2WithRegistersModel"
+    ],
+    "attention_probs_dropout_prob": 0.0,
+    "drop_path_rate": 0.0,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 1024,
+    "image_size": 518,
+    "initializer_range": 0.02,
+    "interpolate_antialias": true,
+    "interpolate_offset": 0.0,
+    "layer_norm_eps": 1e-06,
+    "layerscale_value": 1.0,
+    "mlp_ratio": 4,
+    "model_type": "dinov2_with_registers",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "num_register_tokens": 4,
+    "out_features": [
+        "stage12"
+    ],
+    "out_indices": [
+        12
+    ],
+    "patch_size": 14,
+    "qkv_bias": true,
+    "reshape_hidden_states": true,
+    "stage_names": [
+        "stem",
+        "stage1",
+        "stage2",
+        "stage3",
+        "stage4",
+        "stage5",
+        "stage6",
+        "stage7",
+        "stage8",
+        "stage9",
+        "stage10",
+        "stage11",
+        "stage12"
+    ],
+    "torch_dtype": "float32",
+    "transformers_version": "4.48.0.dev0",
+    "use_swiglu_ffn": false
+}
diff --git a/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_small.json b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_small.json
new file mode 100644
index 0000000..13b1d79
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/dinov2_configs/dinov2_with_registers_small.json
@@ -0,0 +1,50 @@
+{
+    "apply_layernorm": true,
+    "architectures": [
+        "Dinov2WithRegistersModel"
+    ],
+    "attention_probs_dropout_prob": 0.0,
+    "drop_path_rate": 0.0,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 384,
+    "image_size": 518,
+    "initializer_range": 0.02,
+    "interpolate_antialias": true,
+    "interpolate_offset": 0.0,
+    "layer_norm_eps": 1e-06,
+    "layerscale_value": 1.0,
+    "mlp_ratio": 4,
+    "model_type": "dinov2_with_registers",
+    "num_attention_heads": 6,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "num_register_tokens": 4,
+    "out_features": [
+        "stage12"
+    ],
+    "out_indices": [
+        12
+    ],
+    "patch_size": 14,
+    "qkv_bias": true,
+    "reshape_hidden_states": true,
+    "stage_names": [
+        "stem",
+        "stage1",
+        "stage2",
+        "stage3",
+        "stage4",
+        "stage5",
+        "stage6",
+        "stage7",
+        "stage8",
+        "stage9",
+        "stage10",
+        "stage11",
+        "stage12"
+    ],
+    "torch_dtype": "float32",
+    "transformers_version": "4.48.0.dev0",
+    "use_swiglu_ffn": false
+}
diff --git a/surya/common/rfdetr/models/backbone/dinov2_with_windowed_attn.py b/surya/common/rfdetr/models/backbone/dinov2_with_windowed_attn.py
new file mode 100644
index 0000000..49fd241
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/dinov2_with_windowed_attn.py
@@ -0,0 +1,1187 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from HuggingFace Dinov2 (https://github.com/huggingface/transformers)
+# Copyright 2024 Meta Inc. and the HuggingFace Inc. team. All rights reserved.
+# ------------------------------------------------------------------------
+
+import collections.abc
+import math
+from typing import Dict, List, Optional, Set, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_outputs import (
+    BackboneOutput,
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
+    ImageClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import prune_linear_layer
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+    torch_int,
+)
+from transformers.utils.backbone_utils import (
+    BackboneConfigMixin,
+    BackboneMixin,
+)
+
+from surya.common.compat import find_pruneable_heads_and_indices, get_aligned_output_features_output_indices
+
+logger = logging.get_logger(__name__)
+
+# General docstring
+_CONFIG_FOR_DOC = "WindowedDinov2WithRegistersConfig"
+
+
+class WindowedDinov2WithRegistersConfig(BackboneConfigMixin, PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Dinov2WithRegistersModel`]. It is used to instantiate an
+    Dinov2WithRegisters model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the DINOv2 with Registers
+    [facebook/dinov2-with-registers-base](https://huggingface.co/facebook/dinov2-with-registers-base) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        mlp_ratio (`int`, *optional*, defaults to 4):
+            Ratio of the hidden size of the MLPs relative to the `hidden_size`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the layer normalization layers.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        num_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        qkv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to add a bias to the queries, keys and values.
+        layerscale_value (`float`, *optional*, defaults to 1.0):
+           Initial value to use for layer scale.
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Stochastic depth rate per sample (when applied in the main path of residual layers).
+        use_swiglu_ffn (`bool`, *optional*, defaults to `False`):
+            Whether to use the SwiGLU feedforward neural network.
+        num_register_tokens (`int`, *optional*, defaults to 4):
+            Number of register tokens to use.
+        out_features (`List[str]`, *optional*):
+            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
+            (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
+            corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
+            same order as defined in the `stage_names` attribute.
+        out_indices (`List[int]`, *optional*):
+            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
+            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
+            If unset and `out_features` is unset, will default to the last stage. Must be in the
+            same order as defined in the `stage_names` attribute.
+        apply_layernorm (`bool`, *optional*, defaults to `True`):
+            Whether to apply layer normalization to the feature maps in case the model is used as backbone.
+        reshape_hidden_states (`bool`, *optional*, defaults to `True`):
+            Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in
+            case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size,
+            seq_len, hidden_size)`.
+
+    Example:
+
+    >>> from surya.common.rfdetr.models.backbone.dinov2_with_windowed_attn import WindowedDinov2WithRegistersConfig
+
+    >>> # Initializing a tiny configuration suitable for doctests
+    >>> configuration = WindowedDinov2WithRegistersConfig(
+    ...     image_size=32,
+    ...     patch_size=16,
+    ...     hidden_size=32,
+    ...     num_hidden_layers=2,
+    ...     num_attention_heads=4,
+    ...     num_register_tokens=2,
+    ... )
+
+    >>> configuration.hidden_size
+    32
+
+    """
+
+    model_type = "dinov2_with_registers"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        mlp_ratio=4,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-6,
+        image_size=224,
+        patch_size=16,
+        num_channels=3,
+        qkv_bias=True,
+        layerscale_value=1.0,
+        drop_path_rate=0.0,
+        use_swiglu_ffn=False,
+        num_register_tokens=4,
+        out_features=None,
+        out_indices=None,
+        apply_layernorm=True,
+        reshape_hidden_states=True,
+        num_windows=1,
+        window_block_indexes=None,
+        gradient_checkpointing=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.mlp_ratio = mlp_ratio
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.qkv_bias = qkv_bias
+        self.layerscale_value = layerscale_value
+        self.drop_path_rate = drop_path_rate
+        self.use_swiglu_ffn = use_swiglu_ffn
+        self.num_register_tokens = num_register_tokens
+        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, num_hidden_layers + 1)]
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+        )
+        self.apply_layernorm = apply_layernorm
+        self.reshape_hidden_states = reshape_hidden_states
+        self.num_windows = num_windows
+        self.window_block_indexes = (
+            list(range(num_hidden_layers)) if window_block_indexes is None else window_block_indexes
+        )
+        self.gradient_checkpointing = gradient_checkpointing
+
+
+class Dinov2WithRegistersPatchEmbeddings(nn.Module):
+    """
+    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
+    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
+    Transformer.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        image_size, patch_size = config.image_size, config.patch_size
+        num_channels, hidden_size = config.num_channels, config.hidden_size
+
+        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
+        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.num_patches = num_patches
+
+        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        num_channels = pixel_values.shape[1]
+        if num_channels != self.num_channels:
+            raise ValueError(
+                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
+                f" Expected {self.num_channels} but got {num_channels}."
+            )
+        embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
+        return embeddings
+
+
+class WindowedDinov2WithRegistersEmbeddings(nn.Module):
+    """
+    Construct the CLS token, mask token, register tokens, position and patch embeddings.
+    """
+
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__()
+
+        self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
+        self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size))
+        self.register_tokens = (
+            nn.Parameter(torch.zeros(1, config.num_register_tokens, config.hidden_size))
+            if config.num_register_tokens > 0
+            else None
+        )
+        self.patch_embeddings = Dinov2WithRegistersPatchEmbeddings(config)
+        num_patches = self.patch_embeddings.num_patches
+        self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size))
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.patch_size = config.patch_size
+        self.config = config
+
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
+        resolution images. This implementation supports torch.jit tracing while maintaining backwards compatibility
+        with the original implementation.
+
+        Adapted from:
+        - https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+        - https://github.com/facebookresearch/dinov2/blob/main/dinov2/models/vision_transformer.py
+        """
+        num_patches = embeddings.shape[1] - 1
+        num_positions = self.position_embeddings.shape[1] - 1
+
+        # Skip interpolation for matching dimensions (unless tracing)
+        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+            return self.position_embeddings
+
+        # Handle class token and patch embeddings separately
+        class_pos_embed = self.position_embeddings[:, 0]
+        patch_pos_embed = self.position_embeddings[:, 1:]
+        dim = embeddings.shape[-1]
+
+        # Calculate new dimensions
+        height = height // self.config.patch_size
+        width = width // self.config.patch_size
+
+        # Reshape for interpolation
+        sqrt_num_positions = torch_int(num_positions**0.5)
+        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+        # Store original dtype for restoration after interpolation
+        target_dtype = patch_pos_embed.dtype
+
+        # Interpolate at float32 precision
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.to(dtype=torch.float32),
+            size=(torch_int(height), torch_int(width)),  # Explicit size instead of scale_factor
+            mode="bicubic",
+            align_corners=False,
+            antialias=True,
+        ).to(dtype=target_dtype)
+
+        # Validate output dimensions if not tracing
+        if not torch.jit.is_tracing():
+            if int(height) != patch_pos_embed.shape[-2] or int(width) != patch_pos_embed.shape[-1]:
+                raise ValueError("Width or height does not match with the interpolated position embeddings")
+
+        # Reshape back to original format
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+
+        # Combine class and patch embeddings
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
+
+    def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
+        batch_size, _, height, width = pixel_values.shape
+        target_dtype = self.patch_embeddings.projection.weight.dtype
+        embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))
+
+        if bool_masked_pos is not None:
+            embeddings = torch.where(
+                bool_masked_pos.unsqueeze(-1), self.mask_token.to(embeddings.dtype).unsqueeze(0), embeddings
+            )
+
+        # add the [CLS] token to the embedded patch tokens
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
+
+        # add positional encoding to each token
+        embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+
+        if self.config.num_windows > 1:
+            # reshape for windows
+            num_h_patches = height // self.config.patch_size
+            num_w_patches = width // self.config.patch_size
+            cls_token_with_pos_embed = embeddings[:, :1]
+            pixel_tokens_with_pos_embed = embeddings[:, 1:]
+            pixel_tokens_with_pos_embed = pixel_tokens_with_pos_embed.view(batch_size, num_h_patches, num_w_patches, -1)
+            num_w_patches_per_window = num_w_patches // self.config.num_windows
+            num_h_patches_per_window = num_h_patches // self.config.num_windows
+            num_windows = self.config.num_windows
+            windowed_pixel_tokens = pixel_tokens_with_pos_embed.reshape(
+                batch_size * num_windows, num_h_patches_per_window, num_windows, num_w_patches_per_window, -1
+            )
+            windowed_pixel_tokens = windowed_pixel_tokens.permute(0, 2, 1, 3, 4)
+            windowed_pixel_tokens = windowed_pixel_tokens.reshape(
+                batch_size * num_windows**2, num_h_patches_per_window * num_w_patches_per_window, -1
+            )
+            windowed_cls_token_with_pos_embed = cls_token_with_pos_embed.repeat(num_windows**2, 1, 1)
+            embeddings = torch.cat((windowed_cls_token_with_pos_embed, windowed_pixel_tokens), dim=1)
+
+        # add register tokens
+        embeddings = (
+            torch.cat(
+                (embeddings[:, :1], self.register_tokens.expand(embeddings.shape[0], -1, -1), embeddings[:, 1:]), dim=1
+            )
+            if self.config.num_register_tokens > 0
+            else embeddings
+        )
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+
+class Dinov2WithRegistersSelfAttention(nn.Module):
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size {(config.hidden_size,)} is not a multiple of the number of attention "
+                f"heads {config.num_attention_heads}."
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        mixed_query_layer = self.query(hidden_states)
+
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        return outputs
+
+
+class Dinov2WithRegistersSdpaSelfAttention(Dinov2WithRegistersSelfAttention):
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__(config)
+        self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
+
+    def forward(
+        self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Dinov2WithRegistersModel is using Dinov2WithRegistersSdpaSelfAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states, head_mask=head_mask, output_attentions=output_attentions
+            )
+
+        mixed_query_layer = self.query(hidden_states)
+
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        context_layer = torch.nn.functional.scaled_dot_product_attention(
+            query_layer,
+            key_layer,
+            value_layer,
+            head_mask,
+            self.attention_probs_dropout_prob if self.training else 0.0,
+            is_causal=False,
+            scale=None,
+        )
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        return context_layer, None
+
+
+class Dinov2WithRegistersSelfOutput(nn.Module):
+    """
+    The residual connection is defined in Dinov2WithRegistersLayer instead of here (as is the case with other models), due to the
+    layernorm applied before each block.
+    """
+
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        return hidden_states
+
+
+class Dinov2WithRegistersAttention(nn.Module):
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__()
+        self.attention = Dinov2WithRegistersSelfAttention(config)
+        self.output = Dinov2WithRegistersSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads: Set[int]) -> None:
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.attention.query = prune_linear_layer(self.attention.query, index)
+        self.attention.key = prune_linear_layer(self.attention.key, index)
+        self.attention.value = prune_linear_layer(self.attention.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
+        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        self_outputs = self.attention(hidden_states, head_mask, output_attentions)
+
+        attention_output = self.output(self_outputs[0], hidden_states)
+
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class Dinov2WithRegistersSdpaAttention(Dinov2WithRegistersAttention):
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__(config)
+        self.attention = Dinov2WithRegistersSdpaSelfAttention(config)
+
+
+class Dinov2WithRegistersLayerScale(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        self.lambda1 = nn.Parameter(config.layerscale_value * torch.ones(config.hidden_size))
+
+    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        return hidden_state * self.lambda1
+
+
+def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
+    """
+    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
+    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
+    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
+    argument.
+    """
+    if drop_prob == 0.0 or not training:
+        return input
+    keep_prob = 1 - drop_prob
+    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
+    random_tensor.floor_()  # binarize
+    output = input.div(keep_prob) * random_tensor
+    return output
+
+
+class Dinov2WithRegistersDropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob: Optional[float] = None) -> None:
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
+
+    def extra_repr(self) -> str:
+        return "p={}".format(self.drop_prob)
+
+
+class Dinov2WithRegistersMLP(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        in_features = out_features = config.hidden_size
+        hidden_features = int(config.hidden_size * config.mlp_ratio)
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
+        if isinstance(config.hidden_act, str):
+            self.activation = ACT2FN[config.hidden_act]
+        else:
+            self.activation = config.hidden_act
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=True)
+
+    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        hidden_state = self.fc1(hidden_state)
+        hidden_state = self.activation(hidden_state)
+        hidden_state = self.fc2(hidden_state)
+        return hidden_state
+
+
+class Dinov2WithRegistersSwiGLUFFN(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        in_features = out_features = config.hidden_size
+        hidden_features = int(config.hidden_size * config.mlp_ratio)
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+
+        self.weights_in = nn.Linear(in_features, 2 * hidden_features, bias=True)
+        self.weights_out = nn.Linear(hidden_features, out_features, bias=True)
+
+    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        hidden_state = self.weights_in(hidden_state)
+        x1, x2 = hidden_state.chunk(2, dim=-1)
+        hidden = nn.functional.silu(x1) * x2
+        return self.weights_out(hidden)
+
+
+DINOV2_WITH_REGISTERS_ATTENTION_CLASSES = {
+    "eager": Dinov2WithRegistersAttention,
+    "sdpa": Dinov2WithRegistersSdpaAttention,
+}
+
+
+class WindowedDinov2WithRegistersLayer(nn.Module):
+    """This corresponds to the Block class in the original implementation."""
+
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__()
+
+        self.num_windows = config.num_windows
+
+        self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.attention = DINOV2_WITH_REGISTERS_ATTENTION_CLASSES[config._attn_implementation](config)
+        self.layer_scale1 = Dinov2WithRegistersLayerScale(config)
+        self.drop_path = (
+            Dinov2WithRegistersDropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()
+        )
+
+        self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        if config.use_swiglu_ffn:
+            self.mlp = Dinov2WithRegistersSwiGLUFFN(config)
+        else:
+            self.mlp = Dinov2WithRegistersMLP(config)
+        self.layer_scale2 = Dinov2WithRegistersLayerScale(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        run_full_attention: bool = False,
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        assert head_mask is None, "head_mask is not supported for windowed attention"
+        assert not output_attentions, "output_attentions is not supported for windowed attention"
+        shortcut = hidden_states
+        if run_full_attention:
+            # reshape x to remove windows
+            B, HW, C = hidden_states.shape
+            num_windows_squared = self.num_windows**2
+            hidden_states = hidden_states.view(B // num_windows_squared, num_windows_squared * HW, C)
+
+        self_attention_outputs = self.attention(
+            self.norm1(hidden_states),  # in Dinov2WithRegisters, layernorm is applied before self-attention
+            head_mask,
+            output_attentions=output_attentions,
+        )
+        attention_output = self_attention_outputs[0]
+
+        if run_full_attention:
+            # reshape x to add windows back
+            B, HW, C = hidden_states.shape
+            num_windows_squared = self.num_windows**2
+            # hidden_states = hidden_states.view(B * num_windows_squared, HW // num_windows_squared, C)
+            attention_output = attention_output.view(B * num_windows_squared, HW // num_windows_squared, C)
+
+        attention_output = self.layer_scale1(attention_output)
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        # first residual connection
+        hidden_states = self.drop_path(attention_output) + shortcut
+
+        # in Dinov2WithRegisters, layernorm is also applied after self-attention
+        layer_output = self.norm2(hidden_states)
+        layer_output = self.mlp(layer_output)
+        layer_output = self.layer_scale2(layer_output)
+
+        # second residual connection
+        layer_output = self.drop_path(layer_output) + hidden_states
+
+        outputs = (layer_output,) + outputs
+
+        return outputs
+
+
+class WindowedDinov2WithRegistersEncoder(nn.Module):
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([WindowedDinov2WithRegistersLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = config.gradient_checkpointing
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ) -> Union[tuple, BaseModelOutput]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if i > int(self.config.out_features[-1][5:]):
+                # early stop if we have reached the last output feature
+                break
+
+            run_full_attention = i not in self.config.window_block_indexes
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    layer_head_mask,
+                    output_attentions,
+                    run_full_attention,
+                )
+            else:
+                layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions, run_full_attention)
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+class WindowedDinov2WithRegistersPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = WindowedDinov2WithRegistersConfig
+    base_model_prefix = "dinov2_with_registers"
+    main_input_name = "pixel_values"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Dinov2WithRegistersSwiGLUFFN"]
+    _supports_sdpa = True
+
+    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
+        """Initialize the weights"""
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
+            # `trunc_normal_cpu` not implemented in `half` issues
+            module.weight.data = nn.init.trunc_normal_(
+                module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
+            ).to(module.weight.dtype)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, WindowedDinov2WithRegistersEmbeddings):
+            module.position_embeddings.data = nn.init.trunc_normal_(
+                module.position_embeddings.data.to(torch.float32),
+                mean=0.0,
+                std=self.config.initializer_range,
+            ).to(module.position_embeddings.dtype)
+
+            module.cls_token.data = nn.init.trunc_normal_(
+                module.cls_token.data.to(torch.float32),
+                mean=0.0,
+                std=self.config.initializer_range,
+            ).to(module.cls_token.dtype)
+
+
+DINOV2_WITH_REGISTERS_START_DOCSTRING = r"""
+    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
+    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
+    behavior.
+
+    Parameters:
+        config ([`Dinov2WithRegistersConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+DINOV2_WITH_REGISTERS_BASE_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
+            [`BitImageProcessor.preprocess`] for details.
+
+        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
+            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Only relevant for
+            pre-training.
+
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Dinov2WithRegisters Model transformer outputting raw hidden-states without any specific head on top.",
+    DINOV2_WITH_REGISTERS_START_DOCSTRING,
+)
+class WindowedDinov2WithRegistersModel(WindowedDinov2WithRegistersPreTrainedModel):
+    def __init__(self, config: WindowedDinov2WithRegistersConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = WindowedDinov2WithRegistersEmbeddings(config)
+        self.encoder = WindowedDinov2WithRegistersEncoder(config)
+
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> Dinov2WithRegistersPatchEmbeddings:
+        return self.embeddings.patch_embeddings
+
+    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(DINOV2_WITH_REGISTERS_BASE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        bool_masked_pos: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        """
+        Returns:
+
+        Examples:
+
+        >>> import torch
+        >>> from surya.common.rfdetr.models.backbone.dinov2_with_windowed_attn import (
+        ...     WindowedDinov2WithRegistersConfig,
+        ...     WindowedDinov2WithRegistersModel,
+        ... )
+        >>> config = WindowedDinov2WithRegistersConfig(
+        ...     image_size=32,
+        ...     patch_size=16,
+        ...     hidden_size=32,
+        ...     num_hidden_layers=2,
+        ...     num_attention_heads=4,
+        ...     num_register_tokens=2,
+        ... )
+        >>> model = WindowedDinov2WithRegistersModel(config)
+        >>> pixel_values = torch.randn(1, 3, 32, 32)
+        >>> outputs = model(pixel_values)
+        >>> list(outputs.last_hidden_state.shape)
+        [1, 7, 32]
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        pooled_output = sequence_output[:, 0, :]
+
+        if not return_dict:
+            head_outputs = (sequence_output, pooled_output)
+            return head_outputs + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+DINOV2_WITH_REGISTERS_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
+            [`BitImageProcessor.preprocess`] for details.
+
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    """
+    Dinov2WithRegisters Model transformer with an image classification head on top (a linear layer on top of the final hidden state
+    of the [CLS] token) e.g. for ImageNet.
+    """,
+    DINOV2_WITH_REGISTERS_START_DOCSTRING,
+)
+class WindowedDinov2WithRegistersForImageClassification(WindowedDinov2WithRegistersPreTrainedModel):
+    def __init__(self, config: WindowedDinov2WithRegistersConfig) -> None:
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.dinov2_with_registers = WindowedDinov2WithRegistersModel(config)
+
+        # Classifier head
+        self.classifier = (
+            nn.Linear(config.hidden_size * 2, config.num_labels) if config.num_labels > 0 else nn.Identity()
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(DINOV2_WITH_REGISTERS_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=ImageClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple, ImageClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Example:
+
+        >>> import torch
+        >>> from surya.common.rfdetr.models.backbone.dinov2_with_windowed_attn import (
+        ...     WindowedDinov2WithRegistersConfig,
+        ...     WindowedDinov2WithRegistersForImageClassification,
+        ... )
+        >>> config = WindowedDinov2WithRegistersConfig(
+        ...     image_size=32,
+        ...     patch_size=16,
+        ...     hidden_size=32,
+        ...     num_hidden_layers=2,
+        ...     num_attention_heads=4,
+        ...     num_register_tokens=2,
+        ...     num_labels=3,
+        ... )
+        >>> model = WindowedDinov2WithRegistersForImageClassification(config)
+        >>> pixel_values = torch.randn(1, 3, 32, 32)
+        >>> outputs = model(pixel_values)
+        >>> list(outputs.logits.shape)
+        [1, 3]
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.dinov2_with_registers(
+            pixel_values,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]  # batch_size, sequence_length, hidden_size
+
+        cls_token = sequence_output[:, 0]
+        patch_tokens = sequence_output[:, 1:]
+
+        linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1)
+
+        logits = self.classifier(linear_input)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Dinov2WithRegisters backbone, to be used with frameworks like DETR and MaskFormer.
+    """,
+    DINOV2_WITH_REGISTERS_START_DOCSTRING,
+)
+class WindowedDinov2WithRegistersBackbone(WindowedDinov2WithRegistersPreTrainedModel, BackboneMixin):
+    def __init__(self, config: WindowedDinov2WithRegistersConfig):
+        super().__init__(config)
+        self._init_transformers_backbone()
+        self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
+        self.embeddings = WindowedDinov2WithRegistersEmbeddings(config)
+        self.encoder = WindowedDinov2WithRegistersEncoder(config)
+
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.num_register_tokens = config.num_register_tokens
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> Dinov2WithRegistersPatchEmbeddings:
+        return self.embeddings.patch_embeddings
+
+    @add_start_docstrings_to_model_forward(DINOV2_WITH_REGISTERS_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> BackboneOutput:
+        """
+        Returns:
+
+        Examples:
+
+        >>> import torch
+        >>> from surya.common.rfdetr.models.backbone.dinov2_with_windowed_attn import (
+        ...     WindowedDinov2WithRegistersBackbone,
+        ...     WindowedDinov2WithRegistersConfig,
+        ... )
+        >>> config = WindowedDinov2WithRegistersConfig(
+        ...     image_size=32,
+        ...     patch_size=16,
+        ...     hidden_size=32,
+        ...     num_hidden_layers=2,
+        ...     num_attention_heads=4,
+        ...     num_register_tokens=2,
+        ...     out_indices=[2],
+        ... )
+        >>> model = WindowedDinov2WithRegistersBackbone(config)
+        >>> pixel_values = torch.randn(1, 3, 32, 32)
+        >>> outputs = model(pixel_values)
+        >>> len(outputs.feature_maps)
+        1
+        >>> list(outputs.feature_maps[0].shape)
+        [1, 32, 2, 2]
+
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+        embedding_output = self.embeddings(pixel_values)
+
+        outputs = self.encoder(
+            embedding_output, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict
+        )
+
+        hidden_states = outputs.hidden_states if return_dict else outputs[1]
+
+        feature_maps = ()
+        for stage, hidden_state in zip(self.stage_names, hidden_states):
+            if stage in self.out_features:
+                if self.config.apply_layernorm:
+                    hidden_state = self.layernorm(hidden_state)
+                if self.config.reshape_hidden_states:
+                    hidden_state = hidden_state[:, self.num_register_tokens + 1 :]
+                    # this was actually a bug in the original implementation that we copied here,
+                    # cause normally the order is height, width
+                    batch_size, _, height, width = pixel_values.shape
+                    patch_size = self.config.patch_size
+
+                    num_h_patches = height // patch_size
+                    num_w_patches = width // patch_size
+
+                    if self.config.num_windows > 1:
+                        # undo windowing
+                        num_windows_squared = self.config.num_windows**2
+                        B, HW, C = hidden_state.shape
+                        num_h_patches_per_window = num_h_patches // self.config.num_windows
+                        num_w_patches_per_window = num_w_patches // self.config.num_windows
+                        hidden_state = hidden_state.reshape(B // num_windows_squared, num_windows_squared * HW, C)
+                        hidden_state = hidden_state.reshape(
+                            (B // num_windows_squared) * self.config.num_windows,
+                            self.config.num_windows,
+                            num_h_patches_per_window,
+                            num_w_patches_per_window,
+                            C,
+                        )
+                        hidden_state = hidden_state.permute(0, 2, 1, 3, 4)
+
+                    hidden_state = hidden_state.reshape(batch_size, num_h_patches, num_w_patches, -1)
+                    hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
+
+                feature_maps += (hidden_state,)
+
+        if not return_dict:
+            if output_hidden_states:
+                output = (feature_maps,) + outputs[1:]
+            else:
+                output = (feature_maps,) + outputs[2:]
+            return output
+
+        return BackboneOutput(
+            feature_maps=feature_maps,
+            hidden_states=outputs.hidden_states if output_hidden_states else None,
+            attentions=outputs.attentions if output_attentions else None,
+        )
+
+
+__all__ = [
+    "WindowedDinov2WithRegistersPreTrainedModel",
+    "WindowedDinov2WithRegistersModel",
+    "WindowedDinov2WithRegistersForImageClassification",
+    "WindowedDinov2WithRegistersBackbone",
+]
diff --git a/surya/common/rfdetr/models/backbone/projector.py b/surya/common/rfdetr/models/backbone/projector.py
new file mode 100644
index 0000000..2fd1d70
--- /dev/null
+++ b/surya/common/rfdetr/models/backbone/projector.py
@@ -0,0 +1,315 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from ViTDet (https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Projector
+"""
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LayerNorm(nn.Module):
+    """
+    A LayerNorm variant, popularized by Transformers, that performs point-wise mean and
+    variance normalization over the channel dimension for inputs that have shape
+    (batch_size, channels, height, width).
+    https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119
+    """
+
+    def __init__(self, normalized_shape, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(normalized_shape))
+        self.bias = nn.Parameter(torch.zeros(normalized_shape))
+        self.eps = eps
+        self.normalized_shape = (normalized_shape,)
+
+    def forward(self, x):
+        """
+        LayerNorm forward
+        TODO: this is a hack to avoid overflow when using fp16
+        """
+        x = x.permute(0, 2, 3, 1)
+        x = F.layer_norm(x, (x.size(3),), self.weight, self.bias, self.eps)
+        x = x.permute(0, 3, 1, 2)
+        return x
+
+
+def get_norm(norm, out_channels):
+    """
+    Args:
+        norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
+            or a callable that takes a channel number and returns
+            the normalization layer as a nn.Module.
+    Returns:
+        nn.Module or None: the normalization layer
+    """
+    if norm is None:
+        return None
+    if isinstance(norm, str):
+        if len(norm) == 0:
+            return None
+        norm = {
+            "LN": lambda channels: LayerNorm(channels),
+        }[norm]
+    return norm(out_channels)
+
+
+def get_activation(name, inplace=False):
+    """get activation"""
+    if name == "silu":
+        module = nn.SiLU(inplace=inplace)
+    elif name == "relu":
+        module = nn.ReLU(inplace=inplace)
+    elif name in ["LeakyReLU", "leakyrelu", "lrelu"]:
+        module = nn.LeakyReLU(0.1, inplace=inplace)
+    elif name is None:
+        module = nn.Identity()
+    else:
+        raise AttributeError("Unsupported act type: {}".format(name))
+    return module
+
+
+class ConvX(nn.Module):
+    """Conv-bn module"""
+
+    def __init__(
+        self,
+        in_planes,
+        out_planes,
+        kernel=3,
+        stride=1,
+        groups=1,
+        dilation=1,
+        act="relu",
+        layer_norm=False,
+        rms_norm=False,
+    ):
+        super(ConvX, self).__init__()
+        if not isinstance(kernel, tuple):
+            kernel = (kernel, kernel)
+        padding = (kernel[0] // 2, kernel[1] // 2)
+        self.conv = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size=kernel,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            dilation=dilation,
+            bias=False,
+        )
+        if rms_norm:
+            self.bn = nn.RMSNorm(out_planes)
+        else:
+            self.bn = get_norm("LN", out_planes) if layer_norm else nn.BatchNorm2d(out_planes)
+        self.act = get_activation(act, inplace=True)
+
+    def forward(self, x):
+        """forward"""
+        out = self.act(self.bn(self.conv(x.contiguous())))
+        return out
+
+
+class Bottleneck(nn.Module):
+    """Standard bottleneck."""
+
+    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5, act="silu", layer_norm=False, rms_norm=False):
+        """ch_in, ch_out, shortcut, groups, kernels, expand"""
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = ConvX(c1, c_, k[0], 1, act=act, layer_norm=layer_norm, rms_norm=rms_norm)
+        self.cv2 = ConvX(c_, c2, k[1], 1, groups=g, act=act, layer_norm=layer_norm, rms_norm=rms_norm)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        """'forward()' applies the YOLOv5 FPN to input data."""
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class C2f(nn.Module):
+    """Faster Implementation of CSP Bottleneck with 2 convolutions."""
+
+    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, act="silu", layer_norm=False, rms_norm=False):
+        """ch_in, ch_out, number, shortcut, groups, expansion"""
+        super().__init__()
+        self.c = int(c2 * e)  # hidden channels
+        self.cv1 = ConvX(c1, 2 * self.c, 1, 1, act=act, layer_norm=layer_norm, rms_norm=rms_norm)
+        self.cv2 = ConvX(
+            (2 + n) * self.c, c2, 1, act=act, layer_norm=layer_norm, rms_norm=rms_norm
+        )  # optional act=FReLU(c2)
+        self.m = nn.ModuleList(
+            Bottleneck(self.c, self.c, shortcut, g, k=(3, 3), e=1.0, act=act, layer_norm=layer_norm, rms_norm=rms_norm)
+            for _ in range(n)
+        )
+
+    def forward(self, x):
+        """Forward pass using split() instead of chunk()."""
+        y = list(self.cv1(x).split((self.c, self.c), 1))
+        y.extend(m(y[-1]) for m in self.m)
+        return self.cv2(torch.cat(y, 1))
+
+
+class MultiScaleProjector(nn.Module):
+    """
+    This module implements MultiScaleProjector in :paper:`lwdetr`.
+    It creates pyramid features built on top of the input feature map.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        scale_factors,
+        num_blocks=3,
+        layer_norm=False,
+        rms_norm=False,
+        survival_prob=1.0,
+        force_drop_last_n_features=0,
+    ):
+        """
+        Args:
+            net (Backbone): module representing the subnetwork backbone.
+                Must be a subclass of :class:`Backbone`.
+            out_channels (int): number of channels in the output feature maps.
+            scale_factors (list[float]): list of scaling factors to upsample or downsample
+                the input features for creating pyramid features.
+        """
+        super(MultiScaleProjector, self).__init__()
+
+        self.scale_factors = scale_factors
+        self.survival_prob = survival_prob
+        self.force_drop_last_n_features = force_drop_last_n_features
+
+        stages_sampling = []
+        stages = []
+        # use_bias = norm == ""
+        self.use_extra_pool = False
+        for scale in scale_factors:
+            stages_sampling.append([])
+            for in_dim in in_channels:
+                layers = []
+
+                # if in_dim > 512:
+                #     layers.append(ConvX(in_dim, in_dim // 2, kernel=1))
+                #     in_dim = in_dim // 2
+
+                if scale == 4.0:
+                    layers.extend(
+                        [
+                            nn.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2),
+                            get_norm("LN", in_dim // 2),
+                            nn.GELU(),
+                            nn.ConvTranspose2d(in_dim // 2, in_dim // 4, kernel_size=2, stride=2),
+                        ]
+                    )
+                    # in_dim // 4
+                elif scale == 2.0:
+                    # a hack to reduce the FLOPs and Params when the dimension of output feature is too large
+                    # if in_dim > 512:
+                    #     layers = [
+                    #         ConvX(in_dim, in_dim // 2, kernel=1),
+                    #         nn.ConvTranspose2d(in_dim // 2, in_dim // 4, kernel_size=2, stride=2),
+                    #     ]
+                    #     out_dim = in_dim // 4
+                    # else:
+                    layers.extend(
+                        [
+                            nn.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2),
+                        ]
+                    )
+                    # in_dim // 2
+                elif scale == 1.0:
+                    pass
+                elif scale == 0.5:
+                    layers.extend(
+                        [
+                            ConvX(in_dim, in_dim, 3, 2, layer_norm=layer_norm),
+                        ]
+                    )
+                elif scale == 0.25:
+                    self.use_extra_pool = True
+                    continue
+                else:
+                    raise NotImplementedError("Unsupported scale_factor:{}".format(scale))
+                layers = nn.Sequential(*layers)
+                stages_sampling[-1].append(layers)
+            stages_sampling[-1] = nn.ModuleList(stages_sampling[-1])
+
+            in_dim = int(sum(in_channel // max(1, scale) for in_channel in in_channels))
+            layers = [
+                C2f(in_dim, out_channels, num_blocks, layer_norm=layer_norm),
+                get_norm("LN", out_channels),
+            ]
+            layers = nn.Sequential(*layers)
+            stages.append(layers)
+
+        self.stages_sampling = nn.ModuleList(stages_sampling)
+        self.stages = nn.ModuleList(stages)
+
+    def forward(self, x):
+        """
+        Args:
+            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
+        Returns:
+            dict[str->Tensor]:
+                mapping from feature map name to pyramid feature map tensor
+                in high to low resolution order. Returned feature names follow the FPN
+                convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
+                ["p2", "p3", ..., "p6"].
+        """
+        num_features = len(x)
+        if self.survival_prob < 1.0 and self.training:
+            final_drop_prob = 1 - self.survival_prob
+            drop_p = np.random.uniform()
+            for i in range(1, num_features):
+                critical_drop_prob = i * (final_drop_prob / (num_features - 1))
+                if drop_p < critical_drop_prob:
+                    x[i][:] = 0
+        elif self.force_drop_last_n_features > 0:
+            for i in range(self.force_drop_last_n_features):
+                # don't do it inplace to ensure the compiler can optimize out the backbone layers
+                x[-(i + 1)] = torch.zeros_like(x[-(i + 1)])
+
+        results = []
+        # x list of len(out_features_indexes)
+        for i, stage in enumerate(self.stages):
+            feat_fuse = []
+            for j, stage_sampling in enumerate(self.stages_sampling[i]):
+                feat_fuse.append(stage_sampling(x[j]))
+            if len(feat_fuse) > 1:
+                feat_fuse = torch.cat(feat_fuse, dim=1)
+            else:
+                feat_fuse = feat_fuse[0]
+            results.append(stage(feat_fuse))
+        if self.use_extra_pool:
+            results.append(F.max_pool2d(results[-1], kernel_size=1, stride=2, padding=0))
+        return results
+
+
+class SimpleProjector(nn.Module):
+    def __init__(self, in_dim, out_dim, factor_kernel=False):
+        super(SimpleProjector, self).__init__()
+        if not factor_kernel:
+            self.convx1 = ConvX(in_dim, in_dim * 2, layer_norm=True, act="silu")
+            self.convx2 = ConvX(in_dim * 2, out_dim, layer_norm=True, act="silu")
+        else:
+            self.convx1 = ConvX(in_dim, out_dim, kernel=(3, 1), layer_norm=True, act="silu")
+            self.convx2 = ConvX(out_dim, out_dim, kernel=(1, 3), layer_norm=True, act="silu")
+        self.ln = get_norm("LN", out_dim)
+
+    def forward(self, x):
+        """forward"""
+        out = self.ln(self.convx2(self.convx1(x[0])))
+        return [out]
diff --git a/surya/common/rfdetr/models/lwdetr.py b/surya/common/rfdetr/models/lwdetr.py
new file mode 100644
index 0000000..cea4662
--- /dev/null
+++ b/surya/common/rfdetr/models/lwdetr.py
@@ -0,0 +1,481 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Conditional DETR (https://github.com/Atten4Vis/ConditionalDETR)
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+LW-DETR model and criterion classes
+"""
+
+import copy
+import math
+from typing import Callable, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from surya.common.rfdetr.models.backbone import build_backbone
+from surya.common.rfdetr.models.transformer import build_transformer
+from surya.common.rfdetr.util import box_ops
+from surya.common.rfdetr.util.misc import (
+    NestedTensor,
+    nested_tensor_from_tensor_list,
+)
+
+
+class LWDETR(nn.Module):
+    """This is the Group DETR v3 module that performs object detection"""
+
+    def __init__(
+        self,
+        backbone,
+        transformer,
+        segmentation_head,
+        num_classes,
+        num_queries,
+        aux_loss=False,
+        group_detr=1,
+        two_stage=False,
+        lite_refpoint_refine=False,
+        bbox_reparam=False,
+    ):
+        """Initializes the model.
+        Parameters:
+            backbone: torch module of the backbone to be used. See backbone.py
+            transformer: torch module of the transformer architecture. See transformer.py
+            num_classes: number of object classes
+            num_queries: number of object queries, ie detection slot. This is the maximal number of objects
+                         Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
+            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
+            group_detr: Number of groups to speed detr training. Default is 1.
+            lite_refpoint_refine: TODO
+        """
+        super().__init__()
+        self.num_queries = num_queries
+        self.transformer = transformer
+        hidden_dim = transformer.d_model
+        self.class_embed = nn.Linear(hidden_dim, num_classes)
+        self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
+        self.segmentation_head = segmentation_head
+
+        query_dim = 4
+        self.refpoint_embed = nn.Embedding(num_queries * group_detr, query_dim)
+        self.query_feat = nn.Embedding(num_queries * group_detr, hidden_dim)
+        nn.init.constant_(self.refpoint_embed.weight.data, 0)
+
+        self.backbone = backbone
+        self.aux_loss = aux_loss
+        self.group_detr = group_detr
+
+        # iter update
+        self.lite_refpoint_refine = lite_refpoint_refine
+        if not self.lite_refpoint_refine:
+            self.transformer.decoder.bbox_embed = self.bbox_embed
+        else:
+            self.transformer.decoder.bbox_embed = None
+
+        self.bbox_reparam = bbox_reparam
+
+        # init prior_prob setting for focal loss
+        prior_prob = 0.01
+        bias_value = -math.log((1 - prior_prob) / prior_prob)
+        self.class_embed.bias.data = torch.ones(num_classes) * bias_value
+
+        # init bbox_mebed
+        nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)
+        nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)
+
+        # two_stage
+        self.two_stage = two_stage
+        if self.two_stage:
+            self.transformer.enc_out_bbox_embed = nn.ModuleList(
+                [copy.deepcopy(self.bbox_embed) for _ in range(group_detr)]
+            )
+            self.transformer.enc_out_class_embed = nn.ModuleList(
+                [copy.deepcopy(self.class_embed) for _ in range(group_detr)]
+            )
+
+        self._export = False
+
+    def reinitialize_detection_head(self, num_classes):
+        base = self.class_embed.weight.shape[0]
+        num_repeats = int(math.ceil(num_classes / base))
+        self.class_embed.weight.data = self.class_embed.weight.data.repeat(num_repeats, 1)
+        self.class_embed.weight.data = self.class_embed.weight.data[:num_classes]
+        self.class_embed.bias.data = self.class_embed.bias.data.repeat(num_repeats)
+        self.class_embed.bias.data = self.class_embed.bias.data[:num_classes]
+
+        if self.two_stage:
+            for enc_out_class_embed in self.transformer.enc_out_class_embed:
+                enc_out_class_embed.weight.data = enc_out_class_embed.weight.data.repeat(num_repeats, 1)
+                enc_out_class_embed.weight.data = enc_out_class_embed.weight.data[:num_classes]
+                enc_out_class_embed.bias.data = enc_out_class_embed.bias.data.repeat(num_repeats)
+                enc_out_class_embed.bias.data = enc_out_class_embed.bias.data[:num_classes]
+
+    def export(self):
+        self._export = True
+        self._forward_origin = self.forward
+        self.forward = self.forward_export
+        for name, m in self.named_modules():
+            if hasattr(m, "export") and isinstance(m.export, Callable) and hasattr(m, "_export") and not m._export:
+                m.export()
+
+    def forward(self, samples: NestedTensor, targets=None):
+        """The forward expects a NestedTensor, which consists of:
+           - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
+           - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
+
+        It returns a dict with the following elements:
+           - "pred_logits": the classification logits (including no-object) for all queries.
+                            Shape= [batch_size x num_queries x num_classes]
+           - "pred_boxes": The normalized boxes coordinates for all queries, represented as
+                           (center_x, center_y, width, height). These values are normalized in [0, 1],
+                           relative to the size of each individual image (disregarding possible padding).
+                           See PostProcess for information on how to retrieve the unnormalized bounding box.
+           - "aux_outputs": Optional, only returned when auxiliary losses are activated. It is a list of
+                            dictionaries containing the two above keys for each decoder layer.
+        """
+        if isinstance(samples, (list, torch.Tensor)):
+            samples = nested_tensor_from_tensor_list(samples)
+        features, poss = self.backbone(samples)
+
+        srcs = []
+        masks = []
+        for l, feat in enumerate(features):
+            src, mask = feat.decompose()
+            srcs.append(src)
+            masks.append(mask)
+            assert mask is not None
+
+        if self.training:
+            refpoint_embed_weight = self.refpoint_embed.weight
+            query_feat_weight = self.query_feat.weight
+        else:
+            # only use one group in inference
+            refpoint_embed_weight = self.refpoint_embed.weight[: self.num_queries]
+            query_feat_weight = self.query_feat.weight[: self.num_queries]
+
+        if self.segmentation_head is not None:
+            seg_head_fwd = self.segmentation_head.sparse_forward if self.training else self.segmentation_head.forward
+
+        hs, ref_unsigmoid, hs_enc, ref_enc = self.transformer(
+            srcs, masks, poss, refpoint_embed_weight, query_feat_weight
+        )
+
+        if hs is not None:
+            if self.bbox_reparam:
+                outputs_coord_delta = self.bbox_embed(hs)
+                outputs_coord_cxcy = outputs_coord_delta[..., :2] * ref_unsigmoid[..., 2:] + ref_unsigmoid[..., :2]
+                outputs_coord_wh = outputs_coord_delta[..., 2:].exp() * ref_unsigmoid[..., 2:]
+                outputs_coord = torch.concat([outputs_coord_cxcy, outputs_coord_wh], dim=-1)
+            else:
+                outputs_coord = (self.bbox_embed(hs) + ref_unsigmoid).sigmoid()
+
+            outputs_class = self.class_embed(hs)
+
+            if self.segmentation_head is not None:
+                outputs_masks = seg_head_fwd(features[0].tensors, hs, samples.tensors.shape[-2:])
+
+            out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord[-1]}
+            if self.segmentation_head is not None:
+                out["pred_masks"] = outputs_masks[-1]
+            if self.aux_loss:
+                out["aux_outputs"] = self._set_aux_loss(
+                    outputs_class,
+                    outputs_coord,
+                    outputs_masks if self.segmentation_head is not None else None,
+                )
+
+        if self.two_stage:
+            group_detr = self.group_detr if self.training else 1
+            hs_enc_list = hs_enc.chunk(group_detr, dim=1)
+            cls_enc = []
+            for g_idx in range(group_detr):
+                cls_enc_gidx = self.transformer.enc_out_class_embed[g_idx](hs_enc_list[g_idx])
+                cls_enc.append(cls_enc_gidx)
+
+            cls_enc = torch.cat(cls_enc, dim=1)
+
+            if self.segmentation_head is not None:
+                masks_enc = seg_head_fwd(
+                    features[0].tensors,
+                    [
+                        hs_enc,
+                    ],
+                    samples.tensors.shape[-2:],
+                    skip_blocks=True,
+                )[0]
+
+            if hs is not None:
+                out["enc_outputs"] = {"pred_logits": cls_enc, "pred_boxes": ref_enc}
+                if self.segmentation_head is not None:
+                    out["enc_outputs"]["pred_masks"] = masks_enc
+            else:
+                out = {"pred_logits": cls_enc, "pred_boxes": ref_enc}
+                if self.segmentation_head is not None:
+                    out["pred_masks"] = masks_enc
+
+        return out
+
+    def forward_export(self, tensors):
+        srcs, _, poss = self.backbone(tensors)
+        # only use one group in inference
+        refpoint_embed_weight = self.refpoint_embed.weight[: self.num_queries]
+        query_feat_weight = self.query_feat.weight[: self.num_queries]
+
+        hs, ref_unsigmoid, hs_enc, ref_enc = self.transformer(
+            srcs, None, poss, refpoint_embed_weight, query_feat_weight
+        )
+
+        outputs_masks = None
+
+        if hs is not None:
+            if self.bbox_reparam:
+                outputs_coord_delta = self.bbox_embed(hs)
+                outputs_coord_cxcy = outputs_coord_delta[..., :2] * ref_unsigmoid[..., 2:] + ref_unsigmoid[..., :2]
+                outputs_coord_wh = outputs_coord_delta[..., 2:].exp() * ref_unsigmoid[..., 2:]
+                outputs_coord = torch.concat([outputs_coord_cxcy, outputs_coord_wh], dim=-1)
+            else:
+                outputs_coord = (self.bbox_embed(hs) + ref_unsigmoid).sigmoid()
+            outputs_class = self.class_embed(hs)
+            if self.segmentation_head is not None:
+                outputs_masks = self.segmentation_head(
+                    srcs[0],
+                    [
+                        hs,
+                    ],
+                    tensors.shape[-2:],
+                )[0]
+        else:
+            assert self.two_stage, "if not using decoder, two_stage must be True"
+            outputs_class = self.transformer.enc_out_class_embed[0](hs_enc)
+            outputs_coord = ref_enc
+            if self.segmentation_head is not None:
+                outputs_masks = self.segmentation_head(
+                    srcs[0],
+                    [
+                        hs_enc,
+                    ],
+                    tensors.shape[-2:],
+                    skip_blocks=True,
+                )[0]
+
+        if outputs_masks is not None:
+            return outputs_coord, outputs_class, outputs_masks
+        else:
+            return outputs_coord, outputs_class
+
+    @torch.jit.unused
+    def _set_aux_loss(self, outputs_class, outputs_coord, outputs_masks):
+        # this is a workaround to make torchscript happy, as torchscript
+        # doesn't support dictionary with non-homogeneous values, such
+        # as a dict having both a Tensor and a list.
+        if outputs_masks is not None:
+            return [
+                {"pred_logits": a, "pred_boxes": b, "pred_masks": c}
+                for a, b, c in zip(outputs_class[:-1], outputs_coord[:-1], outputs_masks[:-1])
+            ]
+        else:
+            return [{"pred_logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
+
+    def _get_backbone_encoder_layers(self) -> Optional[nn.ModuleList]:
+        """Resolve the list of transformer blocks/layers from backbone[0].encoder.
+
+        Supports multiple backbone architectures:
+        - encoder.blocks (standard ViT)
+        - encoder.trunk.blocks (aimv2)
+        - encoder.encoder.encoder.layer (HuggingFace DinoV2)
+
+        Returns:
+            List of transformer layers, or None if not found.
+        """
+        enc = self.backbone[0].encoder
+        if hasattr(enc, "blocks"):
+            return enc.blocks
+        if hasattr(enc, "trunk") and hasattr(enc.trunk, "blocks"):
+            return enc.trunk.blocks
+        if hasattr(enc, "encoder") and hasattr(enc.encoder, "encoder") and hasattr(enc.encoder.encoder, "layer"):
+            return enc.encoder.encoder.layer
+        return None
+
+    def update_drop_path(self, drop_path_rate: float, vit_encoder_num_layers: int) -> None:
+        """Update drop_path rates for backbone encoder layers with linear schedule.
+
+        Applies a linear schedule where the first layer has drop_path_rate=0 and the last
+        layer has drop_path_rate=drop_path_rate. Intermediate layers are interpolated linearly.
+
+        Args:
+            drop_path_rate: Maximum drop path rate (applied to last layer).
+            vit_encoder_num_layers: Number of encoder layers to update.
+        """
+        layers = self._get_backbone_encoder_layers()
+        if layers is None:
+            return
+        n = min(vit_encoder_num_layers, len(layers))
+        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, n)]
+        for i in range(n):
+            if hasattr(layers[i], "drop_path") and hasattr(layers[i].drop_path, "drop_prob"):
+                layers[i].drop_path.drop_prob = dp_rates[i]
+
+    def update_dropout(self, drop_rate):
+        for module in self.transformer.modules():
+            if isinstance(module, nn.Dropout):
+                module.p = drop_rate
+
+
+class PostProcess(nn.Module):
+    """This module converts the model's output into the format expected by the coco api"""
+
+    def __init__(self, num_select=300) -> None:
+        super().__init__()
+        self.num_select = num_select
+
+    @torch.no_grad()
+    def forward(self, outputs, target_sizes):
+        """Perform the computation
+        Parameters:
+            outputs: raw outputs of the model
+            target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
+                          For evaluation, this must be the original image size (before any data augmentation)
+                          For visualization, this should be the image size after data augment, but before padding
+        """
+        out_logits, out_bbox = outputs["pred_logits"], outputs["pred_boxes"]
+        out_masks = outputs.get("pred_masks", None)
+
+        assert len(out_logits) == len(target_sizes)
+        assert target_sizes.shape[1] == 2
+
+        prob = out_logits.sigmoid()
+        topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), self.num_select, dim=1)
+        scores = topk_values
+        topk_boxes = topk_indexes // out_logits.shape[2]
+        labels = topk_indexes % out_logits.shape[2]
+        boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)
+        boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
+
+        # and from relative [0, 1] to absolute [0, height] coordinates
+        img_h, img_w = target_sizes.unbind(1)
+        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
+        boxes = boxes * scale_fct[:, None, :]
+
+        # Optionally gather masks corresponding to the same top-K queries and resize to original size
+        results = []
+        if out_masks is not None:
+            for i in range(out_masks.shape[0]):
+                res_i = {"scores": scores[i], "labels": labels[i], "boxes": boxes[i]}
+                k_idx = topk_boxes[i]
+                masks_i = torch.gather(
+                    out_masks[i],
+                    0,
+                    k_idx.unsqueeze(-1).unsqueeze(-1).repeat(1, out_masks.shape[-2], out_masks.shape[-1]),
+                )  # [K, Hm, Wm]
+                h, w = target_sizes[i].tolist()
+                masks_i = F.interpolate(
+                    masks_i.unsqueeze(1),
+                    size=(int(h), int(w)),
+                    mode="bilinear",
+                    align_corners=False,
+                )  # [K,1,H,W]
+                res_i["masks"] = masks_i > 0.0
+                results.append(res_i)
+        else:
+            results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
+
+        return results
+
+
+class MLP(nn.Module):
+    """Very simple multi-layer perceptron (also called FFN)"""
+
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+
+def build_model(args):
+    # the `num_classes` naming here is somewhat misleading.
+    # it indeed corresponds to `max_obj_id + 1`, where max_obj_id
+    # is the maximum id for a class in your dataset. For example,
+    # COCO has a max_obj_id of 90, so we pass `num_classes` to be 91.
+    # As another example, for a dataset that has a single class with id 1,
+    # you should pass `num_classes` to be 2 (max_obj_id + 1).
+    # For more details on this, check the following discussion
+    # https://github.com/facebookresearch/detr/issues/108#issuecomment-650269223
+    num_classes = args.num_classes + 1
+    torch.device(args.device)
+
+    backbone = build_backbone(
+        encoder=args.encoder,
+        vit_encoder_num_layers=args.vit_encoder_num_layers,
+        pretrained_encoder=args.pretrained_encoder,
+        window_block_indexes=args.window_block_indexes,
+        drop_path=args.drop_path,
+        out_channels=args.hidden_dim,
+        out_feature_indexes=args.out_feature_indexes,
+        projector_scale=args.projector_scale,
+        use_cls_token=args.use_cls_token,
+        hidden_dim=args.hidden_dim,
+        position_embedding=args.position_embedding,
+        freeze_encoder=args.freeze_encoder,
+        layer_norm=args.layer_norm,
+        target_shape=(
+            args.shape
+            if hasattr(args, "shape")
+            else ((args.resolution, args.resolution) if hasattr(args, "resolution") else (640, 640))
+        ),
+        rms_norm=args.rms_norm,
+        backbone_lora=args.backbone_lora,
+        force_no_pretrain=args.force_no_pretrain,
+        gradient_checkpointing=args.gradient_checkpointing,
+        load_dinov2_weights=args.pretrain_weights is None,
+        patch_size=args.patch_size,
+        num_windows=args.num_windows,
+        positional_encoding_size=args.positional_encoding_size,
+    )
+    if args.encoder_only:
+        return backbone[0].encoder, None, None
+    if args.backbone_only:
+        return backbone, None, None
+
+    args.num_feature_levels = len(args.projector_scale)
+    transformer = build_transformer(args)
+
+    # detection-only vendored copy: segmentation head removed
+    segmentation_head = None
+
+    model = LWDETR(
+        backbone,
+        transformer,
+        segmentation_head,
+        num_classes=num_classes,
+        num_queries=args.num_queries,
+        aux_loss=args.aux_loss,
+        group_detr=args.group_detr,
+        two_stage=args.two_stage,
+        lite_refpoint_refine=args.lite_refpoint_refine,
+        bbox_reparam=args.bbox_reparam,
+    )
+    return model
+
+
diff --git a/surya/common/rfdetr/models/ops/__init__.py b/surya/common/rfdetr/models/ops/__init__.py
new file mode 100644
index 0000000..148e179
--- /dev/null
+++ b/surya/common/rfdetr/models/ops/__init__.py
@@ -0,0 +1,5 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
diff --git a/surya/common/rfdetr/models/ops/functions/__init__.py b/surya/common/rfdetr/models/ops/functions/__init__.py
new file mode 100644
index 0000000..2e7f5c0
--- /dev/null
+++ b/surya/common/rfdetr/models/ops/functions/__init__.py
@@ -0,0 +1,18 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------------------------------
+# Modified from Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+"""
+ms_deform_attn_func
+"""
+
+from surya.common.rfdetr.models.ops.functions.ms_deform_attn_func import ms_deform_attn_core_pytorch
diff --git a/surya/common/rfdetr/models/ops/functions/ms_deform_attn_func.py b/surya/common/rfdetr/models/ops/functions/ms_deform_attn_func.py
new file mode 100644
index 0000000..5143ca5
--- /dev/null
+++ b/surya/common/rfdetr/models/ops/functions/ms_deform_attn_func.py
@@ -0,0 +1,47 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------------------------------
+# Modified from Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+"""
+ms_deform_attn_func
+"""
+
+from __future__ import absolute_import, division, print_function
+
+import torch
+import torch.nn.functional as F
+
+
+def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):
+    """ "for debug and test only, need to use cuda version instead"""
+    # B, n_heads, head_dim, N
+    B, n_heads, head_dim, _ = value.shape
+    _, Len_q, n_heads, L, P, _ = sampling_locations.shape
+    value_list = value.split([H * W for H, W in value_spatial_shapes], dim=3)
+    sampling_grids = 2 * sampling_locations - 1
+    sampling_value_list = []
+    for lid_, (H, W) in enumerate(value_spatial_shapes):
+        # B, n_heads, head_dim, H, W
+        value_l_ = value_list[lid_].view(B * n_heads, head_dim, H, W)
+        # B, Len_q, n_heads, P, 2 -> B, n_heads, Len_q, P, 2 -> B*n_heads, Len_q, P, 2
+        sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)
+        # B*n_heads, head_dim, Len_q, P
+        sampling_value_l_ = F.grid_sample(
+            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+        )
+        sampling_value_list.append(sampling_value_l_)
+    # (B, Len_q, n_heads, L * P) -> (B, n_heads, Len_q, L, P) -> (B*n_heads, 1, Len_q, L*P)
+    attention_weights = attention_weights.transpose(1, 2).reshape(B * n_heads, 1, Len_q, L * P)
+    # B*n_heads, head_dim, Len_q, L*P
+    sampling_value_list = torch.stack(sampling_value_list, dim=-2).flatten(-2)
+    output = (sampling_value_list * attention_weights).sum(-1).view(B, n_heads * head_dim, Len_q)
+    return output.transpose(1, 2).contiguous()
diff --git a/surya/common/rfdetr/models/ops/modules/__init__.py b/surya/common/rfdetr/models/ops/modules/__init__.py
new file mode 100644
index 0000000..dfb92fb
--- /dev/null
+++ b/surya/common/rfdetr/models/ops/modules/__init__.py
@@ -0,0 +1,13 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------
+
+from surya.common.rfdetr.models.ops.modules.ms_deform_attn import MSDeformAttn
diff --git a/surya/common/rfdetr/models/ops/modules/ms_deform_attn.py b/surya/common/rfdetr/models/ops/modules/ms_deform_attn.py
new file mode 100644
index 0000000..8622b63
--- /dev/null
+++ b/surya/common/rfdetr/models/ops/modules/ms_deform_attn.py
@@ -0,0 +1,152 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------------------------------
+# Modified from Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+"""
+Multi-Scale Deformable Attention Module
+"""
+
+from __future__ import absolute_import, division, print_function
+
+import math
+import warnings
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.init import constant_, xavier_uniform_
+
+from surya.common.rfdetr.models.ops.functions import ms_deform_attn_core_pytorch
+
+
+def _is_power_of_2(n):
+    if (not isinstance(n, int)) or (n < 0):
+        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+    return (n & (n - 1) == 0) and n != 0
+
+
+class MSDeformAttn(nn.Module):
+    """Multi-Scale Deformable Attention Module"""
+
+    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
+        """
+        Multi-Scale Deformable Attention Module
+        :param d_model      hidden dimension
+        :param n_levels     number of feature levels
+        :param n_heads      number of attention heads
+        :param n_points     number of sampling points per attention head per feature level
+        """
+        super().__init__()
+        if d_model % n_heads != 0:
+            raise ValueError("d_model must be divisible by n_heads, but got {} and {}".format(d_model, n_heads))
+        _d_per_head = d_model // n_heads
+        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
+        if not _is_power_of_2(_d_per_head):
+            warnings.warn(
+                "You'd better set d_model in MSDeformAttn to make the "
+                "dimension of each attention head a power of 2 "
+                "which is more efficient in our CUDA implementation."
+            )
+
+        self.im2col_step = 64
+
+        self.d_model = d_model
+        self.n_levels = n_levels
+        self.n_heads = n_heads
+        self.n_points = n_points
+
+        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
+        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
+        self.value_proj = nn.Linear(d_model, d_model)
+        self.output_proj = nn.Linear(d_model, d_model)
+
+        self._reset_parameters()
+
+        self._export = False
+
+    def export(self):
+        """export mode"""
+        self._export = True
+
+    def _reset_parameters(self):
+        constant_(self.sampling_offsets.weight.data, 0.0)
+        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
+        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+        grid_init = (
+            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+            .view(self.n_heads, 1, 1, 2)
+            .repeat(1, self.n_levels, self.n_points, 1)
+        )
+        for i in range(self.n_points):
+            grid_init[:, :, i, :] *= i + 1
+        with torch.no_grad():
+            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+        constant_(self.attention_weights.weight.data, 0.0)
+        constant_(self.attention_weights.bias.data, 0.0)
+        xavier_uniform_(self.value_proj.weight.data)
+        constant_(self.value_proj.bias.data, 0.0)
+        xavier_uniform_(self.output_proj.weight.data)
+        constant_(self.output_proj.bias.data, 0.0)
+
+    def forward(
+        self,
+        query,
+        reference_points,
+        input_flatten,
+        input_spatial_shapes,
+        input_level_start_index,
+        input_padding_mask=None,
+    ):
+        r"""
+        :param query                       (N, Length_{query}, C)
+        :param reference_points            (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area
+                                        or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes
+        :param input_flatten               (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C)
+        :param input_spatial_shapes        (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
+        :param input_level_start_index     (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]
+        :param input_padding_mask          (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
+
+        :return output                     (N, Length_{query}, C)
+        """
+        N, Len_q, _ = query.shape
+        N, Len_in, _ = input_flatten.shape
+        assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in
+
+        value = self.value_proj(input_flatten)
+        if input_padding_mask is not None:
+            value = value.masked_fill(input_padding_mask[..., None], float(0))
+
+        sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)
+        attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
+
+        # N, Len_q, n_heads, n_levels, n_points, 2
+        if reference_points.shape[-1] == 2:
+            offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)
+            sampling_locations = (
+                reference_points[:, :, None, :, None, :]
+                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+            )
+        elif reference_points.shape[-1] == 4:
+            sampling_locations = (
+                reference_points[:, :, None, :, None, :2]
+                + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
+            )
+        else:
+            raise ValueError(
+                "Last dim of reference_points must be 2 or 4, but get {} instead.".format(reference_points.shape[-1])
+            )
+        attention_weights = F.softmax(attention_weights, -1)
+
+        value = value.transpose(1, 2).contiguous().view(N, self.n_heads, self.d_model // self.n_heads, Len_in)
+        output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
+        output = self.output_proj(output)
+        return output
diff --git a/surya/common/rfdetr/models/position_encoding.py b/surya/common/rfdetr/models/position_encoding.py
new file mode 100644
index 0000000..dbe1811
--- /dev/null
+++ b/surya/common/rfdetr/models/position_encoding.py
@@ -0,0 +1,155 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Conditional DETR (https://github.com/Atten4Vis/ConditionalDETR)
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Various positional encodings for the transformer.
+"""
+
+import math
+
+import torch
+from torch import nn
+
+from surya.common.rfdetr.util.misc import NestedTensor
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+        self._export = False
+
+    def export(self):
+        self._export = True
+        self._forward_origin = self.forward
+        self.forward = self.forward_export
+
+    def forward(self, tensor_list: NestedTensor, align_dim_orders=True):
+        x = tensor_list.tensors
+        mask = tensor_list.mask
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        if align_dim_orders:
+            pos = torch.cat((pos_y, pos_x), dim=3).permute(1, 2, 0, 3)
+            # return: (H, W, bs, C)
+        else:
+            pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+            # return: (bs, C, H, W)
+        return pos
+
+    def forward_export(self, mask: torch.Tensor, align_dim_orders=True):
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=mask.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        if align_dim_orders:
+            pos = torch.cat((pos_y, pos_x), dim=3).permute(1, 2, 0, 3)
+            # return: (H, W, bs, C)
+        else:
+            pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+            # return: (bs, C, H, W)
+        return pos
+
+
+class PositionEmbeddingLearned(nn.Module):
+    """
+    Absolute pos embedding, learned.
+    """
+
+    def __init__(self, num_pos_feats=256):
+        super().__init__()
+        self.row_embed = nn.Embedding(50, num_pos_feats)
+        self.col_embed = nn.Embedding(50, num_pos_feats)
+        self.reset_parameters()
+        self._export = False
+
+    def export(self):
+        raise NotImplementedError
+
+    def reset_parameters(self):
+        nn.init.uniform_(self.row_embed.weight)
+        nn.init.uniform_(self.col_embed.weight)
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        h, w = x.shape[:2]
+        i = torch.arange(w, device=x.device)
+        j = torch.arange(h, device=x.device)
+        x_emb = self.col_embed(i)
+        y_emb = self.row_embed(j)
+        pos = (
+            torch.cat(
+                [
+                    x_emb.unsqueeze(0).repeat(h, 1, 1),
+                    y_emb.unsqueeze(1).repeat(1, w, 1),
+                ],
+                dim=-1,
+            )
+            .unsqueeze(2)
+            .repeat(1, 1, x.shape[2], 1)
+        )
+        # return: (H, W, bs, C)
+        return pos
+
+
+def build_position_encoding(hidden_dim, position_embedding):
+    N_steps = hidden_dim // 2
+    if position_embedding in ("v2", "sine"):
+        # TODO find a better way of exposing other arguments
+        position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
+    elif position_embedding in ("v3", "learned"):
+        position_embedding = PositionEmbeddingLearned(N_steps)
+    else:
+        raise ValueError(f"not supported {position_embedding}")
+
+    return position_embedding
diff --git a/surya/common/rfdetr/models/transformer.py b/surya/common/rfdetr/models/transformer.py
new file mode 100644
index 0000000..aa4bef4
--- /dev/null
+++ b/surya/common/rfdetr/models/transformer.py
@@ -0,0 +1,659 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Conditional DETR (https://github.com/Atten4Vis/ConditionalDETR)
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+"""
+Transformer class
+"""
+
+import copy
+import math
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+from surya.common.rfdetr.models.ops.modules import MSDeformAttn
+
+
+class MLP(nn.Module):
+    """Very simple multi-layer perceptron (also called FFN)"""
+
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+
+def gen_sineembed_for_position(pos_tensor, dim=128):
+    # n_query, bs, _ = pos_tensor.size()
+    # sineembed_tensor = torch.zeros(n_query, bs, 256)
+    scale = 2 * math.pi
+    dim_t = torch.arange(dim, dtype=pos_tensor.dtype, device=pos_tensor.device)
+    dim_t = 10000 ** (2 * (dim_t // 2) / dim)
+    x_embed = pos_tensor[:, :, 0] * scale
+    y_embed = pos_tensor[:, :, 1] * scale
+    pos_x = x_embed[:, :, None] / dim_t
+    pos_y = y_embed[:, :, None] / dim_t
+    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
+    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
+    if pos_tensor.size(-1) == 2:
+        pos = torch.cat((pos_y, pos_x), dim=2)
+    elif pos_tensor.size(-1) == 4:
+        w_embed = pos_tensor[:, :, 2] * scale
+        pos_w = w_embed[:, :, None] / dim_t
+        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
+
+        h_embed = pos_tensor[:, :, 3] * scale
+        pos_h = h_embed[:, :, None] / dim_t
+        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
+
+        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
+    else:
+        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
+    return pos
+
+
+def gen_encoder_output_proposals(memory, memory_padding_mask, spatial_shapes, unsigmoid=True):
+    r"""
+    Input:
+        - memory: bs, \sum{hw}, d_model
+        - memory_padding_mask: bs, \sum{hw}
+        - spatial_shapes: nlevel, 2
+    Output:
+        - output_memory: bs, \sum{hw}, d_model
+        - output_proposals: bs, \sum{hw}, 4
+    """
+    N_, S_, C_ = memory.shape
+    proposals = []
+    _cur = 0
+    for lvl, (H_, W_) in enumerate(spatial_shapes):
+        if memory_padding_mask is not None:
+            mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)
+            valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
+            valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
+        else:
+            valid_H = torch.tensor([H_ for _ in range(N_)], device=memory.device)
+            valid_W = torch.tensor([W_ for _ in range(N_)], device=memory.device)
+
+        grid_y, grid_x = torch.meshgrid(
+            torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
+            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),
+            indexing="ij",
+        )
+        grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)  # H_, W_, 2
+
+        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
+        grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
+
+        wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
+
+        proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
+        proposals.append(proposal)
+        _cur += H_ * W_
+
+    output_proposals = torch.cat(proposals, 1)
+    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
+
+    if unsigmoid:
+        output_proposals = torch.log(output_proposals / (1 - output_proposals))  # unsigmoid
+        if memory_padding_mask is not None:
+            output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
+        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
+    else:
+        if memory_padding_mask is not None:
+            output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
+        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float(0))
+
+    output_memory = memory
+    if memory_padding_mask is not None:
+        output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
+    output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
+
+    return output_memory.to(memory.dtype), output_proposals.to(memory.dtype)
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        d_model=512,
+        sa_nhead=8,
+        ca_nhead=8,
+        num_queries=300,
+        num_decoder_layers=6,
+        dim_feedforward=2048,
+        dropout=0.0,
+        activation="relu",
+        normalize_before=False,
+        return_intermediate_dec=False,
+        group_detr=1,
+        two_stage=False,
+        num_feature_levels=4,
+        dec_n_points=4,
+        lite_refpoint_refine=False,
+        decoder_norm_type="LN",
+        bbox_reparam=False,
+    ):
+        super().__init__()
+        self.encoder = None
+
+        decoder_layer = TransformerDecoderLayer(
+            d_model,
+            sa_nhead,
+            ca_nhead,
+            dim_feedforward,
+            dropout,
+            activation,
+            normalize_before,
+            group_detr=group_detr,
+            num_feature_levels=num_feature_levels,
+            dec_n_points=dec_n_points,
+            skip_self_attn=False,
+        )
+        assert decoder_norm_type in ["LN", "Identity"]
+        norm = {
+            "LN": lambda channels: nn.LayerNorm(channels),
+            "Identity": lambda channels: nn.Identity(),
+        }
+        decoder_norm = norm[decoder_norm_type](d_model)
+
+        self.decoder = TransformerDecoder(
+            decoder_layer,
+            num_decoder_layers,
+            decoder_norm,
+            return_intermediate=return_intermediate_dec,
+            d_model=d_model,
+            lite_refpoint_refine=lite_refpoint_refine,
+            bbox_reparam=bbox_reparam,
+        )
+
+        self.two_stage = two_stage
+        if two_stage:
+            self.enc_output = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(group_detr)])
+            self.enc_output_norm = nn.ModuleList([nn.LayerNorm(d_model) for _ in range(group_detr)])
+
+        self._reset_parameters()
+
+        self.num_queries = num_queries
+        self.d_model = d_model
+        self.dec_layers = num_decoder_layers
+        self.group_detr = group_detr
+        self.num_feature_levels = num_feature_levels
+        self.bbox_reparam = bbox_reparam
+
+        self._export = False
+
+    def export(self):
+        self._export = True
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        for m in self.modules():
+            if isinstance(m, MSDeformAttn):
+                m._reset_parameters()
+
+    def get_valid_ratio(self, mask):
+        _, H, W = mask.shape
+        valid_H = torch.sum(~mask[:, :, 0], 1)
+        valid_W = torch.sum(~mask[:, 0, :], 1)
+        valid_ratio_h = valid_H.float() / H
+        valid_ratio_w = valid_W.float() / W
+        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+        return valid_ratio
+
+    def forward(self, srcs, masks, pos_embeds, refpoint_embed, query_feat):
+        src_flatten = []
+        mask_flatten = [] if masks is not None else None
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        valid_ratios = [] if masks is not None else None
+        for lvl, (src, pos_embed) in enumerate(zip(srcs, pos_embeds)):
+            bs, c, h, w = src.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+
+            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
+            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
+            lvl_pos_embed_flatten.append(pos_embed)
+            src_flatten.append(src)
+            if masks is not None:
+                mask = masks[lvl].flatten(1)  # bs, hw
+                mask_flatten.append(mask)
+        memory = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
+        if masks is not None:
+            mask_flatten = torch.cat(mask_flatten, 1)  # bs, \sum{hxw}
+            valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
+        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=memory.device)
+        level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
+
+        if self.two_stage:
+            output_memory, output_proposals = gen_encoder_output_proposals(
+                memory, mask_flatten, spatial_shapes, unsigmoid=not self.bbox_reparam
+            )
+            # group detr for first stage
+            refpoint_embed_ts, memory_ts, boxes_ts = [], [], []
+            group_detr = self.group_detr if self.training else 1
+            for g_idx in range(group_detr):
+                output_memory_gidx = self.enc_output_norm[g_idx](self.enc_output[g_idx](output_memory))
+
+                enc_outputs_class_unselected_gidx = self.enc_out_class_embed[g_idx](output_memory_gidx)
+                if self.bbox_reparam:
+                    enc_outputs_coord_delta_gidx = self.enc_out_bbox_embed[g_idx](output_memory_gidx)
+                    enc_outputs_coord_cxcy_gidx = (
+                        enc_outputs_coord_delta_gidx[..., :2] * output_proposals[..., 2:] + output_proposals[..., :2]
+                    )
+                    enc_outputs_coord_wh_gidx = enc_outputs_coord_delta_gidx[..., 2:].exp() * output_proposals[..., 2:]
+                    enc_outputs_coord_unselected_gidx = torch.concat(
+                        [enc_outputs_coord_cxcy_gidx, enc_outputs_coord_wh_gidx], dim=-1
+                    )
+                else:
+                    enc_outputs_coord_unselected_gidx = (
+                        self.enc_out_bbox_embed[g_idx](output_memory_gidx) + output_proposals
+                    )  # (bs, \sum{hw}, 4) unsigmoid
+
+                topk = min(self.num_queries, enc_outputs_class_unselected_gidx.shape[-2])
+                topk_proposals_gidx = torch.topk(enc_outputs_class_unselected_gidx.max(-1)[0], topk, dim=1)[1]  # bs, nq
+
+                refpoint_embed_gidx_undetach = torch.gather(
+                    enc_outputs_coord_unselected_gidx, 1, topk_proposals_gidx.unsqueeze(-1).repeat(1, 1, 4)
+                )  # unsigmoid
+                # for decoder layer, detached as initial ones, (bs, nq, 4)
+                refpoint_embed_gidx = refpoint_embed_gidx_undetach.detach()
+
+                # get memory tgt
+                tgt_undetach_gidx = torch.gather(
+                    output_memory_gidx, 1, topk_proposals_gidx.unsqueeze(-1).repeat(1, 1, self.d_model)
+                )
+
+                refpoint_embed_ts.append(refpoint_embed_gidx)
+                memory_ts.append(tgt_undetach_gidx)
+                boxes_ts.append(refpoint_embed_gidx_undetach)
+            # concat on dim=1, the nq dimension, (bs, nq, d) --> (bs, nq, d)
+            refpoint_embed_ts = torch.cat(refpoint_embed_ts, dim=1)
+            # (bs, nq, d)
+            memory_ts = torch.cat(memory_ts, dim=1)  # .transpose(0, 1)
+            boxes_ts = torch.cat(boxes_ts, dim=1)  # .transpose(0, 1)
+
+        if self.dec_layers > 0:
+            tgt = query_feat.unsqueeze(0).repeat(bs, 1, 1)
+            refpoint_embed = refpoint_embed.unsqueeze(0).repeat(bs, 1, 1)
+            if self.two_stage:
+                ts_len = refpoint_embed_ts.shape[-2]
+                refpoint_embed_ts_subset = refpoint_embed[..., :ts_len, :]
+                refpoint_embed_subset = refpoint_embed[..., ts_len:, :]
+
+                if self.bbox_reparam:
+                    refpoint_embed_cxcy = refpoint_embed_ts_subset[..., :2] * refpoint_embed_ts[..., 2:]
+                    refpoint_embed_cxcy = refpoint_embed_cxcy + refpoint_embed_ts[..., :2]
+                    refpoint_embed_wh = refpoint_embed_ts_subset[..., 2:].exp() * refpoint_embed_ts[..., 2:]
+                    refpoint_embed_ts_subset = torch.concat([refpoint_embed_cxcy, refpoint_embed_wh], dim=-1)
+                else:
+                    refpoint_embed_ts_subset = refpoint_embed_ts_subset + refpoint_embed_ts
+
+                refpoint_embed = torch.concat([refpoint_embed_ts_subset, refpoint_embed_subset], dim=-2)
+
+            hs, references = self.decoder(
+                tgt,
+                memory,
+                memory_key_padding_mask=mask_flatten,
+                pos=lvl_pos_embed_flatten,
+                refpoints_unsigmoid=refpoint_embed,
+                level_start_index=level_start_index,
+                spatial_shapes=spatial_shapes,
+                valid_ratios=valid_ratios.to(memory.dtype) if valid_ratios is not None else valid_ratios,
+            )
+        else:
+            assert self.two_stage, "if not using decoder, two_stage must be True"
+            hs = None
+            references = None
+
+        if self.two_stage:
+            if self.bbox_reparam:
+                return hs, references, memory_ts, boxes_ts
+            else:
+                return hs, references, memory_ts, boxes_ts.sigmoid()
+        return hs, references, None, None
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(
+        self,
+        decoder_layer,
+        num_layers,
+        norm=None,
+        return_intermediate=False,
+        d_model=256,
+        lite_refpoint_refine=False,
+        bbox_reparam=False,
+    ):
+        super().__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.d_model = d_model
+        self.norm = norm
+        self.return_intermediate = return_intermediate
+        self.lite_refpoint_refine = lite_refpoint_refine
+        self.bbox_reparam = bbox_reparam
+
+        self.ref_point_head = MLP(2 * d_model, d_model, d_model, 2)
+
+        self._export = False
+
+    def export(self):
+        self._export = True
+
+    def refpoints_refine(self, refpoints_unsigmoid, new_refpoints_delta):
+        if self.bbox_reparam:
+            new_refpoints_cxcy = (
+                new_refpoints_delta[..., :2] * refpoints_unsigmoid[..., 2:] + refpoints_unsigmoid[..., :2]
+            )
+            new_refpoints_wh = new_refpoints_delta[..., 2:].exp() * refpoints_unsigmoid[..., 2:]
+            new_refpoints_unsigmoid = torch.concat([new_refpoints_cxcy, new_refpoints_wh], dim=-1)
+        else:
+            new_refpoints_unsigmoid = refpoints_unsigmoid + new_refpoints_delta
+        return new_refpoints_unsigmoid
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        refpoints_unsigmoid: Optional[Tensor] = None,
+        # for memory
+        level_start_index: Optional[Tensor] = None,  # num_levels
+        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
+        valid_ratios: Optional[Tensor] = None,
+    ):
+        output = tgt
+
+        intermediate = []
+        hs_refpoints_unsigmoid = [refpoints_unsigmoid]
+
+        def get_reference(refpoints):
+            # [num_queries, batch_size, 4]
+            obj_center = refpoints[..., :4]
+
+            if self._export:
+                query_sine_embed = gen_sineembed_for_position(obj_center, self.d_model / 2)  # bs, nq, 256*2
+                refpoints_input = obj_center[:, :, None]  # bs, nq, 1, 4
+            else:
+                refpoints_input = (
+                    obj_center[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
+                )  # bs, nq, nlevel, 4
+                query_sine_embed = gen_sineembed_for_position(
+                    refpoints_input[:, :, 0, :], self.d_model / 2
+                )  # bs, nq, 256*2
+            query_pos = self.ref_point_head(query_sine_embed)
+            return obj_center, refpoints_input, query_pos, query_sine_embed
+
+        # always use init refpoints
+        if self.lite_refpoint_refine:
+            if self.bbox_reparam:
+                obj_center, refpoints_input, query_pos, query_sine_embed = get_reference(refpoints_unsigmoid)
+            else:
+                obj_center, refpoints_input, query_pos, query_sine_embed = get_reference(refpoints_unsigmoid.sigmoid())
+
+        for layer_id, layer in enumerate(self.layers):
+            # iter refine each layer
+            if not self.lite_refpoint_refine:
+                if self.bbox_reparam:
+                    obj_center, refpoints_input, query_pos, query_sine_embed = get_reference(refpoints_unsigmoid)
+                else:
+                    obj_center, refpoints_input, query_pos, query_sine_embed = get_reference(
+                        refpoints_unsigmoid.sigmoid()
+                    )
+
+            # For the first decoder layer, we do not apply transformation over p_s
+            pos_transformation = 1
+
+            query_pos = query_pos * pos_transformation
+
+            output = layer(
+                output,
+                memory,
+                tgt_mask=tgt_mask,
+                memory_mask=memory_mask,
+                tgt_key_padding_mask=tgt_key_padding_mask,
+                memory_key_padding_mask=memory_key_padding_mask,
+                pos=pos,
+                query_pos=query_pos,
+                query_sine_embed=query_sine_embed,
+                is_first=(layer_id == 0),
+                reference_points=refpoints_input,
+                spatial_shapes=spatial_shapes,
+                level_start_index=level_start_index,
+            )
+
+            if not self.lite_refpoint_refine:
+                # box iterative update
+                new_refpoints_delta = self.bbox_embed(output)
+                new_refpoints_unsigmoid = self.refpoints_refine(refpoints_unsigmoid, new_refpoints_delta)
+                if layer_id != self.num_layers - 1:
+                    hs_refpoints_unsigmoid.append(new_refpoints_unsigmoid)
+                refpoints_unsigmoid = new_refpoints_unsigmoid.detach()
+
+            if self.return_intermediate:
+                intermediate.append(self.norm(output))
+
+        if self.norm is not None:
+            output = self.norm(output)
+            if self.return_intermediate:
+                intermediate.pop()
+                intermediate.append(output)
+
+        if self.return_intermediate:
+            if self._export:
+                # to shape: B, N, C
+                hs = intermediate[-1]
+                if self.bbox_embed is not None:
+                    ref = hs_refpoints_unsigmoid[-1]
+                else:
+                    ref = refpoints_unsigmoid
+                return hs, ref
+            # box iterative update
+            if self.bbox_embed is not None:
+                return [
+                    torch.stack(intermediate),
+                    torch.stack(hs_refpoints_unsigmoid),
+                ]
+            else:
+                return [torch.stack(intermediate), refpoints_unsigmoid.unsqueeze(0)]
+
+        return output.unsqueeze(0)
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        sa_nhead,
+        ca_nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        normalize_before=False,
+        group_detr=1,
+        num_feature_levels=4,
+        dec_n_points=4,
+        skip_self_attn=False,
+    ):
+        super().__init__()
+        # Decoder Self-Attention
+        self.self_attn = nn.MultiheadAttention(embed_dim=d_model, num_heads=sa_nhead, dropout=dropout, batch_first=True)
+        self.dropout1 = nn.Dropout(dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # Decoder Cross-Attention
+        self.cross_attn = MSDeformAttn(d_model, n_levels=num_feature_levels, n_heads=ca_nhead, n_points=dec_n_points)
+
+        self.nhead = ca_nhead
+
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+        self.group_detr = group_detr
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        query_sine_embed=None,
+        is_first=False,
+        reference_points=None,
+        spatial_shapes=None,
+        level_start_index=None,
+    ):
+        bs, num_queries, _ = tgt.shape
+
+        # ========== Begin of Self-Attention =============
+        # Apply projections here
+        # shape: batch_size x num_queries x 256
+        q = k = tgt + query_pos
+        v = tgt
+        if self.training:
+            q = torch.cat(q.split(num_queries // self.group_detr, dim=1), dim=0)
+            k = torch.cat(k.split(num_queries // self.group_detr, dim=1), dim=0)
+            v = torch.cat(v.split(num_queries // self.group_detr, dim=1), dim=0)
+
+        tgt2 = self.self_attn(q, k, v, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask, need_weights=False)[0]
+
+        if self.training:
+            tgt2 = torch.cat(tgt2.split(bs, dim=0), dim=1)
+        # ========== End of Self-Attention =============
+
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # ========== Begin of Cross-Attention =============
+        tgt2 = self.cross_attn(
+            self.with_pos_embed(tgt, query_pos),
+            reference_points,
+            memory,
+            spatial_shapes,
+            level_start_index,
+            memory_key_padding_mask,
+        )
+        # ========== End of Cross-Attention =============
+
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        query_sine_embed=None,
+        is_first=False,
+        reference_points=None,
+        spatial_shapes=None,
+        level_start_index=None,
+    ):
+        return self.forward_post(
+            tgt,
+            memory,
+            tgt_mask,
+            memory_mask,
+            tgt_key_padding_mask,
+            memory_key_padding_mask,
+            pos,
+            query_pos,
+            query_sine_embed,
+            is_first,
+            reference_points,
+            spatial_shapes,
+            level_start_index,
+        )
+
+
+def _get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def build_transformer(args):
+
+    try:
+        two_stage = args.two_stage
+    except:
+        two_stage = False
+
+    return Transformer(
+        d_model=args.hidden_dim,
+        sa_nhead=args.sa_nheads,
+        ca_nhead=args.ca_nheads,
+        num_queries=args.num_queries,
+        dropout=args.dropout,
+        dim_feedforward=args.dim_feedforward,
+        num_decoder_layers=args.dec_layers,
+        return_intermediate_dec=True,
+        group_detr=args.group_detr,
+        two_stage=two_stage,
+        num_feature_levels=args.num_feature_levels,
+        dec_n_points=args.dec_n_points,
+        lite_refpoint_refine=args.lite_refpoint_refine,
+        decoder_norm_type=args.decoder_norm,
+        bbox_reparam=args.bbox_reparam,
+    )
+
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
diff --git a/surya/common/rfdetr/predictor.py b/surya/common/rfdetr/predictor.py
new file mode 100644
index 0000000..87a2743
--- /dev/null
+++ b/surya/common/rfdetr/predictor.py
@@ -0,0 +1,200 @@
+"""Vendored rf-detr (Roboflow RF-DETR) detection inference — no rfdetr package dependency.
+
+Model definition copied (slimmed, detection-only) from the rfdetr package under
+``surya/common/rfdetr/models`` + ``util``. This module is the thin runtime wrapper:
+build the LWDETR architecture, load a fine-tuned checkpoint, and run predict() with the
+same preprocessing/post-processing the rfdetr package uses (ImageNet-normalize, resize to
+the model resolution, sigmoid top-k decode). Pure PyTorch — runs on cpu/mps/cuda.
+"""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import List, Optional
+
+import torch
+import torchvision.transforms.functional as TF
+from PIL import Image
+
+from surya.common.rfdetr.models import PostProcess, build_model
+
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+
+LARGE_ARGS = {   'amp': True,
+    'aug_config': None,
+    'aux_loss': True,
+    'backbone_lora': False,
+    'backbone_only': False,
+    'batch_size': 2,
+    'bbox_loss_coef': 5,
+    'bbox_reparam': True,
+    'ca_nheads': 16,
+    'checkpoint_interval': 10,
+    'clip_max_norm': 0.1,
+    'cls_loss_coef': 1.0,
+    'coco_path': None,
+    'cutoff_epoch': 0,
+    'dataset_dir': None,
+    'dataset_file': 'coco',
+    'dec_layers': 4,
+    'dec_n_points': 2,
+    'decoder_norm': 'LN',
+    'dim_feedforward': 2048,
+    'dist_url': 'env://',
+    'do_benchmark': False,
+    'do_random_resize_via_padding': False,
+    'dont_save_weights': False,
+    'drop_mode': 'standard',
+    'drop_path': 0,
+    'drop_schedule': 'constant',
+    'dropout': 0,
+    'early_stopping': True,
+    'early_stopping_min_delta': 0.001,
+    'early_stopping_patience': 10,
+    'early_stopping_use_ema': False,
+    'ema_decay': 0.9997,
+    'ema_tau': 0,
+    'encoder': 'dinov2_windowed_small',
+    'encoder_only': False,
+    'epochs': 12,
+    'eval': False,
+    'expanded_scales': False,
+    'focal_alpha': 0.25,
+    'force_no_pretrain': False,
+    'fp16_eval': False,
+    'freeze_batch_norm': False,
+    'freeze_encoder': False,
+    'giou_loss_coef': 2,
+    'grad_accum_steps': 1,
+    'gradient_checkpointing': False,
+    'group_detr': 13,
+    'hidden_dim': 256,
+    'ia_bce_loss': True,
+    'layer_norm': True,
+    'license': 'Apache-2.0',
+    'lite_refpoint_refine': True,
+    'lr': 0.0001,
+    'lr_component_decay': 1.0,
+    'lr_drop': 11,
+    'lr_encoder': 0.00015,
+    'lr_min_factor': 0.0,
+    'lr_scheduler': 'step',
+    'lr_vit_layer_decay': 0.8,
+    'mask_downsample_ratio': 4,
+    'multi_scale': False,
+    'num_classes': 90,
+    'num_feature_levels': 1,
+    'num_queries': 300,
+    'num_select': 100,
+    'num_windows': 2,
+    'num_workers': 2,
+    'out_feature_indexes': [3, 6, 9, 12],
+    'output_dir': 'output',
+    'patch_size': 16,
+    'position_embedding': 'sine',
+    'positional_encoding_size': 44,
+    'pretrain_exclude_keys': None,
+    'pretrain_keys_modify_to_load': None,
+    'pretrained_distiller': None,
+    'pretrained_encoder': None,
+    'print_freq': 10,
+    'projector_scale': ['P4'],
+    'resolution': 704,
+    'resume': '',
+    'rms_norm': False,
+    'sa_nheads': 8,
+    'seed': 42,
+    'segmentation_head': False,
+    'set_cost_bbox': 5,
+    'set_cost_class': 2,
+    'set_cost_giou': 2,
+    'square_resize_div_64': False,
+    'start_epoch': 0,
+    'sum_group_losses': False,
+    'sync_bn': True,
+    'two_stage': True,
+    'use_cls_token': False,
+    'use_ema': False,
+    'use_position_supervised_loss': False,
+    'use_varifocal_loss': False,
+    'vit_encoder_num_layers': 12,
+    'warmup_epochs': 1,
+    'weight_decay': 0.0001,
+    'window_block_indexes': None,
+    'world_size': 1}
+
+
+class RFDetrDetector:
+    """Builds RF-DETR-Large and runs detection. ``predict`` returns, per image, a dict with
+    ``boxes`` (xyxy pixels), ``scores``, ``labels`` (0-indexed class ids) as CPU tensors."""
+
+    def __init__(self, weights_path: str, device: str = "cpu",
+                 arch_args: Optional[dict] = None):
+        args = SimpleNamespace(**{**LARGE_ARGS, **(arch_args or {})})
+        args.device = device
+        # Truthy so build_backbone sets load_dinov2_weights=False (no DINOv2 hub download);
+        # the fine-tuned checkpoint is loaded manually below, not by build_model.
+        args.pretrain_weights = weights_path
+        self.device = torch.device(device)
+        self.resolution = int(args.resolution)
+        self.num_select = int(args.num_select)
+
+        model = build_model(args)
+
+        ckpt = torch.load(weights_path, map_location="cpu", weights_only=False)
+        state = ckpt["model"]
+        # Match the checkpoint's class count (head was configured for the default 90).
+        ckpt_num_classes = state["class_embed.bias"].shape[0]
+        if ckpt_num_classes != args.num_classes + 1:
+            model.reinitialize_detection_head(ckpt_num_classes)
+        # Trim group-detr query params to the desired query count (no-op if already matching).
+        num_desired = args.num_queries * args.group_detr
+        for name in list(state.keys()):
+            if name.endswith("refpoint_embed.weight") or name.endswith("query_feat.weight"):
+                state[name] = state[name][:num_desired]
+        model.load_state_dict(state, strict=False)
+
+        self.model = model.eval().to(self.device)
+        self.postprocess = PostProcess(num_select=self.num_select)
+
+        # Optional capture of the encoder feature map (projector output, [B,C,F,F]) so the
+        # reading-order head can cross-attend to it. Hook the LWDETR backbone projector.
+        self._feat = {}
+        for name, mod in self.model.named_modules():
+            if name.endswith("projector"):
+                mod.register_forward_hook(
+                    lambda m, i, o: self._feat.__setitem__(
+                        "f", (o[0] if isinstance(o, (tuple, list)) else o)))
+                break
+
+    @torch.inference_mode()
+    def predict(self, images: List[Image.Image], threshold: float = 0.4,
+                return_features: bool = False) -> List[dict]:
+        if not images:
+            return []
+        tensors, sizes = [], []
+        for img in images:
+            img = img.convert("RGB")
+            sizes.append((img.height, img.width))  # (h, w) for PostProcess scaling
+            t = TF.to_tensor(img).to(self.device)
+            t = TF.normalize(t, IMAGENET_MEAN, IMAGENET_STD)
+            t = TF.resize(t, (self.resolution, self.resolution))
+            tensors.append(t)
+        outputs = self.model(torch.stack(tensors, 0))
+        feats = self._feat.get("f") if return_features else None  # [B,C,F,F]
+        target_sizes = torch.tensor(sizes, device=self.device)
+        results = self.postprocess(outputs, target_sizes=target_sizes)
+
+        out = []
+        for i, res in enumerate(results):
+            keep = res["scores"] > threshold
+            d = {
+                "boxes": res["boxes"][keep].cpu(),
+                "scores": res["scores"][keep].cpu(),
+                "labels": res["labels"][keep].cpu(),
+            }
+            if return_features and feats is not None:
+                d["features"] = feats[i].cpu()  # [C,F,F] for this page
+            out.append(d)
+        return out
diff --git a/surya/common/rfdetr/util/__init__.py b/surya/common/rfdetr/util/__init__.py
new file mode 100644
index 0000000..ad6e160
--- /dev/null
+++ b/surya/common/rfdetr/util/__init__.py
@@ -0,0 +1,16 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
diff --git a/surya/common/rfdetr/util/box_ops.py b/surya/common/rfdetr/util/box_ops.py
new file mode 100644
index 0000000..c2227fe
--- /dev/null
+++ b/surya/common/rfdetr/util/box_ops.py
@@ -0,0 +1,161 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Utilities for bounding box manipulation and GIoU.
+"""
+
+from typing import Tuple
+
+import torch
+import torch.nn.functional as F
+from torchvision.ops.boxes import box_area
+
+
+def box_cxcywh_to_xyxy(x: torch.Tensor) -> torch.Tensor:
+    x_c, y_c, w, h = x.unbind(-1)
+    b = [
+        (x_c - 0.5 * w.clamp(min=0.0)),
+        (y_c - 0.5 * h.clamp(min=0.0)),
+        (x_c + 0.5 * w.clamp(min=0.0)),
+        (y_c + 0.5 * h.clamp(min=0.0)),
+    ]
+    return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_cxcywh(x: torch.Tensor) -> torch.Tensor:
+    x0, y0, x1, y1 = x.unbind(-1)
+    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
+    return torch.stack(b, dim=-1)
+
+
+# modified from torchvision to also return the union
+def box_iou(boxes1: torch.Tensor, boxes2: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Returns:
+        iou: the NxM matrix containing the pairwise
+            IoU values for every element in boxes1 and boxes2
+        union: the NxM matrix containing the pairwise
+            union values for every element in boxes1 and boxes2
+    """
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+
+    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
+    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+
+    union = area1[:, None] + area2 - inter
+
+    iou = inter / union
+    return iou, union
+
+
+def generalized_box_iou(boxes1: torch.Tensor, boxes2: torch.Tensor) -> torch.Tensor:
+    """
+    Generalized IoU from https://giou.stanford.edu/
+
+    The boxes should be in [x0, y0, x1, y1] format
+
+    Returns a [N, M] pairwise matrix, where N = len(boxes1)
+    and M = len(boxes2)
+    """
+    # degenerate boxes gives inf / nan results
+    # so do an early check
+    iou, union = box_iou(boxes1, boxes2)
+
+    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
+    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    area = wh[:, :, 0] * wh[:, :, 1]
+
+    return iou - (area - union) / area
+
+
+def masks_to_boxes(masks: torch.Tensor) -> torch.Tensor:
+    """Compute the bounding boxes around the provided masks
+
+    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
+
+    Returns a [N, 4] tensors, with the boxes in xyxy format
+    """
+    if masks.numel() == 0:
+        return torch.zeros((0, 4), device=masks.device)
+
+    h, w = masks.shape[-2:]
+
+    y = torch.arange(0, h, dtype=torch.float)
+    x = torch.arange(0, w, dtype=torch.float)
+    y, x = torch.meshgrid(y, x, indexing="ij")
+
+    x_mask = masks * x.unsqueeze(0)
+    x_max = x_mask.flatten(1).max(-1)[0]
+    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    y_mask = masks * y.unsqueeze(0)
+    y_max = y_mask.flatten(1).max(-1)[0]
+    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    return torch.stack([x_min, y_min, x_max, y_max], 1)
+
+
+def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
+    """
+    Compute the DICE loss, similar to generalized IOU for masks
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    """
+    inputs = inputs.sigmoid()
+    inputs = inputs.flatten(1)
+    numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets)
+    denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]
+    loss = 1 - (numerator + 1) / (denominator + 1)
+    return loss
+
+
+batch_dice_loss_jit = torch.jit.script(batch_dice_loss)  # type: torch.jit.ScriptModule
+
+
+def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
+    """
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    Returns:
+        Loss tensor
+    """
+    hw = inputs.shape[1]
+
+    pos = F.binary_cross_entropy_with_logits(inputs, torch.ones_like(inputs), reduction="none")
+    neg = F.binary_cross_entropy_with_logits(inputs, torch.zeros_like(inputs), reduction="none")
+
+    loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum("nc,mc->nm", neg, (1 - targets))
+
+    return loss / hw
+
+
+batch_sigmoid_ce_loss_jit = torch.jit.script(batch_sigmoid_ce_loss)  # type: torch.jit.ScriptModule
diff --git a/surya/common/rfdetr/util/logger.py b/surya/common/rfdetr/util/logger.py
new file mode 100644
index 0000000..42e1196
--- /dev/null
+++ b/surya/common/rfdetr/util/logger.py
@@ -0,0 +1,55 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import logging
+import os
+import sys
+from typing import Optional
+
+
+def get_logger(name: str = "rf-detr", level: Optional[int] = None) -> logging.Logger:
+    """Creates and configures a logger with stdout and stderr handlers.
+
+    This function creates a logger that sends INFO and DEBUG level logs to stdout,
+    and WARNING, ERROR, and CRITICAL level logs to stderr. If the logger already
+    has handlers, it returns the existing logger without adding new handlers.
+
+    The log level can be specified directly or through the LOG_LEVEL environment
+    variable.
+
+    Args:
+        name: The name of the logger. Defaults to "rf-detr".
+        level: The logging level to set. If None, uses the LOG_LEVEL environment
+            variable, defaulting to INFO if not set.
+
+    Returns:
+        A configured logging.Logger instance.
+    """
+    if level is None:
+        level = getattr(logging, os.getenv("LOG_LEVEL", "INFO").upper(), logging.INFO)
+
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+
+    if not logger.handlers:
+        formatter = logging.Formatter(
+            "[%(asctime)s] [%(levelname)s] %(name)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
+        )
+
+        stdout_handler = logging.StreamHandler(sys.stdout)
+        stdout_handler.setLevel(logging.DEBUG)
+        stdout_handler.addFilter(lambda r: r.levelno <= logging.INFO)
+        stdout_handler.setFormatter(formatter)
+
+        stderr_handler = logging.StreamHandler(sys.stderr)
+        stderr_handler.setLevel(logging.WARNING)
+        stderr_handler.setFormatter(formatter)
+
+        logger.addHandler(stdout_handler)
+        logger.addHandler(stderr_handler)
+        logger.propagate = False
+
+    return logger
diff --git a/surya/common/rfdetr/util/misc.py b/surya/common/rfdetr/util/misc.py
new file mode 100644
index 0000000..2ef40e9
--- /dev/null
+++ b/surya/common/rfdetr/util/misc.py
@@ -0,0 +1,531 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied and modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
+# Copyright (c) 2024 Baidu. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Misc functions, including distributed helpers.
+
+Mostly copy-paste from torchvision references.
+"""
+
+import datetime
+import os
+import pickle
+import subprocess
+import tempfile
+import time
+from collections import defaultdict, deque
+from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple
+
+import torch
+import torch.distributed as dist
+
+# needed due to empty tensor bug in pytorch and torchvision 0.5
+import torchvision
+from torch import Tensor
+
+from surya.common.rfdetr.util.logger import get_logger
+
+logger = get_logger()
+
+if float(torchvision.__version__.split(".")[1]) < 7.0:
+    from torchvision.ops import _new_empty_tensor
+    from torchvision.ops.misc import _output_size
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size: int = 20, fmt: Optional[str] = None) -> None:
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value: float, n: int = 1) -> None:
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self) -> None:
+        """
+        Warning: does not synchronize the deque!
+        """
+        if not is_dist_avail_and_initialized():
+            return
+        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
+        dist.barrier()
+        dist.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self) -> float:
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self) -> float:
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self) -> float:
+        return self.total / self.count
+
+    @property
+    def max(self) -> float:
+        return max(self.deque)
+
+    @property
+    def value(self) -> float:
+        return self.deque[-1]
+
+    def __str__(self) -> str:
+        return self.fmt.format(
+            median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value
+        )
+
+
+def all_gather(data: Any) -> List[Any]:
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list of data gathered from each rank
+    """
+    world_size = get_world_size()
+    if world_size == 1:
+        return [data]
+
+    # serialized to a Tensor
+    buffer = pickle.dumps(data)
+    storage = torch.ByteStorage.from_buffer(buffer)
+    tensor = torch.ByteTensor(storage).to("cuda")
+
+    # obtain Tensor size of each rank
+    local_size = torch.tensor([tensor.numel()], device="cuda")
+    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
+    dist.all_gather(size_list, local_size)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
+    if local_size != max_size:
+        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
+        tensor = torch.cat((tensor, padding), dim=0)
+    dist.all_gather(tensor_list, tensor)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        buffer = tensor.cpu().numpy().tobytes()[:size]
+        data_list.append(pickle.loads(buffer))
+
+    return data_list
+
+
+def reduce_dict(input_dict: Dict[str, torch.Tensor], average: bool = True) -> Dict[str, torch.Tensor]:
+    """
+    Args:
+        input_dict (dict): all the values will be reduced
+        average (bool): whether to do average or sum
+    Reduce the values in the dictionary from all processes so that all processes
+    have the averaged results. Returns a dict with the same fields as
+    input_dict, after reduction.
+    """
+    world_size = get_world_size()
+    if world_size < 2:
+        return input_dict
+    with torch.no_grad():
+        names = []
+        values = []
+        # sort the keys so that they are consistent across processes
+        for k in sorted(input_dict.keys()):
+            names.append(k)
+            values.append(input_dict[k])
+        values = torch.stack(values, dim=0)
+        dist.all_reduce(values)
+        if average:
+            values /= world_size
+        reduced_dict = {k: v for k, v in zip(names, values)}
+    return reduced_dict
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter: str = "\t", wandb_logging: bool = False) -> None:
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+        if wandb_logging:
+            import wandb
+
+            self.wandb = wandb
+        else:
+            self.wandb = None
+
+    def update(self, **kwargs: Any) -> None:
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr: str) -> SmoothedValue:
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
+
+    def __str__(self) -> str:
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append("{}: {}".format(name, str(meter)))
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self) -> None:
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name: str, meter: SmoothedValue) -> None:
+        self.meters[name] = meter
+
+    def log_every(
+        self, iterable: Iterable[Any], print_freq: int, header: Optional[str] = None
+    ) -> Generator[Any, None, None]:
+        i = 0
+        if not header:
+            header = ""
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt="{avg:.4f}")
+        data_time = SmoothedValue(fmt="{avg:.4f}")
+        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
+        if torch.cuda.is_available():
+            log_msg = self.delimiter.join(
+                [
+                    header,
+                    "[{0" + space_fmt + "}/{1}]",
+                    "eta: {eta}",
+                    "{meters}",
+                    "time: {time}",
+                    "data: {data}",
+                    "max mem: {memory:.0f}",
+                ]
+            )
+        else:
+            log_msg = self.delimiter.join(
+                [header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}"]
+            )
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == len(iterable) - 1:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if self.wandb:
+                    if is_main_process():
+                        log_dict = {k: v.value for k, v in self.meters.items()}
+                        self.wandb.log(log_dict)
+                if torch.cuda.is_available():
+                    logger.info(
+                        log_msg.format(
+                            i,
+                            len(iterable),
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                            memory=torch.cuda.max_memory_allocated() / MB,
+                        )
+                    )
+                else:
+                    logger.info(
+                        log_msg.format(
+                            i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time)
+                        )
+                    )
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        logger.info("{} Total time: {} ({:.4f} s / it)".format(header, total_time_str, total_time / len(iterable)))
+
+
+def get_sha() -> str:
+    """Return a short status string for the current git repo, or 'unknown' if unavailable."""
+    cwd = os.path.dirname(os.path.abspath(__file__))
+
+    def _run(command: List[str]) -> str:
+        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
+
+    try:
+        sha = _run(["git", "rev-parse", "HEAD"])
+        has_diff = bool(_run(["git", "diff-index", "HEAD"]))
+        status = "has uncommitted changes" if has_diff else "clean"
+        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
+        return f"sha: {sha}, status: {status}, branch: {branch}"
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return "unknown"
+
+
+def collate_fn(batch: List[Tuple[Any, ...]]) -> Tuple[Any, ...]:
+    batch = list(zip(*batch))
+    batch[0] = nested_tensor_from_tensor_list(batch[0])
+    return tuple(batch)
+
+
+def _max_by_axis(the_list: List[List[int]]) -> List[int]:
+    maxes = the_list[0]
+    for sublist in the_list[1:]:
+        for index, item in enumerate(sublist):
+            maxes[index] = max(maxes[index], item)
+    return maxes
+
+
+class NestedTensor(object):
+    def __init__(self, tensors: Tensor, mask: Optional[Tensor]) -> None:
+        self.tensors = tensors
+        self.mask = mask
+
+    def to(self, device: torch.device) -> "NestedTensor":
+        cast_tensor = self.tensors.to(device)
+        mask = self.mask
+        if mask is not None:
+            assert mask is not None
+            cast_mask = mask.to(device)
+        else:
+            cast_mask = None
+        return NestedTensor(cast_tensor, cast_mask)
+
+    def decompose(self) -> Tuple[Tensor, Optional[Tensor]]:
+        return self.tensors, self.mask
+
+    def __repr__(self) -> str:
+        return str(self.tensors)
+
+
+def nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
+    # TODO make this more general
+    if tensor_list[0].ndim == 3:
+        if torchvision._is_tracing():
+            # nested_tensor_from_tensor_list() does not export well to ONNX
+            # call _onnx_nested_tensor_from_tensor_list() instead
+            return _onnx_nested_tensor_from_tensor_list(tensor_list)
+
+        # TODO make it support different-sized images
+        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
+        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
+        batch_shape = [len(tensor_list)] + max_size
+        b, c, h, w = batch_shape
+        dtype = tensor_list[0].dtype
+        device = tensor_list[0].device
+        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
+        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
+        for img, pad_img, m in zip(tensor_list, tensor, mask):
+            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+            m[: img.shape[1], : img.shape[2]] = False
+    else:
+        raise ValueError("not supported")
+    return NestedTensor(tensor, mask)
+
+
+# _onnx_nested_tensor_from_tensor_list() is an implementation of
+# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
+@torch.jit.unused
+def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
+    max_size = []
+    for i in range(tensor_list[0].dim()):
+        max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64)
+        max_size.append(max_size_i)
+    max_size = tuple(max_size)
+
+    # work around for
+    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+    # m[: img.shape[1], :img.shape[2]] = False
+    # which is not yet supported in onnx
+    padded_imgs = []
+    padded_masks = []
+    for img in tensor_list:
+        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
+        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
+        padded_imgs.append(padded_img)
+
+        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
+        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
+        padded_masks.append(padded_mask.to(torch.bool))
+
+    tensor = torch.stack(padded_imgs)
+    mask = torch.stack(padded_masks)
+
+    return NestedTensor(tensor, mask=mask)
+
+
+def setup_for_distributed(is_master: bool) -> None:
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+    import logging
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs) -> None:
+        force = kwargs.pop("force", False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+    if not is_master:
+        logging.getLogger("rf-detr").setLevel(logging.ERROR)
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(obj, f, *args, **kwargs):
+    """
+    Safely save objects, removing any callbacks that can't be pickled
+    """
+    if is_main_process():
+        torch.save(obj, f, *args, **kwargs)
+
+
+def init_distributed_mode(args: Any) -> None:
+    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ["WORLD_SIZE"])
+        args.gpu = int(os.environ["LOCAL_RANK"])
+    elif "SLURM_PROCID" in os.environ:
+        args.rank = int(os.environ["SLURM_PROCID"])
+        args.gpu = args.rank % torch.cuda.device_count()
+    else:
+        logger.info("Not using distributed mode")
+        args.distributed = False
+        return
+
+    args.distributed = True
+
+    torch.cuda.set_device(args.gpu)
+    args.dist_backend = "nccl"
+    logger.info("| distributed init (rank {}): {}".format(args.rank, args.dist_url))
+    torch.distributed.init_process_group(
+        backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
+    )
+    torch.distributed.barrier()
+    setup_for_distributed(args.rank == 0)
+
+
+@torch.no_grad()
+def accuracy(output: torch.Tensor, target: torch.Tensor, topk: Tuple[int, ...] = (1,)) -> List[torch.Tensor]:
+    """Computes the precision@k for the specified values of k"""
+    if target.numel() == 0:
+        return [torch.zeros([], device=output.device)]
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(100.0 / batch_size))
+    return res
+
+
+def interpolate(
+    input: Tensor,
+    size: Optional[List[int]] = None,
+    scale_factor: Optional[float] = None,
+    mode: str = "nearest",
+    align_corners: Optional[bool] = None,
+) -> Tensor:
+    """
+    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
+    This will eventually be supported natively by PyTorch, and this
+    class can go away.
+    """
+    if float(torchvision.__version__.split(".")[1]) < 7.0:
+        if input.numel() > 0:
+            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
+
+        output_shape = _output_size(2, input, size, scale_factor)
+        output_shape = list(input.shape[:-2]) + list(output_shape)
+        return _new_empty_tensor(input, output_shape)
+    else:
+        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
+
+
+def inverse_sigmoid(x: torch.Tensor, eps: float = 1e-5) -> torch.Tensor:
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+def strip_checkpoint(checkpoint: str | os.PathLike[str]) -> None:
+    state_dict = torch.load(checkpoint, map_location="cpu", weights_only=False)
+    new_state_dict = {
+        "model": state_dict["model"],
+        "args": state_dict["args"],
+    }
+    # Create the temp file in the destination directory so os.replace stays on the same filesystem (atomic).
+    checkpoint_dir = os.path.dirname(os.path.abspath(os.fspath(checkpoint)))
+    with tempfile.NamedTemporaryFile(dir=checkpoint_dir, delete=False) as tmp_file:
+        tmp_path = tmp_file.name
+    try:
+        torch.save(new_state_dict, tmp_path)
+        # Atomic replace avoids leaving a partially written checkpoint on save failures/interruption.
+        os.replace(tmp_path, checkpoint)
+    finally:
+        if os.path.exists(tmp_path):
+            os.remove(tmp_path)
diff --git a/surya/common/rfdetr_torch.py b/surya/common/rfdetr_torch.py
new file mode 100644
index 0000000..d14c0c9
--- /dev/null
+++ b/surya/common/rfdetr_torch.py
@@ -0,0 +1,203 @@
+"""RfDetrTorch — rf-detr (Roboflow) detector via the vendored model copy (no rfdetr package).
+
+Backs ``fast_layout``. Inference goes through the slimmed, detection-only model
+definition vendored under ``surya.common.rfdetr`` (validated byte-for-byte against the
+upstream rfdetr package). Pure PyTorch — runs on cpu/mps/cuda.
+
+Model dir layout (downloaded from the Hub):
+  rfdetr_<task>.pth   the fine-tuned rf-detr weights
+  config.json         {"arch": "rf-detr-large", "categories": [{"id", "name"}, ...], ...}
+"""
+
+from __future__ import annotations
+
+import glob
+import json
+import os
+from typing import List, Optional
+
+from PIL import Image
+
+
+class _DetList(list):
+    """A list of detections that can also carry the page's encoder feature map (.features)."""
+
+    features = None
+
+
+_MPS_OP_OK: Optional[bool] = None
+
+
+def _mps_op_supported() -> bool:
+    """Probe (once) whether the DINOv2 pos-embed op the rf-detr backbone needs runs on
+    MPS. It uses antialiased bicubic (aten::_upsample_bicubic2d_aa), which has no MPS
+    kernel — it only works if PYTORCH_ENABLE_MPS_FALLBACK=1 was set before `import torch`
+    (surya/__init__.py sets it, but that's too late if torch was already imported).
+    Probing at runtime is the only reliable signal, since the env var reads "1" even when
+    torch ignored it."""
+    global _MPS_OP_OK
+    if _MPS_OP_OK is None:
+        import warnings
+
+        import torch
+
+        try:
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                torch.nn.functional.interpolate(
+                    torch.zeros(1, 1, 4, 4, device="mps"),
+                    size=(6, 6),
+                    mode="bicubic",
+                    antialias=True,
+                )
+            _MPS_OP_OK = True
+        except Exception:
+            _MPS_OP_OK = False
+    return _MPS_OP_OK
+
+
+def _pick_device(device: Optional[str]) -> str:
+    import torch
+
+    if device:
+        return device
+    if torch.cuda.is_available():
+        return "cuda"
+    # Use MPS only if the CPU-fallback for the unsupported bicubic op is actually live
+    # (see _mps_op_supported); otherwise auto-select CPU (rf-detr is ~0.3s/page on CPU)
+    # so Apple Silicon never crashes regardless of import order.
+    if torch.backends.mps.is_available() and _mps_op_supported():
+        return "mps"
+    return "cpu"
+
+
+class RfDetrTorch:
+    def __init__(
+        self,
+        model_dir: str,
+        num_threads: Optional[int] = None,
+        device: Optional[str] = None,
+    ):
+        import torch
+
+        if num_threads:
+            torch.set_num_threads(int(num_threads))
+
+        self.device = _pick_device(device)
+        if self.device == "mps" and not _mps_op_supported():
+            # Only reached when the user explicitly forces mps (auto-select already
+            # probes and drops to CPU — see _pick_device). The DINOv2 pos-embed op has
+            # no MPS kernel and will raise unless PYTORCH_ENABLE_MPS_FALLBACK=1 was set
+            # BEFORE torch was imported (surya/__init__.py sets it, but that's too late
+            # if torch was already loaded).
+            from surya.logging import get_logger
+
+            get_logger().warning(
+                "FAST_DETECTOR_DEVICE=mps but the MPS bicubic fallback isn't active "
+                "(torch was likely imported before surya); the rf-detr detector will "
+                "crash. Export PYTORCH_ENABLE_MPS_FALLBACK=1 before importing torch, or use cpu/cuda."
+            )
+
+        with open(os.path.join(model_dir, "config.json")) as f:
+            cfg = json.load(f)
+
+        # rf-detr's predict() returns 0-indexed class ids that line up with the COCO
+        # categories sorted by id (Row=0, Col=1; layout: Caption=0 ... Text=15).
+        cats = sorted(cfg["categories"], key=lambda c: c["id"])
+        self.id2label = {i: c["name"] for i, c in enumerate(cats)}
+
+        weights = cfg.get("weights")
+        weights = os.path.join(model_dir, weights) if weights else None
+        if not weights or not os.path.exists(weights):
+            pths = sorted(glob.glob(os.path.join(model_dir, "*.pth")))
+            if not pths:
+                raise FileNotFoundError(f"no rf-detr .pth weights found in {model_dir}")
+            weights = pths[0]
+
+        arch = (cfg.get("arch") or "rf-detr-large").lower()
+        if "base" in arch:
+            raise ValueError(
+                "vendored rf-detr copy is rf-detr-large only; got arch=%r" % arch
+            )
+        from surya.common.rfdetr import RFDetrDetector
+
+        # Honor resolution / PE overrides from config.json so reduced-resolution
+        # fine-tunes (e.g. the 448 layout model) run at their trained size; absent
+        # these keys the predictor falls back to LARGE_ARGS (704), so older configs
+        # are unaffected.
+        arch_args = {}
+        if cfg.get("resolution"):
+            arch_args["resolution"] = int(cfg["resolution"])
+        if cfg.get("positional_encoding_size"):
+            arch_args["positional_encoding_size"] = int(cfg["positional_encoding_size"])
+        self.model = RFDetrDetector(
+            weights_path=weights, device=self.device, arch_args=arch_args or None
+        )
+
+    def detect(
+        self,
+        images: List[Image.Image],
+        threshold: float = 0.4,
+        batch_size: int = 8,
+        return_features: bool = False,
+    ) -> List[List[dict]]:
+        """Returns, per image, a list of {label, label_id, score, bbox:[x0,y0,x1,y1] pixels}.
+        When return_features=True, each per-image list carries the encoder feature map on a
+        ``.features`` attribute ([C,F,F] tensor) for the reading-order head."""
+        out: List = []
+        for s in range(0, len(images), batch_size):
+            chunk = [im.convert("RGB") for im in images[s : s + batch_size]]
+            for det in self.model.predict(
+                chunk, threshold=threshold, return_features=return_features
+            ):
+                boxes, scores, labels = det["boxes"], det["scores"], det["labels"]
+                dets: List[dict] = _DetList()
+                for i in range(len(scores)):
+                    cid = int(labels[i])
+                    x0, y0, x1, y1 = (float(v) for v in boxes[i].tolist())
+                    dets.append(
+                        {
+                            "label": self.id2label.get(cid, str(cid)),
+                            "label_id": cid,
+                            "score": float(scores[i]),
+                            "bbox": [x0, y0, x1, y1],
+                        }
+                    )
+                if return_features:
+                    dets.features = det.get("features")
+                out.append(dets)
+        return out
+
+
+def load_detector(
+    model_dir: str, num_threads: Optional[int] = None, device: Optional[str] = None
+):
+    """Build the rf-detr torch detector from a model dir containing ``.pth`` weights
+    + ``config.json``. On CPU the torch rf-detr runs ~0.3s/page, so it remains the
+    fast-mode path."""
+    return RfDetrTorch(model_dir, num_threads=num_threads, device=device)
+
+
+def resolve_model_dir(checkpoint: str) -> str:
+    """Resolve a fast-model checkpoint to a local dir. Supports a plain local path, an
+    ``hf://<repo>/<subfolder>`` ref (downloaded from the Hub), or an ``s3://`` path."""
+    if checkpoint and checkpoint.startswith("hf://"):
+        from huggingface_hub import snapshot_download
+
+        parts = checkpoint[len("hf://") :].split("/")
+        repo_id = "/".join(parts[:2])
+        subfolder = "/".join(parts[2:])
+        local = snapshot_download(
+            repo_id,
+            allow_patterns=[f"{subfolder}/*"] if subfolder else None,
+        )
+        return os.path.join(local, subfolder) if subfolder else local
+    if checkpoint and os.path.isdir(checkpoint):
+        return checkpoint
+    if checkpoint and checkpoint.startswith("s3://"):
+        from surya.common.s3 import download_directory  # type: ignore
+
+        return download_directory(checkpoint)
+    raise FileNotFoundError(
+        f"fast-model checkpoint not found as a local dir: {checkpoint!r}"
+    )
diff --git a/surya/common/s3.py b/surya/common/s3.py
new file mode 100644
index 0000000..98b42de
--- /dev/null
+++ b/surya/common/s3.py
@@ -0,0 +1,182 @@
+import json
+import os
+import shutil
+import tempfile
+import time
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+import requests
+from tqdm import tqdm
+
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+# Lock file expiration time in seconds (10 minutes)
+LOCK_EXPIRATION = 600
+
+
+def join_urls(url1: str, url2: str):
+    url1 = url1.rstrip("/")
+    url2 = url2.lstrip("/")
+    return f"{url1}/{url2}"
+
+
+def get_model_name(pretrained_model_name_or_path: str):
+    return pretrained_model_name_or_path.split("/")[0]
+
+
+def download_file(remote_path: str, local_path: str, chunk_size: int = 1024 * 1024):
+    local_path = Path(local_path)
+    try:
+        response = requests.get(remote_path, stream=True, allow_redirects=True)
+        response.raise_for_status()  # Raise an exception for bad status codes
+
+        # Get file size from headers for progress bar
+        total_size = int(response.headers.get('content-length', 0))
+        
+        # Create progress bar with file name and size info
+        filename = local_path.name
+        pbar = tqdm(
+            total=total_size,
+            unit='B',
+            unit_scale=True,
+            unit_divisor=1024,
+            desc=f"Downloading {filename}",
+            miniters=1
+        )
+
+        with open(local_path, "wb") as f:
+            downloaded = 0
+            for chunk in response.iter_content(chunk_size=chunk_size):
+                if chunk:
+                    f.write(chunk)
+                    downloaded += len(chunk)
+                    pbar.update(len(chunk))
+        
+        pbar.close()
+        return local_path
+    except Exception as e:
+        if local_path.exists():
+            local_path.unlink()
+        logger.error(f"Download error for file {remote_path}: {str(e)}")
+        raise
+
+
+def check_manifest(local_dir: str):
+    local_dir = Path(local_dir)
+    manifest_path = local_dir / "manifest.json"
+    if not os.path.exists(manifest_path):
+        return False
+
+    try:
+        with open(manifest_path, "r") as f:
+            manifest = json.load(f)
+        for file in manifest["files"]:
+            if not os.path.exists(local_dir / file):
+                return False
+    except Exception:
+        return False
+
+    return True
+
+
+def download_directory(remote_path: str, local_dir: str):
+    model_name = get_model_name(remote_path)
+    s3_url = join_urls(settings.S3_BASE_URL, remote_path)
+    # Check to see if it's already downloaded
+    model_exists = check_manifest(local_dir)
+    if model_exists:
+        return
+
+    # Use tempfile.TemporaryDirectory to automatically clean up
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Download the manifest file
+        manifest_file = join_urls(s3_url, "manifest.json")
+        manifest_path = os.path.join(temp_dir, "manifest.json")
+        download_file(manifest_file, manifest_path)
+
+        # List and download all files
+        with open(manifest_path, "r") as f:
+            manifest = json.load(f)
+
+        pbar = tqdm(
+            desc=f"Downloading {model_name} model to {local_dir}",
+            total=len(manifest["files"]),
+        )
+
+        with ThreadPoolExecutor(
+            max_workers=settings.PARALLEL_DOWNLOAD_WORKERS
+        ) as executor:
+            futures = []
+            for file in manifest["files"]:
+                remote_file = join_urls(s3_url, file)
+                local_file = os.path.join(temp_dir, file)
+                futures.append(executor.submit(download_file, remote_file, local_file))
+
+            for future in futures:
+                future.result()
+                pbar.update(1)
+
+        pbar.close()
+
+        # Move all files to new directory
+        for file in os.listdir(temp_dir):
+            shutil.move(os.path.join(temp_dir, file), local_dir)
+
+
+class S3DownloaderMixin:
+    s3_prefix = "s3://"
+
+    @classmethod
+    def get_local_path(cls, pretrained_model_name_or_path) -> str:
+        if pretrained_model_name_or_path.startswith(cls.s3_prefix):
+            pretrained_model_name_or_path = pretrained_model_name_or_path.replace(
+                cls.s3_prefix, ""
+            )
+            cache_dir = settings.MODEL_CACHE_DIR
+            local_path = os.path.join(cache_dir, pretrained_model_name_or_path)
+            os.makedirs(local_path, exist_ok=True)
+        else:
+            local_path = ""
+        return local_path
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+        # Allow loading models directly from the hub, or using s3
+        if not pretrained_model_name_or_path.startswith(cls.s3_prefix):
+            return super().from_pretrained(
+                pretrained_model_name_or_path, *args, **kwargs
+            )
+
+        local_path = cls.get_local_path(pretrained_model_name_or_path)
+        pretrained_model_name_or_path = pretrained_model_name_or_path.replace(
+            cls.s3_prefix, ""
+        )
+
+        # Retry logic for downloading the model folder
+        retries = 3
+        delay = 5
+        attempt = 0
+        success = False
+        while not success and attempt < retries:
+            try:
+                download_directory(pretrained_model_name_or_path, local_path)
+                success = True  # If download succeeded
+            except Exception as e:
+                logger.error(
+                    f"Error downloading model from {pretrained_model_name_or_path}. Attempt {attempt + 1} of {retries}. Error: {e}"
+                )
+                attempt += 1
+                if attempt < retries:
+                    logger.info(f"Retrying in {delay} seconds...")
+                    time.sleep(delay)  # Wait before retrying
+                else:
+                    logger.error(
+                        f"Failed to download {pretrained_model_name_or_path} after {retries} attempts."
+                    )
+                    raise e  # Reraise exception after max retries
+
+        return super().from_pretrained(local_path, *args, **kwargs)
diff --git a/surya/common/util.py b/surya/common/util.py
new file mode 100644
index 0000000..bd093f5
--- /dev/null
+++ b/surya/common/util.py
@@ -0,0 +1,44 @@
+from typing import List
+
+from surya.common.polygon import PolygonBox
+
+
+def clean_boxes(boxes: List[PolygonBox]) -> List[PolygonBox]:
+    new_boxes = []
+    for box_obj in boxes:
+        xs = [point[0] for point in box_obj.polygon]
+        ys = [point[1] for point in box_obj.polygon]
+        if max(xs) == min(xs) or max(ys) == min(ys):
+            continue
+
+        box = box_obj.bbox
+        contained = False
+        for other_box_obj in boxes:
+            if other_box_obj.polygon == box_obj.polygon:
+                continue
+
+            other_box = other_box_obj.bbox
+            if box == other_box:
+                continue
+            if (
+                box[0] >= other_box[0]
+                and box[1] >= other_box[1]
+                and box[2] <= other_box[2]
+                and box[3] <= other_box[3]
+            ):
+                contained = True
+                break
+        if not contained:
+            new_boxes.append(box_obj)
+    return new_boxes
+
+
+def expand_bbox(bbox, expansion_factor=0.01):
+    expansion_low = 1 - expansion_factor
+    expansion_high = 1 + expansion_factor
+    return [
+        bbox[0] * expansion_low,
+        bbox[1] * expansion_low,
+        bbox[2] * expansion_high,
+        bbox[3] * expansion_high,
+    ]
diff --git a/surya/debug/draw.py b/surya/debug/draw.py
new file mode 100644
index 0000000..3d15731
--- /dev/null
+++ b/surya/debug/draw.py
@@ -0,0 +1,66 @@
+from PIL import ImageDraw, ImageFont
+
+from surya.debug.fonts import get_font_path
+from surya.debug.text import get_text_size
+
+
+def draw_bboxes_on_image(
+    bboxes, image, labels=None, label_font_size=10, color: str | list = "red"
+):
+    polys = []
+    for bb in bboxes:
+        # Clockwise polygon
+        poly = [[bb[0], bb[1]], [bb[2], bb[1]], [bb[2], bb[3]], [bb[0], bb[3]]]
+        polys.append(poly)
+
+    return draw_polys_on_image(
+        polys, image, labels, label_font_size=label_font_size, color=color
+    )
+
+
+def draw_polys_on_image(
+    corners,
+    image,
+    labels=None,
+    box_padding=-1,
+    label_offset=1,
+    label_font_size=10,
+    color: str | list = "red",
+):
+    draw = ImageDraw.Draw(image)
+    font_path = get_font_path()
+    label_font = ImageFont.truetype(font_path, label_font_size)
+
+    for i in range(len(corners)):
+        poly = corners[i]
+        poly = [(int(p[0]), int(p[1])) for p in poly]
+        draw.polygon(
+            poly, outline=color[i] if isinstance(color, list) else color, width=1
+        )
+
+        if labels is not None:
+            label = labels[i]
+            text_position = (
+                min([p[0] for p in poly]) + label_offset,
+                min([p[1] for p in poly]) + label_offset,
+            )
+            text_size = get_text_size(label, label_font)
+            box_position = (
+                text_position[0] - box_padding + label_offset,
+                text_position[1] - box_padding + label_offset,
+                text_position[0] + text_size[0] + box_padding + label_offset,
+                text_position[1] + text_size[1] + box_padding + label_offset,
+            )
+            try:
+                draw.rectangle(box_position, fill="white")
+            except Exception as e:
+                print(f"Error drawing rectangle at {box_position}: {e}")
+                continue
+            draw.text(
+                text_position,
+                label,
+                fill=color[i] if isinstance(color, list) else color,
+                font=label_font,
+            )
+
+    return image
diff --git a/surya/debug/fonts.py b/surya/debug/fonts.py
new file mode 100644
index 0000000..e9e1878
--- /dev/null
+++ b/surya/debug/fonts.py
@@ -0,0 +1,24 @@
+from typing import List, Optional
+import os
+import requests
+
+from surya.settings import settings
+
+
+def get_font_path(langs: Optional[List[str]] = None) -> str:
+    font_path = settings.RECOGNITION_RENDER_FONTS["all"]
+    if langs is not None:
+        for k in settings.RECOGNITION_RENDER_FONTS:
+            if k in langs and len(langs) == 1:
+                font_path = settings.RECOGNITION_RENDER_FONTS[k]
+                break
+
+    if not os.path.exists(font_path):
+        os.makedirs(os.path.dirname(font_path), exist_ok=True)
+        font_dl_path = f"{settings.RECOGNITION_FONT_DL_BASE}/{os.path.basename(font_path)}"
+        with requests.get(font_dl_path, stream=True) as r, open(font_path, 'wb') as f:
+            r.raise_for_status()
+            for chunk in r.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+    return font_path
\ No newline at end of file
diff --git a/surya/debug/katex.js b/surya/debug/katex.js
new file mode 100644
index 0000000..ac12e7e
--- /dev/null
+++ b/surya/debug/katex.js
@@ -0,0 +1,64 @@
+<style>
+    .katex-display-container {
+        display: inline-block;
+        max-width: 100%;
+        overflow-x: auto;
+        max-height: 100%;
+    }
+
+    .katex-inline-container {
+        display: inline-block;
+        max-width: 100%;
+        overflow-x: auto;
+        max-height: 100%;
+    }
+</style>
+<script src="https://cdn.jsdelivr.net/npm/katex@0.16.21/dist/katex.min.js" onload="setTimeout(function() {renderMath()})" async></script>
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.21/dist/katex.min.css">
+<script>
+    function htmlUnescape(escapedText) {
+      const htmlEntities = {
+        '&amp;': '&',
+        '&lt;': '<',
+        '&gt;': '>',
+        '&quot;': '"',
+        '&#39;': "'",
+        '&nbsp;': ' '
+      };
+
+      return escapedText.replace(/&amp;|&lt;|&gt;|&quot;|&#39;|&nbsp;/g, match => htmlEntities[match]);
+    }
+
+    const renderMath = (function() {
+    try {
+       const mathElements = document.querySelectorAll('math');
+
+        mathElements.forEach(function(element) {
+          let mathContent = element.innerHTML.trim();
+          mathContent = htmlUnescape(mathContent);
+          const isDisplay = element.getAttribute('display') === 'block';
+
+          const container = document.createElement('span');
+          container.className = isDisplay ? 'katex-display-container' : 'katex-inline-container';
+          element.parentNode.insertBefore(container, element);
+
+          try {
+            katex.render(mathContent, container, {
+              displayMode: isDisplay,
+              throwOnError: false
+            });
+
+          } catch (err) {
+            console.error('KaTeX rendering error:', err);
+            container.textContent = mathContent; // Fallback to raw text
+          }
+
+          element.parentNode.removeChild(element);
+        });
+
+        console.log('Math rendering complete with', mathElements.length, 'expressions');
+      } catch (err) {
+        console.error('Error in renderMath function:', err);
+      }
+    });
+</script>
\ No newline at end of file
diff --git a/surya/debug/render_html.py b/surya/debug/render_html.py
new file mode 100644
index 0000000..a0f93e1
--- /dev/null
+++ b/surya/debug/render_html.py
@@ -0,0 +1,90 @@
+import html as htmllib
+import os.path
+import re
+
+filepath = os.path.abspath(__file__)
+
+def render_text_as_html(
+        bboxes: list[list[int]],
+        texts: list[str],
+        image_size: tuple[int, int],
+        base_font_size: int = 16,
+        scaler: int = 2
+):
+    katex_path = os.path.join(os.path.dirname(filepath), "katex.js")
+    with open(katex_path, "r") as f:
+        katex_script = f.read()
+
+    html_content = []
+    image_size = tuple([int(s * scaler) for s in image_size])
+    width, height = image_size
+
+
+    html_content.append(f"""
+<!DOCTYPE html>
+<html>
+<head>
+    <style>
+        body {{
+            margin: 0;
+            padding: 0;
+            width: {width}px;
+            height: {height}px;
+            position: relative;
+            overflow: hidden;
+            background: white;
+            color: black;
+        }}
+        .text-box {{
+            position: absolute;
+            overflow: hidden;
+            display: flex;
+            justify-content: left;
+            font-family: Arial, sans-serif;
+            white-space: pre-wrap;
+        }}
+        .vertical-text {{
+          writing-mode: vertical-rl;  /* Top to bottom, right to left */
+        }}
+    </style>
+    {katex_script}
+</head>
+<body>
+""")
+
+    for i, (bbox, text) in enumerate(zip(bboxes, texts)):
+        bbox = bbox.copy()
+        bbox = [int(bb * scaler) for bb in bbox]
+        x1, y1, x2, y2 = bbox
+        width = x2 - x1
+        height = y2 - y1
+        min_dim = min(width, height)
+
+        # Scale font size based on box height
+        font_size = min(int(min_dim * 0.75), base_font_size)
+
+        # Create div with absolute positioning
+        div_style = (
+            f"left: {x1}px; "
+            f"top: {y1}px; "
+            f"width: {width}px; "
+            f"height: {height}px; "
+            f"font-size: {font_size}px;"
+        )
+
+        class_ = "text-box"
+        if height > width * 2:
+            class_ += " vertical-text"
+
+        # Determine if content is HTML/MathML or plain text
+        if "<" in text and ">" in text and re.search(r"<(html|math|div|sub|sup|i|u|mark|small|del|b|br|code)\b", text.lower()):
+            # Content is already HTML/MathML, include as-is
+            html_content.append(f'<span class="{class_}" id="box-{i}" style="{div_style}">{text}</span>')
+        else:
+            # Plain text, escape it
+            escaped_text = htmllib.escape(text)
+            html_content.append(f'<span class="{class_}" id="box-{i}" style="{div_style}">{escaped_text}</span>')
+
+    html_content.append("</body></html>")
+
+    return "\n".join(html_content), image_size
\ No newline at end of file
diff --git a/surya/debug/text.py b/surya/debug/text.py
new file mode 100644
index 0000000..ce120e2
--- /dev/null
+++ b/surya/debug/text.py
@@ -0,0 +1,8 @@
+from PIL import Image, ImageDraw
+
+
+def get_text_size(text, font):
+    im = Image.new(mode="P", size=(0, 0))
+    draw = ImageDraw.Draw(im)
+    _, _, width, height = draw.textbbox((0, 0), text=text, font=font)
+    return width, height
diff --git a/surya/detection/__init__.py b/surya/detection/__init__.py
new file mode 100644
index 0000000..298bceb
--- /dev/null
+++ b/surya/detection/__init__.py
@@ -0,0 +1,147 @@
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Generator, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from PIL import Image
+from tqdm import tqdm
+
+from surya.common.predictor import BasePredictor
+
+from surya.detection.loader import DetectionModelLoader
+from surya.detection.parallel import FakeExecutor
+from surya.detection.util import get_total_splits, split_image
+from surya.detection.schema import TextDetectionResult
+from surya.settings import settings
+from surya.detection.heatmap import parallel_get_boxes
+
+
+class DetectionPredictor(BasePredictor):
+    model_loader_cls = DetectionModelLoader
+    batch_size = settings.DETECTOR_BATCH_SIZE
+    default_batch_sizes = {"cpu": 8, "mps": 8, "cuda": 36}
+
+    def __call__(
+        self, images: List[Image.Image], batch_size=None, include_maps=False
+    ) -> List[TextDetectionResult]:
+        detection_generator = self.batch_detection(images, batch_size=batch_size)
+
+        postprocessing_futures = []
+        max_workers = min(settings.DETECTOR_POSTPROCESSING_CPU_WORKERS, len(images))
+        parallelize = (
+            not settings.IN_STREAMLIT
+            and len(images) >= settings.DETECTOR_MIN_PARALLEL_THRESH
+        )
+        executor = ThreadPoolExecutor if parallelize else FakeExecutor
+        with executor(max_workers=max_workers) as e:
+            for preds, orig_sizes in detection_generator:
+                for pred, orig_size in zip(preds, orig_sizes):
+                    postprocessing_futures.append(
+                        e.submit(parallel_get_boxes, pred, orig_size, include_maps)
+                    )
+
+        return [future.result() for future in postprocessing_futures]
+
+    def prepare_image(self, img):
+        new_size = (self.processor.size["width"], self.processor.size["height"])
+
+        # This double resize actually necessary for downstream accuracy
+        img.thumbnail(new_size, Image.Resampling.LANCZOS)
+        img = img.resize(
+            new_size, Image.Resampling.LANCZOS
+        )  # Stretch smaller dimension to fit new size
+
+        img = np.asarray(img, dtype=np.uint8)
+        img = self.processor(img)["pixel_values"][0]
+        img = torch.from_numpy(img)
+        return img
+
+    def batch_detection(
+        self, images: List, batch_size=None
+    ) -> Generator[Tuple[List[List[np.ndarray]], List[Tuple[int, int]]], None, None]:
+        assert all([isinstance(image, Image.Image) for image in images])
+        if batch_size is None:
+            batch_size = self.get_batch_size()
+        heatmap_count = self.model.config.num_labels
+
+        orig_sizes = [image.size for image in images]
+        splits_per_image = [
+            get_total_splits(size, self.processor.size["height"]) for size in orig_sizes
+        ]
+
+        batches = []
+        current_batch_size = 0
+        current_batch = []
+        for i in range(len(images)):
+            if current_batch_size + splits_per_image[i] > batch_size:
+                if len(current_batch) > 0:
+                    batches.append(current_batch)
+                current_batch = []
+                current_batch_size = 0
+            current_batch.append(i)
+            current_batch_size += splits_per_image[i]
+
+        if len(current_batch) > 0:
+            batches.append(current_batch)
+
+        for batch_idx in tqdm(
+            range(len(batches)), desc="Detecting bboxes", disable=self.disable_tqdm
+        ):
+            batch_image_idxs = batches[batch_idx]
+            batch_images = [images[j].convert("RGB") for j in batch_image_idxs]
+
+            split_index = []
+            split_heights = []
+            image_splits = []
+            for image_idx, image in enumerate(batch_images):
+                image_parts, split_height = split_image(
+                    image, self.processor.size["height"]
+                )
+                image_splits.extend(image_parts)
+                split_index.extend([image_idx] * len(image_parts))
+                split_heights.extend(split_height)
+
+            image_splits = [self.prepare_image(image) for image in image_splits]
+            # Batch images in dim 0
+            batch = torch.stack(image_splits, dim=0).to(self.model.dtype)
+
+            with settings.INFERENCE_MODE():
+                pred = self.model(pixel_values=batch.to(self.model.device))
+
+            logits = pred.logits
+            correct_shape = [
+                self.processor.size["height"],
+                self.processor.size["width"],
+            ]
+            current_shape = list(logits.shape[2:])
+            if current_shape != correct_shape:
+                logits = F.interpolate(
+                    logits, size=correct_shape, mode="bilinear", align_corners=False
+                )
+
+            logits = logits.to(torch.float32).cpu().numpy()
+            preds = []
+            for i, (idx, height) in enumerate(zip(split_index, split_heights)):
+                # If our current prediction length is below the image idx, that means we have a new image
+                # Otherwise, we need to add to the current image
+                if len(preds) <= idx:
+                    preds.append([logits[i][k] for k in range(heatmap_count)])
+                else:
+                    heatmaps = preds[idx]
+                    pred_heatmaps = [logits[i][k] for k in range(heatmap_count)]
+
+                    if height < self.processor.size["height"]:
+                        # Cut off padding to get original height
+                        pred_heatmaps = [
+                            pred_heatmap[:height, :] for pred_heatmap in pred_heatmaps
+                        ]
+
+                    for k in range(heatmap_count):
+                        heatmaps[k] = np.vstack([heatmaps[k], pred_heatmaps[k]])
+                    preds[idx] = heatmaps
+
+            yield preds, [orig_sizes[j] for j in batch_image_idxs]
+
+        torch.cuda.empty_cache()
diff --git a/surya/detection/heatmap.py b/surya/detection/heatmap.py
new file mode 100644
index 0000000..93ffe04
--- /dev/null
+++ b/surya/detection/heatmap.py
@@ -0,0 +1,165 @@
+from typing import List
+
+import cv2
+import numpy as np
+from PIL import Image
+
+from surya.common.util import clean_boxes
+from surya.detection import TextDetectionResult
+from surya.common.polygon import PolygonBox
+from surya.settings import settings
+
+
+def get_dynamic_thresholds(linemap, text_threshold, low_text, typical_top10_avg=0.7):
+    # Find average intensity of top 10% pixels
+    flat_map = linemap.ravel()
+    top_10_count = int(len(flat_map) * 0.9)
+    avg_intensity = np.mean(np.partition(flat_map, top_10_count)[top_10_count:])
+    scaling_factor = np.clip(avg_intensity / typical_top10_avg, 0, 1) ** (1 / 2)
+
+    low_text = np.clip(low_text * scaling_factor, 0.1, 0.6)
+    text_threshold = np.clip(text_threshold * scaling_factor, 0.15, 0.8)
+
+    return text_threshold, low_text
+
+
+def detect_boxes(linemap, text_threshold, low_text):
+    # From CRAFT - https://github.com/clovaai/CRAFT-pytorch
+    # Modified to return boxes and for speed, accuracy
+    img_h, img_w = linemap.shape
+
+    text_threshold, low_text = get_dynamic_thresholds(linemap, text_threshold, low_text)
+
+    text_score_comb = (linemap > low_text).astype(np.uint8)
+    label_count, labels, stats, centroids = cv2.connectedComponentsWithStats(
+        text_score_comb, connectivity=4
+    )
+
+    det = []
+    confidences = []
+    max_confidence = 0
+
+    for k in range(1, label_count):
+        # size filtering
+        size = stats[k, cv2.CC_STAT_AREA]
+        if size < 10:
+            continue
+
+        # make segmentation map
+        x, y, w, h = stats[
+            k,
+            [cv2.CC_STAT_LEFT, cv2.CC_STAT_TOP, cv2.CC_STAT_WIDTH, cv2.CC_STAT_HEIGHT],
+        ]
+
+        try:
+            niter = int(np.sqrt(min(w, h)))
+        except ValueError:
+            niter = 0
+
+        buffer = 1
+        sx, sy = max(0, x - niter - buffer), max(0, y - niter - buffer)
+        ex, ey = min(img_w, x + w + niter + buffer), min(img_h, y + h + niter + buffer)
+
+        mask = labels[sy:ey, sx:ex] == k
+        selected_linemap = linemap[sy:ey, sx:ex][mask]
+        if selected_linemap.size == 0:
+            continue
+
+        line_max = np.max(selected_linemap)
+
+        # thresholding
+        if line_max < text_threshold:
+            continue
+
+        segmap = mask.astype(np.uint8)
+
+        ksize = buffer + niter
+        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (ksize, ksize))
+        selected_segmap = cv2.dilate(segmap, kernel)
+
+        # make box
+        y_inds, x_inds = np.nonzero(selected_segmap)
+        x_inds += sx
+        y_inds += sy
+        np_contours = np.column_stack((x_inds, y_inds))
+        rectangle = cv2.minAreaRect(np_contours)
+        box = cv2.boxPoints(rectangle)
+
+        # align diamond-shape
+        w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
+        box_ratio = max(w, h) / (min(w, h) + 1e-5)
+        if abs(1 - box_ratio) <= 0.1:
+            left, right = np_contours[:, 0].min(), np_contours[:, 0].max()
+            top, bottom = np_contours[:, 1].min(), np_contours[:, 1].max()
+            box = np.array(
+                [[left, top], [right, top], [right, bottom], [left, bottom]],
+                dtype=np.float32,
+            )
+
+        # make clock-wise order
+        startidx = box.sum(axis=1).argmin()
+        box = np.roll(box, 4 - startidx, 0)
+
+        max_confidence = max(max_confidence, line_max)
+
+        confidences.append(line_max)
+        det.append(box)
+
+    if max_confidence > 0:
+        confidences = [c / max_confidence for c in confidences]
+    return det, confidences
+
+
+def get_detected_boxes(textmap, text_threshold=None, low_text=None) -> List[PolygonBox]:
+    if text_threshold is None:
+        text_threshold = settings.DETECTOR_TEXT_THRESHOLD
+    if low_text is None:
+        low_text = settings.DETECTOR_BLANK_THRESHOLD
+
+    if textmap.dtype != np.float32:
+        textmap = textmap.astype(np.float32)
+
+    boxes, confidences = detect_boxes(textmap, text_threshold, low_text)
+    # From point form to box form
+    return [
+        PolygonBox(polygon=box, confidence=confidence)
+        for box, confidence in zip(boxes, confidences)
+    ]
+
+
+def get_and_clean_boxes(
+    textmap, processor_size, image_size, text_threshold=None, low_text=None
+) -> List[PolygonBox]:
+    bboxes = get_detected_boxes(textmap, text_threshold, low_text)
+    for bbox in bboxes:
+        bbox.rescale(processor_size, image_size)
+        bbox.fit_to_bounds([0, 0, image_size[0], image_size[1]])
+
+    bboxes = clean_boxes(bboxes)
+    return bboxes
+
+
+def parallel_get_boxes(preds, orig_sizes, include_maps=False):
+    heatmap, affinity_map = preds
+    heat_img, aff_img = None, None
+
+    if include_maps:
+        heat_img = Image.fromarray((heatmap * 255).astype(np.uint8))
+        aff_img = Image.fromarray((affinity_map * 255).astype(np.uint8))
+    heatmap_size = list(reversed(heatmap.shape))
+    bboxes = get_and_clean_boxes(heatmap, heatmap_size, orig_sizes)
+    for box in bboxes:
+        # Skip for vertical boxes
+        if box.height < 3 * box.width:
+            box.expand(x_margin=0, y_margin=settings.DETECTOR_BOX_Y_EXPAND_MARGIN)
+            box.fit_to_bounds(
+                [0, 0, orig_sizes[0], orig_sizes[1]]
+            )  # Fix any bad expands
+
+    result = TextDetectionResult(
+        bboxes=bboxes,
+        heatmap=heat_img,
+        affinity_map=aff_img,
+        image_bbox=[0, 0, orig_sizes[0], orig_sizes[1]],
+    )
+    return result
diff --git a/surya/detection/loader.py b/surya/detection/loader.py
new file mode 100644
index 0000000..189ed0e
--- /dev/null
+++ b/surya/detection/loader.py
@@ -0,0 +1,53 @@
+from typing import Optional
+
+import torch
+
+from surya.common.load import ModelLoader
+from surya.detection.processor import SegformerImageProcessor
+
+from surya.detection.model.config import EfficientViTConfig
+from surya.detection.model.encoderdecoder import EfficientViTForSemanticSegmentation
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+class DetectionModelLoader(ModelLoader):
+    def __init__(self, checkpoint: Optional[str] = None):
+        super().__init__(checkpoint)
+
+        if self.checkpoint is None:
+            self.checkpoint = settings.DETECTOR_MODEL_CHECKPOINT
+
+    def model(
+        self,
+        device: Optional[torch.device | str] = None,
+        dtype: Optional[torch.dtype | str] = None,
+        attention_implementation: Optional[str] = None,
+    ) -> EfficientViTForSemanticSegmentation:
+        if device is None:
+            device = settings.TORCH_DEVICE_MODEL
+        if dtype is None:
+            dtype = settings.MODEL_DTYPE
+
+        config = EfficientViTConfig.from_pretrained(self.checkpoint)
+        model = EfficientViTForSemanticSegmentation.from_pretrained(
+            self.checkpoint,
+            dtype=dtype,
+            config=config,
+        )
+        model = model.to(device)
+        model = model.eval()
+
+        logger.debug(
+            f"Loaded detection model {self.checkpoint} from {EfficientViTForSemanticSegmentation.get_local_path(self.checkpoint)} onto device {device} with dtype {dtype}"
+        )
+        return model
+
+    def processor(
+        self,
+        device: Optional[torch.device | str] = None,
+        dtype: Optional[torch.dtype | str] = None,
+    ) -> SegformerImageProcessor:
+        return SegformerImageProcessor.from_pretrained(self.checkpoint)
diff --git a/surya/detection/model/__init__.py b/surya/detection/model/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/surya/detection/model/config.py b/surya/detection/model/config.py
new file mode 100644
index 0000000..b205ab4
--- /dev/null
+++ b/surya/detection/model/config.py
@@ -0,0 +1,53 @@
+from transformers import PretrainedConfig
+
+from surya.common.s3 import S3DownloaderMixin
+
+
+class EfficientViTConfig(S3DownloaderMixin, PretrainedConfig):
+    r"""
+    ```"""
+
+    model_type = "efficientvit"
+
+    def __init__(
+        self,
+        num_classes=2,
+        num_channels=3,
+        widths=(32, 64, 128, 256, 512),
+        head_dim=32,
+        num_stages=4,
+        depths=(1, 1, 1, 6, 6),
+        strides=(2, 2, 2, 2, 2),
+        hidden_sizes=(32, 64, 160, 256),
+        patch_size=(7, 7),
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        classifier_dropout_prob=0.0,
+        layer_norm_eps=1e-6,
+        decoder_layer_hidden_size=128,
+        decoder_hidden_size=512,
+        semantic_loss_ignore_index=255,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.num_classes = num_classes
+        self.widths = widths
+        self.head_dim = head_dim
+
+        self.num_channels = num_channels
+        self.num_stages = num_stages
+        self.depths = depths
+        self.strides = strides
+        self.hidden_sizes = hidden_sizes
+        self.patch_size = patch_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.classifier_dropout_prob = classifier_dropout_prob
+        self.layer_norm_eps = layer_norm_eps
+        self.decoder_hidden_size = decoder_hidden_size
+        self.decoder_layer_hidden_size = decoder_layer_hidden_size
+        self.semantic_loss_ignore_index = semantic_loss_ignore_index
+
+        self.initializer_range = initializer_range
\ No newline at end of file
diff --git a/surya/detection/model/encoderdecoder.py b/surya/detection/model/encoderdecoder.py
new file mode 100644
index 0000000..af5ea9c
--- /dev/null
+++ b/surya/detection/model/encoderdecoder.py
@@ -0,0 +1,839 @@
+"""
+This is an implementation of efficientvit, with some modifications (decode head, etc).
+
+Original paper at https://arxiv.org/abs/2205.14756
+
+Code adapted from timm, https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/efficientvit_mit.py
+Original code (that timm adapted from) at https://github.com/mit-han-lab/efficientvit
+
+License: Apache 2
+"""
+
+from __future__ import annotations
+
+from typing import Optional, Union, Tuple, List, Any
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers.modeling_outputs import SemanticSegmenterOutput
+
+from surya.common.pretrained import SuryaPreTrainedModel
+from surya.common.s3 import S3DownloaderMixin
+from surya.detection.model.config import EfficientViTConfig
+
+
+def val2list(x: Union[List, Tuple, Any], repeat_time=1):
+    if isinstance(x, (list, tuple)):
+        return list(x)
+    return [x for _ in range(repeat_time)]
+
+
+def val2tuple(x: Union[List, Tuple, Any], min_len: int = 1, idx_repeat: int = -1):
+    # repeat elements if necessary
+    x = val2list(x)
+    if len(x) > 0:
+        x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]
+
+    return tuple(x)
+
+
+def get_same_padding(
+    kernel_size: Union[int, Tuple[int, ...]],
+) -> Union[int, Tuple[int, ...]]:
+    if isinstance(kernel_size, tuple):
+        return tuple([get_same_padding(ks) for ks in kernel_size])
+    else:
+        assert kernel_size % 2 > 0, "kernel size should be odd number"
+        return kernel_size // 2
+
+
+def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1) -> int:
+    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
+    return padding
+
+
+class ConvNormAct(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size=3,
+        stride=1,
+        dilation=1,
+        groups=1,
+        bias=False,
+        dropout=0.0,
+        norm_layer=nn.BatchNorm2d,
+        act_layer=nn.ReLU,
+    ):
+        super(ConvNormAct, self).__init__()
+        self.dropout = nn.Dropout(dropout, inplace=False)
+        padding = get_padding(kernel_size, stride, dilation)
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding=padding,
+        )
+        self.norm = (
+            norm_layer(num_features=out_channels) if norm_layer else nn.Identity()
+        )
+        self.act = act_layer(inplace=True) if act_layer is not None else nn.Identity()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        x = self.act(x)
+        return x
+
+
+class DSConv(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size=3,
+        stride=1,
+        use_bias=False,
+        norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
+        act_layer=(nn.ReLU6, None),
+    ):
+        super(DSConv, self).__init__()
+        use_bias = val2tuple(use_bias, 2)
+        norm_layer = val2tuple(norm_layer, 2)
+        act_layer = val2tuple(act_layer, 2)
+
+        self.depth_conv = ConvNormAct(
+            in_channels,
+            in_channels,
+            kernel_size,
+            stride,
+            groups=in_channels,
+            norm_layer=norm_layer[0],
+            act_layer=act_layer[0],
+            bias=use_bias[0],
+        )
+        self.point_conv = ConvNormAct(
+            in_channels,
+            out_channels,
+            1,
+            norm_layer=norm_layer[1],
+            act_layer=act_layer[1],
+            bias=use_bias[1],
+        )
+
+    def forward(self, x):
+        x = self.depth_conv(x)
+        x = self.point_conv(x)
+        return x
+
+
+class ConvBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size=3,
+        stride=1,
+        mid_channels=None,
+        expand_ratio=1,
+        use_bias=False,
+        norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
+        act_layer=(nn.ReLU6, None),
+    ):
+        super(ConvBlock, self).__init__()
+        use_bias = val2tuple(use_bias, 2)
+        norm_layer = val2tuple(norm_layer, 2)
+        act_layer = val2tuple(act_layer, 2)
+        mid_channels = mid_channels or round(in_channels * expand_ratio)
+
+        self.conv1 = ConvNormAct(
+            in_channels,
+            mid_channels,
+            kernel_size,
+            stride,
+            norm_layer=norm_layer[0],
+            act_layer=act_layer[0],
+            bias=use_bias[0],
+        )
+        self.conv2 = ConvNormAct(
+            mid_channels,
+            out_channels,
+            kernel_size,
+            1,
+            norm_layer=norm_layer[1],
+            act_layer=act_layer[1],
+            bias=use_bias[1],
+        )
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+
+class MBConv(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size=3,
+        stride=1,
+        mid_channels=None,
+        expand_ratio=6,
+        use_bias=False,
+        norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d, nn.BatchNorm2d),
+        act_layer=(nn.ReLU6, nn.ReLU6, None),
+    ):
+        super(MBConv, self).__init__()
+        use_bias = val2tuple(use_bias, 3)
+        norm_layer = val2tuple(norm_layer, 3)
+        act_layer = val2tuple(act_layer, 3)
+        mid_channels = mid_channels or round(in_channels * expand_ratio)
+
+        self.inverted_conv = ConvNormAct(
+            in_channels,
+            mid_channels,
+            1,
+            stride=1,
+            norm_layer=norm_layer[0],
+            act_layer=act_layer[0],
+            bias=use_bias[0],
+        )
+        self.depth_conv = ConvNormAct(
+            mid_channels,
+            mid_channels,
+            kernel_size,
+            stride=stride,
+            groups=mid_channels,
+            norm_layer=norm_layer[1],
+            act_layer=act_layer[1],
+            bias=use_bias[1],
+        )
+        self.point_conv = ConvNormAct(
+            mid_channels,
+            out_channels,
+            1,
+            norm_layer=norm_layer[2],
+            act_layer=act_layer[2],
+            bias=use_bias[2],
+        )
+
+    def forward(self, x):
+        x = self.inverted_conv(x)
+        x = self.depth_conv(x)
+        x = self.point_conv(x)
+        return x
+
+
+class FusedMBConv(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size=3,
+        stride=1,
+        mid_channels=None,
+        expand_ratio=6,
+        groups=1,
+        use_bias=False,
+        norm_layer=(nn.BatchNorm2d, nn.BatchNorm2d),
+        act_layer=(nn.ReLU6, None),
+    ):
+        super(FusedMBConv, self).__init__()
+        use_bias = val2tuple(use_bias, 2)
+        norm_layer = val2tuple(norm_layer, 2)
+        act_layer = val2tuple(act_layer, 2)
+        mid_channels = mid_channels or round(in_channels * expand_ratio)
+
+        self.spatial_conv = ConvNormAct(
+            in_channels,
+            mid_channels,
+            kernel_size,
+            stride=stride,
+            groups=groups,
+            norm_layer=norm_layer[0],
+            act_layer=act_layer[0],
+            bias=use_bias[0],
+        )
+        self.point_conv = ConvNormAct(
+            mid_channels,
+            out_channels,
+            1,
+            norm_layer=norm_layer[1],
+            act_layer=act_layer[1],
+            bias=use_bias[1],
+        )
+
+    def forward(self, x):
+        x = self.spatial_conv(x)
+        x = self.point_conv(x)
+        return x
+
+
+class LiteMLA(nn.Module):
+    """Lightweight multi-scale linear attention"""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        heads: Union[int, None] = None,
+        heads_ratio: float = 1.0,
+        dim=8,
+        use_bias=False,
+        norm_layer=(None, nn.BatchNorm2d),
+        act_layer=(None, None),
+        kernel_func=nn.ReLU,
+        scales=(5,),
+        eps=1e-5,
+    ):
+        super(LiteMLA, self).__init__()
+        self.eps = eps
+        heads = heads or int(in_channels // dim * heads_ratio)
+        total_dim = heads * dim
+        use_bias = val2tuple(use_bias, 2)
+        norm_layer = val2tuple(norm_layer, 2)
+        act_layer = val2tuple(act_layer, 2)
+
+        self.dim = dim
+        self.qkv = ConvNormAct(
+            in_channels,
+            3 * total_dim,
+            1,
+            bias=use_bias[0],
+            norm_layer=norm_layer[0],
+            act_layer=act_layer[0],
+        )
+        self.aggreg = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.Conv2d(
+                        3 * total_dim,
+                        3 * total_dim,
+                        scale,
+                        padding=get_same_padding(scale),
+                        groups=3 * total_dim,
+                        bias=use_bias[0],
+                    ),
+                    nn.Conv2d(
+                        3 * total_dim,
+                        3 * total_dim,
+                        1,
+                        groups=3 * heads,
+                        bias=use_bias[0],
+                    ),
+                )
+                for scale in scales
+            ]
+        )
+        self.kernel_func = kernel_func(inplace=False)
+
+        self.proj = ConvNormAct(
+            total_dim * (1 + len(scales)),
+            out_channels,
+            1,
+            bias=use_bias[1],
+            norm_layer=norm_layer[1],
+            act_layer=act_layer[1],
+        )
+
+    def _attn(self, q, k, v):
+        dtype = v.dtype
+        q, k, v = q.float(), k.float(), v.float()
+        kv = k.transpose(-1, -2) @ v
+        out = q @ kv
+        out = out[..., :-1] / (out[..., -1:] + self.eps)
+        return out.to(dtype)
+
+    def forward(self, x):
+        # Shape is B, C, H, W
+        B, _, H, W = x.shape
+
+        # generate multi-scale q, k, v
+        qkv = self.qkv(x)
+        multi_scale_qkv = [qkv]
+        for op in self.aggreg:
+            multi_scale_qkv.append(op(qkv))
+        multi_scale_qkv = torch.cat(multi_scale_qkv, dim=1)
+        multi_scale_qkv = multi_scale_qkv.reshape(B, -1, 3 * self.dim, H * W).transpose(
+            -1, -2
+        )
+        # Shape for each is B, C, HW, head_dim
+        q, k, v = multi_scale_qkv.chunk(3, dim=-1)
+
+        # lightweight global attention
+        q = self.kernel_func(q)
+        k = self.kernel_func(k)
+        v = F.pad(v, (0, 1), mode="constant", value=1.0)
+
+        out = self._attn(q, k, v)
+
+        # final projection
+        out = out.transpose(-1, -2).reshape(B, -1, H, W)
+        out = self.proj(out)
+        return out
+
+
+class EfficientVitBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        heads_ratio=1.0,
+        head_dim=32,
+        expand_ratio=4,
+        norm_layer=nn.BatchNorm2d,
+        act_layer=nn.Hardswish,
+    ):
+        super(EfficientVitBlock, self).__init__()
+        self.context_module = ResidualBlock(
+            LiteMLA(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                heads_ratio=heads_ratio,
+                dim=head_dim,
+                norm_layer=(None, norm_layer),
+            ),
+            nn.Identity(),
+        )
+        self.local_module = ResidualBlock(
+            MBConv(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                expand_ratio=expand_ratio,
+                use_bias=(True, True, False),
+                norm_layer=(None, None, norm_layer),
+                act_layer=(act_layer, act_layer, None),
+            ),
+            nn.Identity(),
+        )
+
+    def forward(self, x):
+        x = self.context_module(x)
+        x = self.local_module(x)
+        return x
+
+
+class ResidualBlock(nn.Module):
+    def __init__(
+        self,
+        main: Optional[nn.Module],
+        shortcut: Optional[nn.Module] = None,
+        pre_norm: Optional[nn.Module] = None,
+    ):
+        super(ResidualBlock, self).__init__()
+        self.pre_norm = pre_norm if pre_norm is not None else nn.Identity()
+        self.main = main
+        self.shortcut = shortcut
+
+    def forward(self, x):
+        res = self.main(self.pre_norm(x))
+        if self.shortcut is not None:
+            res = res + self.shortcut(x)
+        return res
+
+
+def build_local_block(
+    in_channels: int,
+    out_channels: int,
+    stride: int,
+    kernel_size: int,
+    expand_ratio: float,
+    norm_layer: str,
+    act_layer: str,
+    fewer_norm: bool = False,
+    block_type: str = "default",
+):
+    assert block_type in ["default", "large", "fused"]
+    if expand_ratio == 1:
+        if block_type == "default":
+            block = DSConv(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                stride=stride,
+                kernel_size=kernel_size,
+                use_bias=(True, False) if fewer_norm else False,
+                norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
+                act_layer=(act_layer, None),
+            )
+        else:
+            block = ConvBlock(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                stride=stride,
+                kernel_size=kernel_size,
+                use_bias=(True, False) if fewer_norm else False,
+                norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
+                act_layer=(act_layer, None),
+            )
+    else:
+        if block_type == "default":
+            block = MBConv(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                stride=stride,
+                kernel_size=kernel_size,
+                expand_ratio=expand_ratio,
+                use_bias=(True, True, False) if fewer_norm else False,
+                norm_layer=(None, None, norm_layer) if fewer_norm else norm_layer,
+                act_layer=(act_layer, act_layer, None),
+            )
+        else:
+            block = FusedMBConv(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                stride=stride,
+                kernel_size=kernel_size,
+                expand_ratio=expand_ratio,
+                use_bias=(True, False) if fewer_norm else False,
+                norm_layer=(None, norm_layer) if fewer_norm else norm_layer,
+                act_layer=(act_layer, None),
+            )
+    return block
+
+
+class Stem(nn.Sequential):
+    def __init__(
+        self,
+        in_chs,
+        out_chs,
+        depth,
+        stride,
+        norm_layer,
+        act_layer,
+        block_type="default",
+    ):
+        super().__init__()
+        self.stride = stride
+
+        self.add_module(
+            "in_conv",
+            ConvNormAct(
+                in_chs,
+                out_chs,
+                kernel_size=stride + 1,
+                stride=stride,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+            ),
+        )
+        stem_block = 0
+        for _ in range(depth):
+            self.add_module(
+                f"res{stem_block}",
+                ResidualBlock(
+                    build_local_block(
+                        in_channels=out_chs,
+                        out_channels=out_chs,
+                        stride=1,
+                        kernel_size=3,
+                        expand_ratio=1,
+                        norm_layer=norm_layer,
+                        act_layer=act_layer,
+                        block_type=block_type,
+                    ),
+                    nn.Identity(),
+                ),
+            )
+            stem_block += 1
+
+
+class EfficientVitLargeStage(nn.Module):
+    def __init__(
+        self,
+        in_chs,
+        out_chs,
+        depth,
+        stride,
+        norm_layer,
+        act_layer,
+        head_dim,
+        vit_stage=False,
+        fewer_norm=False,
+    ):
+        super(EfficientVitLargeStage, self).__init__()
+        blocks = [
+            ResidualBlock(
+                build_local_block(
+                    in_channels=in_chs,
+                    out_channels=out_chs,
+                    stride=stride,
+                    kernel_size=stride + 1,
+                    expand_ratio=24 if vit_stage else 16,
+                    norm_layer=norm_layer,
+                    act_layer=act_layer,
+                    fewer_norm=vit_stage or fewer_norm,
+                    block_type="default" if fewer_norm else "fused",
+                ),
+                None,
+            )
+        ]
+        in_chs = out_chs
+
+        if vit_stage:
+            # for stage 4
+            for _ in range(depth):
+                blocks.append(
+                    EfficientVitBlock(
+                        in_channels=in_chs,
+                        head_dim=head_dim,
+                        expand_ratio=6,
+                        norm_layer=norm_layer,
+                        act_layer=act_layer,
+                    )
+                )
+        else:
+            # for stage 1, 2, 3
+            for i in range(depth):
+                blocks.append(
+                    ResidualBlock(
+                        build_local_block(
+                            in_channels=in_chs,
+                            out_channels=out_chs,
+                            stride=1,
+                            kernel_size=3,
+                            expand_ratio=4,
+                            norm_layer=norm_layer,
+                            act_layer=act_layer,
+                            fewer_norm=fewer_norm,
+                            block_type="default" if fewer_norm else "fused",
+                        ),
+                        nn.Identity(),
+                    )
+                )
+
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        return self.blocks(x)
+
+
+class EfficientVitLarge(nn.Module):
+    def __init__(
+        self,
+        config: EfficientViTConfig,
+        norm_layer=nn.BatchNorm2d,
+        act_layer=nn.Hardswish,
+    ):
+        super(EfficientVitLarge, self).__init__()
+        self.grad_checkpointing = False
+        self.num_classes = config.num_classes
+        self.norm_eps = config.layer_norm_eps
+        norm_layer = partial(norm_layer, eps=self.norm_eps)
+
+        # input stem
+        self.stem = Stem(
+            config.num_channels,
+            config.widths[0],
+            config.depths[0],
+            config.strides[0],
+            norm_layer,
+            act_layer,
+            block_type="large",
+        )
+        stride = config.strides[0]
+
+        # stages
+        self.feature_info = []
+        self.stages = nn.Sequential()
+        in_channels = config.widths[0]
+        for i, (w, d, s) in enumerate(
+            zip(config.widths[1:], config.depths[1:], config.strides[1:])
+        ):
+            self.stages.append(
+                EfficientVitLargeStage(
+                    in_channels,
+                    w,
+                    depth=d,
+                    stride=s,
+                    norm_layer=norm_layer,
+                    act_layer=act_layer,
+                    head_dim=config.head_dim,
+                    vit_stage=i >= 3,
+                    fewer_norm=i >= 2,
+                )
+            )
+            stride *= s
+            in_channels = w
+            self.feature_info += [
+                dict(num_chs=in_channels, reduction=stride, module=f"stages.{i}")
+            ]
+
+        self.num_features = in_channels
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.grad_checkpointing = enable
+
+    def forward(self, x):
+        x = self.stem(x)
+        encoder_hidden_states = []
+        for i, module in enumerate(self.stages):
+            x = module(x)
+            encoder_hidden_states.append(x)
+
+        return encoder_hidden_states
+
+
+class EfficientViTPreTrainedModel(SuryaPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = EfficientViTConfig
+    base_model_prefix = "efficientvit"
+    main_input_name = "pixel_values"
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+class DecodeMLP(nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super().__init__()
+        self.proj = nn.Linear(input_dim, output_dim)
+
+    def forward(self, hidden_states: torch.Tensor):
+        # Input is B, C, H, W
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+        # Output is B, HW, C
+        hidden_states = self.proj(hidden_states)
+        return hidden_states
+
+
+class DecodeHead(EfficientViTPreTrainedModel):
+    def __init__(self, config: EfficientViTConfig):
+        super().__init__(config)
+
+        # linear layers which will unify the channel dimension of each of the encoder blocks to the same config.decoder_hidden_size
+        mlps = []
+        for width in config.widths[1:]:
+            mlp = DecodeMLP(
+                input_dim=width, output_dim=config.decoder_layer_hidden_size
+            )
+            mlps.append(mlp)
+        self.linear_c = nn.ModuleList(mlps)
+
+        # the following 3 layers implement the ConvModule of the original implementation
+        self.linear_fuse = nn.Conv2d(
+            in_channels=config.decoder_layer_hidden_size * config.num_stages,
+            out_channels=config.decoder_hidden_size,
+            kernel_size=1,
+            bias=False,
+        )
+        self.batch_norm = nn.BatchNorm2d(config.decoder_hidden_size)
+        self.activation = nn.ReLU()
+
+        self.dropout = nn.Dropout(config.classifier_dropout_prob)
+        self.classifier = nn.Conv2d(
+            config.decoder_hidden_size, config.num_labels, kernel_size=1
+        )
+
+        self.config = config
+
+    def forward(self, encoder_hidden_states: torch.FloatTensor) -> torch.Tensor:
+        batch_size = encoder_hidden_states[-1].shape[0]
+
+        all_hidden_states = ()
+        for encoder_hidden_state, mlp in zip(encoder_hidden_states, self.linear_c):
+            height, width = encoder_hidden_state.shape[2], encoder_hidden_state.shape[3]
+            encoder_hidden_state = mlp(encoder_hidden_state)  # Output is B, HW, C
+            # Permute to B, C, HW
+            encoder_hidden_state = encoder_hidden_state.permute(0, 2, 1)
+            encoder_hidden_state = encoder_hidden_state.reshape(
+                batch_size, -1, height, width
+            )
+            # upsample
+            encoder_hidden_state = nn.functional.interpolate(
+                encoder_hidden_state,
+                size=encoder_hidden_states[0].size()[2:],
+                mode="bilinear",
+                align_corners=False,
+            )
+            all_hidden_states += (encoder_hidden_state,)
+
+        hidden_states = self.linear_fuse(torch.cat(all_hidden_states[::-1], dim=1))
+        hidden_states = self.batch_norm(hidden_states)
+        hidden_states = self.activation(hidden_states)
+
+        # logits are of shape (batch_size, num_labels, height/4, width/4)
+        logits = self.classifier(hidden_states)
+
+        return logits
+
+
+class EfficientViTForSemanticSegmentation(
+    S3DownloaderMixin, EfficientViTPreTrainedModel
+):
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        self.vit = EfficientVitLarge(config)
+        self.decode_head = DecodeHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self, pixel_values: torch.FloatTensor
+    ) -> Union[Tuple, SemanticSegmenterOutput]:
+        # Pixel values should be B,C,H,W
+        encoder_hidden_states = self.vit(
+            pixel_values,
+        )
+
+        logits = self.decode_head(encoder_hidden_states)
+
+        # Apply sigmoid to get 0-1 output
+        logits = torch.special.expit(logits)
+
+        return SemanticSegmenterOutput(
+            loss=None, logits=logits, hidden_states=encoder_hidden_states
+        )
+
+
+class EfficientViTForSemanticLayoutSegmentation(EfficientViTPreTrainedModel):
+    def __init__(self, config, **kwargs):
+        super().__init__(config, **kwargs)
+        self.vit = EfficientVitLarge(config)
+        self.decode_head = DecodeHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self, pixel_values: torch.FloatTensor
+    ) -> Union[Tuple, SemanticSegmenterOutput]:
+        # Pixel values should be B,C,H,W
+        encoder_hidden_states = self.vit(
+            pixel_values,
+        )
+
+        logits = self.decode_head(encoder_hidden_states)
+
+        # Apply sigmoid to get 0-1 output
+        logits = torch.special.expit(logits)
+
+        return SemanticSegmenterOutput(
+            loss=None, logits=logits, hidden_states=encoder_hidden_states
+        )
diff --git a/surya/detection/parallel.py b/surya/detection/parallel.py
new file mode 100644
index 0000000..2779c8e
--- /dev/null
+++ b/surya/detection/parallel.py
@@ -0,0 +1,19 @@
+class FakeFuture:
+    def __init__(self, func, *args, **kwargs):
+        self._result = func(*args, **kwargs)
+
+    def result(self):
+        return self._result
+
+class FakeExecutor:
+    def __init__(self, **kwargs):
+        pass
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *excinfo):
+        pass
+
+    def submit(self, fn, *args, **kwargs):
+        return FakeFuture(fn, *args, **kwargs)
diff --git a/surya/detection/processor.py b/surya/detection/processor.py
new file mode 100644
index 0000000..1cb44a0
--- /dev/null
+++ b/surya/detection/processor.py
@@ -0,0 +1,317 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Modified image processor class for Segformer based on transformers"""
+
+import warnings
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+
+from transformers.image_processing_utils import (
+    BaseImageProcessor,
+    BatchFeature,
+    get_size_dict,
+)
+from transformers.image_transforms import to_channel_dimension_format
+from transformers.image_utils import (
+    IMAGENET_DEFAULT_MEAN,
+    IMAGENET_DEFAULT_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    infer_channel_dimension_format,
+    make_list_of_images,
+)
+from transformers.utils import TensorType
+
+
+import PIL.Image
+
+from surya.common.s3 import S3DownloaderMixin
+
+
+class SegformerImageProcessor(S3DownloaderMixin, BaseImageProcessor):
+    r"""
+    Constructs a Segformer image processor.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
+            size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
+        size (`Dict[str, int]` *optional*, defaults to `{"height": 512, "width": 512}`):
+            Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
+            method.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+            `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+            parameter in the `preprocess` method.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+            method.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+            method.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+        do_reduce_labels (`bool`, *optional*, defaults to `False`):
+            Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
+            used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
+            background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the
+            `preprocess` method.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_reduce_labels: bool = False,
+        **kwargs,
+    ) -> None:
+        if "reduce_labels" in kwargs:
+            warnings.warn(
+                "The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use "
+                "`do_reduce_labels` instead.",
+                FutureWarning,
+            )
+            do_reduce_labels = kwargs.pop("reduce_labels")
+
+        super().__init__(**kwargs)
+        size = size if size is not None else {"height": 512, "width": 512}
+        size = get_size_dict(size)
+        self.do_resize = do_resize
+        self.size = size
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = (
+            image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
+        )
+        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
+        self.do_reduce_labels = do_reduce_labels
+        self._valid_processor_keys = [
+            "images",
+            "segmentation_maps",
+            "do_resize",
+            "size",
+            "resample",
+            "do_rescale",
+            "rescale_factor",
+            "do_normalize",
+            "image_mean",
+            "image_std",
+            "do_reduce_labels",
+            "return_tensors",
+            "data_format",
+            "input_data_format",
+        ]
+
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure `do_reduce_labels` is updated if image
+        processor is created using from_dict and kwargs e.g. `SegformerImageProcessor.from_pretrained(checkpoint,
+        reduce_labels=True)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "reduce_labels" in kwargs:
+            image_processor_dict["reduce_labels"] = kwargs.pop("reduce_labels")
+        return super().from_dict(image_processor_dict, **kwargs)
+
+    def _preprocess(
+        self,
+        image: ImageInput,
+        do_resize: bool,
+        do_rescale: bool,
+        do_normalize: bool,
+        size: Optional[Dict[str, int]] = None,
+        resample: PILImageResampling = None,
+        rescale_factor: Optional[float] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ):
+        if do_rescale:
+            image = self.rescale(
+                image=image, scale=rescale_factor, input_data_format=input_data_format
+            )
+
+        if do_normalize:
+            image = self.normalize(
+                image=image,
+                mean=image_mean,
+                std=image_std,
+                input_data_format=input_data_format,
+            )
+
+        return image
+
+    def _preprocess_image(
+        self,
+        image: ImageInput,
+        do_resize: bool = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ) -> np.ndarray:
+        """Preprocesses a single image."""
+        # All transformations expect numpy arrays.
+        if input_data_format is None:
+            input_data_format = infer_channel_dimension_format(image)
+
+        image = self._preprocess(
+            image=image,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+            do_rescale=do_rescale,
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            input_data_format=input_data_format,
+        )
+        if data_format is not None:
+            image = to_channel_dimension_format(
+                image, data_format, input_channel_dim=input_data_format
+            )
+        return image
+
+    def __call__(self, images, segmentation_maps=None, **kwargs):
+        """
+        Preprocesses a batch of images and optionally segmentation maps.
+
+        Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be
+        passed in as positional arguments.
+        """
+        return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        segmentation_maps: Optional[ImageInput] = None,
+        do_resize: Optional[bool] = None,
+        size: Optional[Dict[str, int]] = None,
+        resample: PILImageResampling = None,
+        do_rescale: Optional[bool] = None,
+        rescale_factor: Optional[float] = None,
+        do_normalize: Optional[bool] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_reduce_labels: Optional[bool] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: ChannelDimension = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> PIL.Image.Image:
+        """
+        Preprocess an image or batch of images.
+
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            segmentation_maps (`ImageInput`, *optional*):
+                Segmentation map to preprocess.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after `resize` is applied.
+            resample (`int`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
+                has an effect if `do_resize` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image values between [0 - 1].
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation.
+            do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
+                Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
+                is used for background, and background itself is not included in all classes of a dataset (e.g.
+                ADE20k). The background label will be replaced by 255.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                    - Unset: Return a list of `np.ndarray`.
+                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        resample = resample if resample is not None else self.resample
+        size = size if size is not None else self.size
+        rescale_factor = (
+            rescale_factor if rescale_factor is not None else self.rescale_factor
+        )
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+
+        images = make_list_of_images(images)
+        images = [
+            self._preprocess_image(
+                image=img,
+                do_resize=do_resize,
+                resample=resample,
+                size=size,
+                do_rescale=do_rescale,
+                rescale_factor=rescale_factor,
+                do_normalize=do_normalize,
+                image_mean=image_mean,
+                image_std=image_std,
+                data_format=data_format,
+                input_data_format=input_data_format,
+            )
+            for img in images
+        ]
+
+        data = {"pixel_values": images}
+        return BatchFeature(data=data, tensor_type=return_tensors)
diff --git a/surya/detection/schema.py b/surya/detection/schema.py
new file mode 100644
index 0000000..5e32088
--- /dev/null
+++ b/surya/detection/schema.py
@@ -0,0 +1,12 @@
+from typing import List, Optional, Any
+
+from pydantic import BaseModel
+
+from surya.common.polygon import PolygonBox
+
+
+class TextDetectionResult(BaseModel):
+    bboxes: List[PolygonBox]
+    heatmap: Optional[Any]
+    affinity_map: Optional[Any]
+    image_bbox: List[float]
diff --git a/surya/detection/util.py b/surya/detection/util.py
new file mode 100644
index 0000000..594cf4d
--- /dev/null
+++ b/surya/detection/util.py
@@ -0,0 +1,36 @@
+import math
+from PIL import ImageOps
+
+from surya.settings import settings
+
+
+def get_total_splits(image_size, height):
+    img_height = list(image_size)[1]
+    max_height = settings.DETECTOR_IMAGE_CHUNK_HEIGHT
+    if img_height > max_height:
+        num_splits = math.ceil(img_height / height)
+        return num_splits
+    return 1
+
+
+def split_image(img, height):
+    # This will not modify/return the original image - it will either crop, or copy the image
+    img_height = list(img.size)[1]
+    max_height = settings.DETECTOR_IMAGE_CHUNK_HEIGHT
+    if img_height > max_height:
+        num_splits = math.ceil(img_height / height)
+        splits = []
+        split_heights = []
+        for i in range(num_splits):
+            top = i * height
+            bottom = (i + 1) * height
+            if bottom > img_height:
+                bottom = img_height
+            cropped = img.crop((0, top, img.size[0], bottom))
+            chunk_height = bottom - top
+            if chunk_height < height:
+                cropped = ImageOps.pad(cropped, (img.size[0], height), color=255, centering=(0, 0))
+            splits.append(cropped)
+            split_heights.append(chunk_height)
+        return splits, split_heights
+    return [img.copy()], [img_height]
diff --git a/surya/fast_layout/__init__.py b/surya/fast_layout/__init__.py
new file mode 100644
index 0000000..29f2dab
--- /dev/null
+++ b/surya/fast_layout/__init__.py
@@ -0,0 +1,153 @@
+"""FastLayoutPredictor — rf-detr page-layout detector (torch; cpu/mps/cuda).
+
+Drop-in alternative to surya.layout.LayoutPredictor: same LayoutResult/LayoutBox output, but
+a lightweight rf-detr object detector instead of the VLM. Labels are canonicalized through the
+same LAYOUT_PRED_RELABEL map the VLM layout model uses, so downstream consumers (marker) are unchanged.
+"""
+
+from __future__ import annotations
+
+import threading
+from typing import List, Optional
+
+from PIL import Image
+
+from surya.common.rfdetr_torch import load_detector, resolve_model_dir
+from surya.common.order.predictor import load_order_predictor
+from surya.layout.label import LAYOUT_PRED_RELABEL
+from surya.layout.schema import LayoutBox, LayoutResult
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+def _poly(b):
+    x0, y0, x1, y1 = b
+    return [[x0, y0], [x1, y0], [x1, y1], [x0, y1]]
+
+
+class FastLayoutPredictor:
+    def __init__(
+        self,
+        checkpoint: Optional[str] = None,
+        num_threads: Optional[int] = None,
+        use_order: Optional[bool] = None,
+    ):
+        model_dir = resolve_model_dir(
+            checkpoint or settings.FAST_LAYOUT_MODEL_CHECKPOINT
+        )
+        self.model = load_detector(
+            model_dir, num_threads=num_threads, device=settings.FAST_DETECTOR_DEVICE
+        )
+        # Learned reading-order head (cross-attends to the detector's encoder feature map).
+        # use_order (or settings.FAST_LAYOUT_USE_ORDER) sets the per-instance default,
+        # and each __call__ can override it, so callers that mostly don't need order
+        # (e.g. marker, which orders from the PDF text layer) can still request it for
+        # specific pages. The head is loaded lazily on the first call that wants it;
+        # boxes come back in raster order (top-to-bottom, left-to-right) when it's off.
+        self.use_order = (
+            settings.FAST_LAYOUT_USE_ORDER if use_order is None else use_order
+        )
+        self.order = None
+        self._order_load_attempted = False
+        self._order_load_lock = threading.Lock()
+        self._disable_tqdm = settings.DISABLE_TQDM
+
+    def _load_order(self):
+        # Lock so a concurrent caller during the (slow, possibly-downloading)
+        # first load waits for the result instead of seeing a half-initialized
+        # None and silently falling back to raster order.
+        with self._order_load_lock:
+            if not self._order_load_attempted:
+                self._order_load_attempted = True
+                self.order = load_order_predictor(
+                    device=settings.FAST_DETECTOR_DEVICE or "cpu"
+                )
+                if self.order is None:
+                    logger.warning(
+                        "Reading-order model not available; falling back to raster sort "
+                        "(top-to-bottom, left-to-right) for all pages."
+                    )
+        return self.order
+
+    def to(
+        self, *args, **kwargs
+    ):  # API parity with other predictors (no-op; device is set at load)
+        return
+
+    def __call__(
+        self,
+        images: List[Image.Image],
+        threshold: Optional[float] = None,
+        batch_size: Optional[int] = None,
+        use_order: Optional[bool] = None,
+    ) -> List[LayoutResult]:
+        if not images:
+            return []
+        threshold = (
+            settings.FAST_LAYOUT_CONFIDENCE_THRESHOLD
+            if threshold is None
+            else threshold
+        )
+        batch_size = batch_size or settings.FAST_LAYOUT_BATCH_SIZE or 8
+        use_order = self.use_order if use_order is None else use_order
+        order = self._load_order() if use_order else None
+        want_feats = order is not None
+        detections = self.model.detect(
+            images,
+            threshold=threshold,
+            batch_size=batch_size,
+            return_features=want_feats,
+        )
+
+        results: List[LayoutResult] = []
+        for image, dets in zip(images, detections):
+            # Reading order: the learned AR head (cross-attends to the encoder feature map) when
+            # available, else a top-to-bottom / left-to-right raster sort.
+            feats = getattr(dets, "features", None)
+            if order is not None and feats is not None and dets:
+                positions = order.order_page(
+                    feats,
+                    [d["bbox"] for d in dets],
+                    [d["label"] for d in dets],
+                    image.width,
+                    image.height,
+                )
+            else:
+                # Raster sort: the normal path when order is off for this call.
+                if order is not None and feats is None and dets:
+                    # Order model loaded but no feature map came back — it
+                    # should have run but didn't. Surface this; the "model
+                    # never loaded" case is logged once at first load.
+                    logger.warning(
+                        "Reading-order model loaded but detector returned no feature map; "
+                        "falling back to raster sort for this page."
+                    )
+                raster = sorted(
+                    range(len(dets)),
+                    key=lambda i: (dets[i]["bbox"][1], dets[i]["bbox"][0]),
+                )
+                positions = [0] * len(dets)
+                for rank, i in enumerate(raster):
+                    positions[i] = rank
+            boxes = []
+            for d, pos in zip(dets, positions):
+                raw = d["label"]
+                boxes.append(
+                    LayoutBox(
+                        polygon=_poly(d["bbox"]),
+                        label=LAYOUT_PRED_RELABEL.get(raw, raw),
+                        raw_label=raw,
+                        position=pos,
+                        confidence=d["score"],
+                    )
+                )
+            boxes.sort(key=lambda b: b.position)
+            results.append(
+                LayoutResult(
+                    bboxes=boxes,
+                    image_bbox=[0.0, 0.0, float(image.width), float(image.height)],
+                )
+            )
+        return results
diff --git a/surya/inference/__init__.py b/surya/inference/__init__.py
new file mode 100644
index 0000000..05cab25
--- /dev/null
+++ b/surya/inference/__init__.py
@@ -0,0 +1,119 @@
+"""Surya inference manager.
+
+One process owns one SuryaInferenceManager. The manager wraps a single backend
+(vllm | llamacpp) which speaks OpenAI-compatible chat completions.
+
+Predictors take the manager via explicit injection at construction time.
+"""
+
+from __future__ import annotations
+
+import os
+import shutil
+import subprocess
+from typing import List, Optional
+
+from surya.inference.backends.base import Backend
+from surya.inference.schema import BatchInputItem, BatchOutputItem
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+def _has_nvidia_gpu() -> bool:
+    """True if an NVIDIA GPU is present on this host.
+
+    We deliberately do *not* rely solely on ``torch.cuda.is_available()``:
+    the installed torch wheel's CUDA build can be newer than the host driver
+    (PyPI's default wheel tracks the latest CUDA), in which case torch reports
+    no CUDA even on a perfectly good GPU box. That would silently route us to
+    the CPU llama.cpp backend on a machine that should be running vllm. So we
+    take torch's word when it *does* see CUDA, and otherwise fall back to
+    probing for the GPU directly via ``nvidia-smi``.
+    """
+    try:
+        import torch
+
+        if torch.cuda.is_available():
+            return True
+    except Exception:
+        pass
+
+    # Instant, load-independent check: the NVIDIA device node only exists when
+    # a GPU + driver are present. Preferred over nvidia-smi because nvidia-smi
+    # can block for several seconds on a GPU under heavy load, which would race
+    # a timeout and falsely report "no GPU".
+    if os.path.exists("/dev/nvidia0"):
+        return True
+
+    nvidia_smi = shutil.which("nvidia-smi")
+    if not nvidia_smi:
+        return False
+    try:
+        result = subprocess.run(
+            [nvidia_smi, "-L"], capture_output=True, text=True, timeout=15
+        )
+        return result.returncode == 0 and "GPU" in result.stdout
+    except Exception:
+        return False
+
+
+def _autodetect_backend() -> str:
+    if settings.SURYA_INFERENCE_BACKEND:
+        return settings.SURYA_INFERENCE_BACKEND
+    # NVIDIA GPU → vllm, mps/cpu → llamacpp
+    if _has_nvidia_gpu():
+        return "vllm"
+    return "llamacpp"
+
+
+def _build_backend(method: str) -> Backend:
+    method = method.lower()
+    if method == "vllm":
+        from surya.inference.backends.vllm import VllmBackend
+
+        return VllmBackend()
+    if method == "llamacpp":
+        from surya.inference.backends.llamacpp import LlamaCppBackend
+
+        return LlamaCppBackend()
+    raise ValueError(
+        f"Unknown inference backend {method!r}. Supported: 'vllm', 'llamacpp'."
+    )
+
+
+class SuryaInferenceManager:
+    """Single entry point for VLM inference. Construct once per process."""
+
+    def __init__(self, method: Optional[str] = None, lazy: bool = True):
+        self.method = method or _autodetect_backend()
+        self.backend: Backend = _build_backend(self.method)
+        if not lazy:
+            self.backend.start()
+
+    def start(self) -> None:
+        self.backend.start()
+
+    def stop(self) -> None:
+        self.backend.stop()
+
+    def generate(self, batch: List[BatchInputItem]) -> List[BatchOutputItem]:
+        return self.backend.generate(batch)
+
+    def capacity(self) -> int:
+        """Server concurrency capacity (see Backend.capacity)."""
+        return self.backend.capacity()
+
+
+# Module-level lazy singleton for callers that don't want explicit construction
+# (notebooks, ad-hoc scripts). Surya's own models.py and marker should use
+# explicit construction.
+_default_manager: Optional[SuryaInferenceManager] = None
+
+
+def get_default_manager() -> SuryaInferenceManager:
+    global _default_manager
+    if _default_manager is None:
+        _default_manager = SuryaInferenceManager()
+    return _default_manager
diff --git a/surya/inference/backends/__init__.py b/surya/inference/backends/__init__.py
new file mode 100644
index 0000000..f81cf02
--- /dev/null
+++ b/surya/inference/backends/__init__.py
@@ -0,0 +1,2 @@
+from surya.inference.backends.base import Backend as Backend
+from surya.inference.backends.base import ServerHandle as ServerHandle
diff --git a/surya/inference/backends/base.py b/surya/inference/backends/base.py
new file mode 100644
index 0000000..1f99dd2
--- /dev/null
+++ b/surya/inference/backends/base.py
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import List
+
+
+from surya.inference.schema import BatchInputItem, BatchOutputItem
+
+
+@dataclass
+class ServerHandle:
+    base_url: str  # e.g. "http://127.0.0.1:8765/v1"
+    model_name: str  # what gets passed in OpenAI `model` field
+    spawned_by_us: bool  # if True, we manage atexit cleanup
+
+
+class Backend:
+    """Abstract backend. Concrete backends own server lifecycle + generation."""
+
+    name: str  # "vllm" | "llamacpp"
+
+    def start(self) -> ServerHandle:
+        """Idempotent: probe → attach if alive, else spawn. Returns handle."""
+        raise NotImplementedError
+
+    def stop(self) -> None:
+        """Stop the server if we spawned it."""
+        raise NotImplementedError
+
+    def generate(self, batch: List[BatchInputItem]) -> List[BatchOutputItem]:
+        raise NotImplementedError
+
+    def capacity(self) -> int:
+        """Server-side concurrency capacity (concurrent requests the server can
+        actively process). Callers sizing multi-process client pools should
+        target an aggregate in-flight count of ~this value."""
+        return 8
diff --git a/surya/inference/backends/llamacpp.py b/surya/inference/backends/llamacpp.py
new file mode 100644
index 0000000..820ff72
--- /dev/null
+++ b/surya/inference/backends/llamacpp.py
@@ -0,0 +1,215 @@
+"""llama.cpp backend: spawns the upstream `llama-server` binary natively.
+
+Install:
+- macOS:    brew install llama.cpp     (Metal build, MPS)
+- Linux:    brew install llama.cpp  OR  github.com/ggml-org/llama.cpp/releases
+"""
+
+from __future__ import annotations
+
+import os
+import shutil
+import subprocess
+from pathlib import Path
+from typing import List, Optional
+
+from huggingface_hub import hf_hub_download
+from openai import OpenAI
+
+from surya.inference.backends.base import Backend, ServerHandle
+from surya.inference.backends.openai_client import chat_completions_batch
+from surya.inference.backends.spawn import (
+    SpawnHandle,
+    SpawnError,
+    attach_or_spawn,
+)
+from surya.inference.schema import BatchInputItem, BatchOutputItem
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+def _resolve_llama_server_binary() -> str:
+    binary = settings.LLAMA_CPP_BINARY
+    if binary and os.path.isfile(binary):
+        return binary
+    found = shutil.which(binary or "llama-server")
+    if found:
+        return found
+    raise SpawnError(
+        "llama-server binary not found. Install with:\n"
+        "  macOS:  brew install llama.cpp\n"
+        "  Linux:  brew install llama.cpp  OR download from\n"
+        "          https://github.com/ggml-org/llama.cpp/releases\n"
+        "Or set LLAMA_CPP_BINARY in your env to the binary path."
+    )
+
+
+def _download_gguf_files() -> tuple[str, str]:
+    """Download model + mmproj GGUFs from HF Hub. Returns local paths."""
+    repo = settings.SURYA_GGUF_REPO
+    model_file = settings.SURYA_GGUF_MODEL_FILE
+    mmproj_file = settings.SURYA_GGUF_MMPROJ_FILE
+    logger.info(f"Downloading {model_file} and {mmproj_file} from {repo}")
+    model_path = hf_hub_download(repo_id=repo, filename=model_file)
+    mmproj_path = hf_hub_download(repo_id=repo, filename=mmproj_file)
+    return model_path, mmproj_path
+
+
+def _health_url(port: int) -> str:
+    return f"http://{settings.SURYA_INFERENCE_HOST}:{port}"
+
+
+def _openai_url(port: int) -> str:
+    return f"http://{settings.SURYA_INFERENCE_HOST}:{port}/v1"
+
+
+class LlamaCppBackend(Backend):
+    name = "llamacpp"
+    # Conservative default slot count - each parallel slot consumes KV cache,
+    # so llama.cpp can't fan out as wide as a server-class GPU under vllm.
+    DEFAULT_PARALLEL = 8
+
+    def __init__(self):
+        self.handle: Optional[ServerHandle] = None
+        self._client: Optional[OpenAI] = None
+
+    def start(self) -> ServerHandle:
+        if self.handle is not None:
+            return self.handle
+
+        # If user pinned an external server, attach without spawning.
+        # No binary or GGUF download needed in that case.
+        if settings.SURYA_INFERENCE_URL:
+            spawned = attach_or_spawn(
+                backend=self.name,
+                expected_model_name=settings.SURYA_MODEL_CHECKPOINT,
+                spawn_fn=lambda port: SpawnHandle(
+                    pid=None, cleanup_id="", cleanup_kind="process"
+                ),  # never called
+                health_url_for=_health_url,
+                openai_url_for=_openai_url,
+                startup_timeout=settings.SURYA_INFERENCE_STARTUP_TIMEOUT,
+            )
+            self.handle = ServerHandle(
+                base_url=spawned.base_url,
+                model_name=spawned.model_name,
+                spawned_by_us=spawned.spawned_by_us,
+            )
+            self._client = OpenAI(api_key="EMPTY", base_url=self.handle.base_url)
+            return self.handle
+
+        binary = _resolve_llama_server_binary()
+
+        # Pre-download GGUFs so the spawn doesn't race the download
+        if (
+            settings.SURYA_GGUF_LOCAL_MODEL_PATH
+            and settings.SURYA_GGUF_LOCAL_MMPROJ_PATH
+        ):
+            model_path = settings.SURYA_GGUF_LOCAL_MODEL_PATH
+            mmproj_path = settings.SURYA_GGUF_LOCAL_MMPROJ_PATH
+        else:
+            model_path, mmproj_path = _download_gguf_files()
+
+        # Total KV-cache budget. llama-server divides --ctx-size across
+        # --parallel slots, so a too-small total silently truncates outputs
+        # once each slot's share fills. Scale with parallel by default;
+        # SURYA_INFERENCE_CTX_SIZE overrides to a fixed value if set.
+        parallel = settings.SURYA_INFERENCE_PARALLEL or self.DEFAULT_PARALLEL
+        per_slot = settings.SURYA_INFERENCE_CTX_PER_SLOT
+        ctx_size = settings.SURYA_INFERENCE_CTX_SIZE
+        if ctx_size is None:
+            ctx_size = max(16384, parallel * per_slot)
+        effective_per_slot = ctx_size // max(parallel, 1)
+        logger.info(
+            f"llama-server ctx-size={ctx_size} "
+            f"(~{effective_per_slot}/slot × {parallel} parallel slots)"
+        )
+        if effective_per_slot < per_slot:
+            logger.warning(
+                f"per-slot ctx ({effective_per_slot}) is below recommended "
+                f"{per_slot}; outputs may truncate. Raise "
+                f"SURYA_INFERENCE_CTX_SIZE or SURYA_INFERENCE_CTX_PER_SLOT, "
+                f"or lower SURYA_INFERENCE_PARALLEL."
+            )
+
+        def spawn_fn(port: int) -> SpawnHandle:
+            cmd = [
+                binary,
+                "-m",
+                model_path,
+                "--mmproj",
+                mmproj_path,
+                "-ngl",
+                str(settings.LLAMA_CPP_NGL),
+                "--host",
+                settings.SURYA_INFERENCE_HOST,
+                "--port",
+                str(port),
+                "--parallel",
+                str(parallel),
+                "--ctx-size",
+                str(ctx_size),
+                "--no-mmproj-offload" if settings.LLAMA_CPP_NO_MMPROJ_OFFLOAD else "",
+                "--alias",
+                settings.SURYA_MODEL_CHECKPOINT,
+                "--jinja",
+            ]
+            cmd = [c for c in cmd if c]
+            for extra in (settings.LLAMA_CPP_EXTRA_ARGS or "").split():
+                cmd.append(extra)
+            logger.info(f"Spawning: {' '.join(cmd)}")
+            log_path = Path("~/.cache/datalab/surya/llamacpp_server.log").expanduser()
+            log_path.parent.mkdir(parents=True, exist_ok=True)
+            log_fp = open(log_path, "ab")
+            proc = subprocess.Popen(
+                cmd,
+                stdout=log_fp,
+                stderr=subprocess.STDOUT,
+                start_new_session=True,
+            )
+            return SpawnHandle(
+                pid=proc.pid, cleanup_id=str(proc.pid), cleanup_kind="process"
+            )
+
+        spawned = attach_or_spawn(
+            backend=self.name,
+            expected_model_name=settings.SURYA_MODEL_CHECKPOINT,
+            spawn_fn=spawn_fn,
+            health_url_for=_health_url,
+            openai_url_for=_openai_url,
+            startup_timeout=settings.SURYA_INFERENCE_STARTUP_TIMEOUT,
+        )
+        self.handle = ServerHandle(
+            base_url=spawned.base_url,
+            model_name=spawned.model_name,
+            spawned_by_us=spawned.spawned_by_us,
+        )
+        self._client = OpenAI(
+            api_key="EMPTY",
+            base_url=self.handle.base_url,
+        )
+        return self.handle
+
+    def capacity(self) -> int:
+        from surya.settings import settings
+
+        return settings.SURYA_INFERENCE_PARALLEL or self.DEFAULT_PARALLEL
+
+    def stop(self) -> None:
+        # atexit handler in spawn.py owns cleanup; nothing to do here.
+        self.handle = None
+        self._client = None
+
+    def generate(self, batch: List[BatchInputItem]) -> List[BatchOutputItem]:
+        if self.handle is None or self._client is None:
+            self.start()
+        return chat_completions_batch(
+            batch,
+            client=self._client,
+            model_name=self.handle.model_name,
+            timeout=settings.SURYA_INFERENCE_TIMEOUT_SECONDS,
+            max_workers=settings.SURYA_INFERENCE_PARALLEL or self.DEFAULT_PARALLEL,
+            request_logprobs_default=settings.SURYA_INFERENCE_LOGPROBS,
+        )
diff --git a/surya/inference/backends/openai_client.py b/surya/inference/backends/openai_client.py
new file mode 100644
index 0000000..d3c90a4
--- /dev/null
+++ b/surya/inference/backends/openai_client.py
@@ -0,0 +1,215 @@
+"""Shared OpenAI-compatible chat completions client. Used by vllm + llama.cpp.
+
+Both servers expose `/v1/chat/completions` with the same request/response shape,
+so this module is the single point of HTTP contact for both backends.
+"""
+
+from __future__ import annotations
+
+import base64
+import io
+import math
+import time
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Optional
+
+from PIL import Image
+
+from surya.inference.prompts import PROMPT_MAPPING
+from surya.inference.schema import (
+    BatchInputItem,
+    BatchOutputItem,
+    GenerationResult,
+)
+from surya.inference.util import detect_repeat_token, scale_to_fit
+from surya.logging import get_logger
+
+logger = get_logger()
+
+
+def encode_image_b64(image: Image.Image) -> str:
+    buf = io.BytesIO()
+    image.save(buf, format="PNG")
+    return base64.b64encode(buf.getvalue()).decode("ascii")
+
+
+def _build_messages(image: Image.Image, prompt: str):
+    image_b64 = encode_image_b64(image)
+    return [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/png;base64,{image_b64}"},
+                },
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+
+
+def _mean_token_prob(logprobs_content) -> Optional[float]:
+    if not logprobs_content:
+        return None
+    probs = []
+    for tok in logprobs_content:
+        lp = (
+            tok.get("logprob")
+            if isinstance(tok, dict)
+            else getattr(tok, "logprob", None)
+        )
+        if lp is None:
+            continue
+        probs.append(math.exp(lp))
+    if not probs:
+        return None
+    return sum(probs) / len(probs)
+
+
+def _generate_one(
+    item: BatchInputItem,
+    client,
+    model_name: str,
+    max_tokens_default: int,
+    temperature: float,
+    top_p: float,
+    timeout: float,
+    request_logprobs_default: bool,
+) -> GenerationResult:
+    prompt = item.prompt or PROMPT_MAPPING[item.prompt_type]
+    image = scale_to_fit(item.image)
+    messages = _build_messages(image, prompt)
+
+    max_tokens = item.max_tokens or max_tokens_default
+    request_logprobs = item.request_logprobs or request_logprobs_default
+    temp = item.temperature if item.temperature is not None else temperature
+    tp = item.top_p if item.top_p is not None else top_p
+
+    kwargs = dict(
+        model=model_name,
+        messages=messages,
+        max_tokens=max_tokens,
+        temperature=temp,
+        top_p=tp,
+        timeout=timeout,
+    )
+    if request_logprobs:
+        kwargs["logprobs"] = True
+
+    # Structured output: prefer OpenAI-standard response_format (works on both
+    # vllm and llama.cpp). Fall back to vllm's extra_body for guided_regex.
+    if item.guided_json is not None:
+        kwargs["response_format"] = {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "structured_output",
+                "schema": item.guided_json,
+                "strict": True,
+            },
+        }
+    if item.guided_regex is not None:
+        kwargs.setdefault("extra_body", {})["guided_regex"] = item.guided_regex
+
+    try:
+        completion = client.chat.completions.create(**kwargs)
+        raw = completion.choices[0].message.content or ""
+        token_count = completion.usage.completion_tokens if completion.usage else 0
+        mean_p = None
+        logprobs_content = None
+        if request_logprobs:
+            choice = completion.choices[0]
+            lp = getattr(choice, "logprobs", None)
+            if lp is not None:
+                content = getattr(lp, "content", None)
+                if content is not None:
+                    logprobs_content = [
+                        c.model_dump() if hasattr(c, "model_dump") else c
+                        for c in content
+                    ]
+                    mean_p = _mean_token_prob(content)
+        return GenerationResult(
+            raw=raw,
+            token_count=token_count,
+            error=False,
+            mean_token_prob=mean_p,
+            logprobs=logprobs_content,
+        )
+    except Exception as e:
+        logger.warning(f"Inference error: {e}")
+        return GenerationResult(raw="", token_count=0, error=True)
+
+
+def _should_retry(
+    result: GenerationResult,
+    retries: int,
+    max_retries: int,
+) -> bool:
+    if retries >= max_retries:
+        return False
+    if result.error:
+        return True
+    has_repeat = detect_repeat_token(result.raw) or (
+        len(result.raw) > 50 and detect_repeat_token(result.raw, cut_from_end=50)
+    )
+    return has_repeat
+
+
+def chat_completions_batch(
+    batch: List[BatchInputItem],
+    client,
+    model_name: str,
+    max_tokens_default: int = 2048,
+    temperature: float = 0.0,
+    top_p: float = 0.1,
+    timeout: float = 600.0,
+    max_workers: Optional[int] = None,
+    max_retries: int = 3,
+    request_logprobs_default: bool = True,
+) -> List[BatchOutputItem]:
+    """Run a batch of items through the chat completions endpoint with concurrent workers."""
+    if not batch:
+        return []
+    if max_workers is None:
+        max_workers = min(64, len(batch))
+
+    def _process(item: BatchInputItem) -> BatchOutputItem:
+        result = _generate_one(
+            item,
+            client=client,
+            model_name=model_name,
+            max_tokens_default=max_tokens_default,
+            temperature=temperature,
+            top_p=top_p,
+            timeout=timeout,
+            request_logprobs_default=request_logprobs_default,
+        )
+        retries = 0
+        while _should_retry(result, retries, max_retries):
+            backoff = 1.5 * (retries + 1) if result.error else 0
+            if backoff:
+                time.sleep(backoff)
+            retry_temp = min(temperature + 0.2 * (retries + 1), 0.8)
+            retry_top_p = 0.95 if not result.error else top_p
+            result = _generate_one(
+                item,
+                client=client,
+                model_name=model_name,
+                max_tokens_default=max_tokens_default,
+                temperature=retry_temp,
+                top_p=retry_top_p,
+                timeout=timeout,
+                request_logprobs_default=request_logprobs_default,
+            )
+            retries += 1
+        return BatchOutputItem(
+            raw=result.raw,
+            token_count=result.token_count,
+            error=result.error,
+            mean_token_prob=result.mean_token_prob,
+            logprobs=result.logprobs,
+            metadata=item.metadata,
+        )
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        return list(executor.map(_process, batch))
diff --git a/surya/inference/backends/spawn.py b/surya/inference/backends/spawn.py
new file mode 100644
index 0000000..e1d6ff1
--- /dev/null
+++ b/surya/inference/backends/spawn.py
@@ -0,0 +1,351 @@
+"""Server lifecycle: probe, filelock, sentinel, atexit cleanup.
+
+Pattern: probe `/health` → if alive return handle → else acquire lock, re-probe,
+spawn detached, write sentinel, register atexit kill (only the spawner cleans up).
+"""
+
+from __future__ import annotations
+
+import atexit
+import json
+import os
+import socket
+import subprocess
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Callable, Optional
+
+import httpx
+
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+def _cache_dir() -> Path:
+    base = Path(os.path.expanduser("~/.cache/datalab/surya"))
+    base.mkdir(parents=True, exist_ok=True)
+    return base
+
+
+def _sentinel_path(backend: str) -> Path:
+    return _cache_dir() / f"{backend}_server.json"
+
+
+def _lock_path(backend: str) -> Path:
+    return _cache_dir() / f"{backend}_server.lock"
+
+
+def find_free_port() -> int:
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("127.0.0.1", 0))
+        return s.getsockname()[1]
+
+
+def probe_health(base_url: str, timeout: float = 1.0) -> bool:
+    """Returns True if the server reports healthy at /health."""
+    try:
+        # llama.cpp returns 200 on /health when ready; vllm returns 200 on /health too.
+        with httpx.Client(timeout=timeout) as client:
+            r = client.get(f"{base_url}/health")
+            return r.status_code == 200
+    except Exception:
+        return False
+
+
+def wait_for_health(
+    base_url: str, total_timeout: float = 300.0, interval: float = 1.0
+) -> bool:
+    deadline = time.time() + total_timeout
+    while time.time() < deadline:
+        if probe_health(base_url):
+            return True
+        time.sleep(interval)
+    return False
+
+
+def probe_model_id(openai_base: str, timeout: float = 5.0) -> Optional[str]:
+    """Returns the model id reported by the running server, or None on failure."""
+    try:
+        with httpx.Client(timeout=timeout) as client:
+            r = client.get(f"{openai_base}/models")
+            r.raise_for_status()
+            data = r.json()
+            models = data.get("data") or []
+            if models:
+                return models[0].get("id")
+    except Exception:
+        return None
+    return None
+
+
+@dataclass
+class SpawnedServer:
+    base_url: str  # full openai base, e.g. "http://127.0.0.1:8765/v1"
+    health_url: str  # base for /health, e.g. "http://127.0.0.1:8765"
+    model_name: str  # what to pass as `model`
+    pid: Optional[int]
+    backend: str
+    spawned_by_us: bool
+
+
+class SpawnError(RuntimeError):
+    pass
+
+
+def _read_sentinel(backend: str) -> Optional[dict]:
+    p = _sentinel_path(backend)
+    if not p.exists():
+        return None
+    try:
+        return json.loads(p.read_text())
+    except Exception:
+        return None
+
+
+def _write_sentinel(backend: str, data: dict) -> None:
+    _sentinel_path(backend).write_text(json.dumps(data))
+
+
+def _delete_sentinel(backend: str) -> None:
+    p = _sentinel_path(backend)
+    if p.exists():
+        try:
+            p.unlink()
+        except Exception:
+            pass
+
+
+def _stop_process(pid: int, name: str) -> None:
+    try:
+        # Graceful first
+        os.kill(pid, 15)  # SIGTERM
+        for _ in range(20):
+            try:
+                os.kill(pid, 0)  # still alive?
+            except ProcessLookupError:
+                logger.info(f"Stopped {name} (pid {pid})")
+                return
+            time.sleep(0.5)
+        # Hard
+        os.kill(pid, 9)
+        logger.warning(f"Force-killed {name} (pid {pid})")
+    except ProcessLookupError:
+        pass
+    except Exception as e:
+        logger.warning(f"Failed to stop {name} (pid {pid}): {e}")
+
+
+def _capture_server_logs(handle: "SpawnHandle", tail: int = 100) -> str:
+    """Best-effort tail of a server's logs, for surfacing startup failures."""
+    try:
+        if handle.cleanup_kind == "docker":
+            r = subprocess.run(
+                ["docker", "logs", "--tail", str(tail), handle.cleanup_id],
+                capture_output=True,
+                text=True,
+                timeout=15,
+            )
+            return (r.stdout or "") + (r.stderr or "") or "(no docker logs)"
+        # llama.cpp process backend logs to this file (see llamacpp.py)
+        log_path = Path("~/.cache/datalab/surya/llamacpp_server.log").expanduser()
+        if log_path.exists():
+            lines = log_path.read_text(errors="replace").splitlines()
+            return "\n".join(lines[-tail:]) or "(empty log)"
+    except Exception as e:
+        return f"(could not capture logs: {e})"
+    return "(no logs available)"
+
+
+def _stop_docker_container(name: str) -> None:
+    try:
+        subprocess.run(
+            ["docker", "stop", name], check=False, capture_output=True, timeout=30
+        )
+        logger.info(f"Stopped docker container {name}")
+    except Exception as e:
+        logger.warning(f"Failed to stop docker container {name}: {e}")
+
+
+def attach_or_spawn(
+    backend: str,
+    expected_model_name: str,
+    spawn_fn: Callable[[int], "SpawnHandle"],
+    health_url_for: Callable[[int], str],
+    openai_url_for: Callable[[int], str],
+    startup_timeout: float = 600.0,
+) -> SpawnedServer:
+    """Generic attach-or-spawn with file lock and sentinel.
+
+    `spawn_fn(port)` must launch the server detached and return a SpawnHandle
+    with `pid` (int or None for docker) and a `cleanup_id` (e.g. container name).
+    """
+    # 0. If user pinned an external URL, attach without lock
+    if settings.SURYA_INFERENCE_URL:
+        base_url = settings.SURYA_INFERENCE_URL.rstrip("/")
+        health_url = base_url[: -len("/v1")] if base_url.endswith("/v1") else base_url
+        if not probe_health(health_url):
+            raise SpawnError(
+                f"SURYA_INFERENCE_URL={base_url} is not reachable at /health. "
+                "Start the server or unset the variable."
+            )
+        model_name = probe_model_id(base_url) or expected_model_name
+        if model_name != expected_model_name:
+            raise SpawnError(
+                f"Model mismatch at {base_url}: expected {expected_model_name!r}, got {model_name!r}. "
+                "Stop the running server or unset SURYA_INFERENCE_URL."
+            )
+        return SpawnedServer(
+            base_url=base_url,
+            health_url=health_url,
+            model_name=model_name,
+            pid=None,
+            backend=backend,
+            spawned_by_us=False,
+        )
+
+    # 1. Probe sentinel without lock
+    existing = _read_sentinel(backend)
+    if existing:
+        port = existing.get("port")
+        pid = existing.get("pid")
+        if port and probe_health(health_url_for(port)):
+            running_model = probe_model_id(openai_url_for(port)) or expected_model_name
+            if running_model != expected_model_name:
+                raise SpawnError(
+                    f"Existing {backend} server on port {port} serves {running_model!r}, "
+                    f"expected {expected_model_name!r}. Stop it before continuing."
+                )
+            logger.info(f"Attaching to existing {backend} server on port {port}")
+            return SpawnedServer(
+                base_url=openai_url_for(port),
+                health_url=health_url_for(port),
+                model_name=running_model,
+                pid=pid,
+                backend=backend,
+                spawned_by_us=False,
+            )
+        else:
+            _delete_sentinel(backend)
+
+    if not settings.SURYA_INFERENCE_AUTOSTART:
+        raise SpawnError(
+            f"No running {backend} server and SURYA_INFERENCE_AUTOSTART is False. "
+            "Set the variable to True or start the server manually."
+        )
+
+    # 2. Acquire filelock to prevent races
+    try:
+        from filelock import FileLock
+    except ImportError as e:
+        raise SpawnError(
+            "filelock is required for server spawn. pip install filelock"
+        ) from e
+
+    lock = FileLock(str(_lock_path(backend)), timeout=120)
+    with lock:
+        # Re-check sentinel inside the lock
+        existing = _read_sentinel(backend)
+        if existing:
+            port = existing.get("port")
+            if port and probe_health(health_url_for(port)):
+                running_model = (
+                    probe_model_id(openai_url_for(port)) or expected_model_name
+                )
+                if running_model != expected_model_name:
+                    raise SpawnError(
+                        f"Existing {backend} server on port {port} serves {running_model!r}, "
+                        f"expected {expected_model_name!r}."
+                    )
+                return SpawnedServer(
+                    base_url=openai_url_for(port),
+                    health_url=health_url_for(port),
+                    model_name=running_model,
+                    pid=existing.get("pid"),
+                    backend=backend,
+                    spawned_by_us=False,
+                )
+
+        # 3. Spawn fresh
+        port = settings.SURYA_INFERENCE_PORT or find_free_port()
+        logger.info(f"Spawning {backend} server on port {port}")
+        spawn_handle = spawn_fn(port)
+
+        # 4. Write sentinel
+        _write_sentinel(
+            backend,
+            {
+                "port": port,
+                "pid": spawn_handle.pid,
+                "model": expected_model_name,
+                "backend": backend,
+                "cleanup_id": spawn_handle.cleanup_id,
+                "cleanup_kind": spawn_handle.cleanup_kind,
+            },
+        )
+
+        # 5. Register atexit cleanup (only spawner). Skipped when keep-alive is
+        # set so the server outlives this process and later commands attach to
+        # it via the sentinel. (_cleanup is still callable below on startup
+        # failure, where we always tear a half-started server down.)
+        def _cleanup():
+            try:
+                if spawn_handle.cleanup_kind == "docker":
+                    _stop_docker_container(spawn_handle.cleanup_id)
+                elif spawn_handle.cleanup_kind == "process":
+                    if spawn_handle.pid:
+                        _stop_process(spawn_handle.pid, backend)
+            finally:
+                _delete_sentinel(backend)
+
+        if settings.SURYA_INFERENCE_KEEP_ALIVE:
+            logger.info(
+                f"keep-alive: {backend} server on port {port} will stay up "
+                f"after exit (cleanup_id={spawn_handle.cleanup_id!r})"
+            )
+        else:
+            atexit.register(_cleanup)
+
+        # 6. Wait for health
+        health_url = health_url_for(port)
+        if not wait_for_health(health_url, total_timeout=startup_timeout):
+            # Grab the server's own logs *before* cleanup tears the (--rm)
+            # container down, otherwise the actual failure reason is lost and
+            # all the caller sees is this timeout.
+            logs = _capture_server_logs(spawn_handle)
+            _cleanup()
+            raise SpawnError(
+                f"{backend} server failed to become healthy at {health_url} "
+                f"within {startup_timeout}s.\n"
+                f"--- last {backend} server logs ---\n{logs}"
+            )
+
+        # 7. Verify model name
+        running_model = probe_model_id(openai_url_for(port))
+        if running_model and running_model != expected_model_name:
+            logger.warning(
+                f"{backend} server reports model={running_model!r} "
+                f"but expected {expected_model_name!r}; using reported name."
+            )
+            expected_model_name = running_model
+
+        logger.info(
+            f"{backend} server ready on port {port} (model={expected_model_name})"
+        )
+        return SpawnedServer(
+            base_url=openai_url_for(port),
+            health_url=health_url,
+            model_name=expected_model_name,
+            pid=spawn_handle.pid,
+            backend=backend,
+            spawned_by_us=True,
+        )
+
+
+@dataclass
+class SpawnHandle:
+    pid: Optional[int]
+    cleanup_id: str  # container name for docker, str(pid) for process
+    cleanup_kind: str  # "docker" | "process"
diff --git a/surya/inference/backends/vllm.py b/surya/inference/backends/vllm.py
new file mode 100644
index 0000000..012e12d
--- /dev/null
+++ b/surya/inference/backends/vllm.py
@@ -0,0 +1,233 @@
+"""vllm backend: spawns the vllm/vllm-openai docker image with MTP=2."""
+
+from __future__ import annotations
+
+import json
+import math
+import os
+import shutil
+import subprocess
+from typing import List, Optional
+
+from openai import OpenAI
+
+from surya.inference.backends.base import Backend, ServerHandle
+from surya.inference.backends.openai_client import chat_completions_batch
+from surya.inference.backends.spawn import (
+    SpawnHandle,
+    SpawnError,
+    attach_or_spawn,
+)
+from surya.inference.schema import BatchInputItem, BatchOutputItem
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+# 24GB baseline (re-tune for surya-2 once benchmarks land)
+BASELINE_VRAM_GB = 24
+BASELINE_MAX_BATCHED_TOKENS = 8192
+BASELINE_MAX_NUM_SEQS = 32
+
+GPU_VRAM_GB = {
+    "b300": 270,
+    "b200": 180,
+    "h200": 141,
+    "h100": 80,
+    "a100-80": 80,
+    "a100": 40,
+    "a100-40": 40,
+    "l40s": 48,
+    "a10": 24,
+    "l4": 24,
+    "5090": 32,
+    "4090": 24,
+    "3090": 24,
+    "t4": 16,
+}
+
+
+def _gpu_settings(gpu: str) -> tuple[int, int]:
+    vram = GPU_VRAM_GB.get(gpu)
+    if vram is None:
+        available = ", ".join(sorted(GPU_VRAM_GB.keys()))
+        raise SpawnError(f"Unknown VLLM_GPU_TYPE {gpu!r}. Available: {available}")
+    ratio = vram / BASELINE_VRAM_GB
+    raw_tokens = BASELINE_MAX_BATCHED_TOKENS * ratio
+    max_batched_tokens = max(1024, 2 ** math.floor(math.log2(raw_tokens)))
+    max_num_seqs = max(8, (int(BASELINE_MAX_NUM_SEQS * ratio) // 8) * 8)
+    return max_batched_tokens, max_num_seqs
+
+
+def _resolve_docker_binary() -> str:
+    found = shutil.which("docker")
+    if found:
+        return found
+    raise SpawnError(
+        "docker binary not found. Install Docker (https://docs.docker.com/get-docker/) "
+        "and ensure the daemon is running."
+    )
+
+
+def _health_url(port: int) -> str:
+    return f"http://{settings.SURYA_INFERENCE_HOST}:{port}"
+
+
+def _openai_url(port: int) -> str:
+    return f"http://{settings.SURYA_INFERENCE_HOST}:{port}/v1"
+
+
+class VllmBackend(Backend):
+    name = "vllm"
+
+    # Cap auto-scaled client concurrency at the GPU-saturation knee. Measured
+    # layout throughput on a B200 (max_num_seqs=240): 48→96 gives +28%, but
+    # 96→240 only +5% — the GPU is compute-bound past ~96 concurrent, so extra
+    # requests just queue while adding thread/connection overhead. Smaller GPUs
+    # are bounded by their own (lower) max_num_seqs below this.
+    MAX_AUTO_PARALLEL = 96
+
+    def __init__(self):
+        self.handle: Optional[ServerHandle] = None
+        self._client: Optional[OpenAI] = None
+        # Server concurrency capacity, set when the server is configured;
+        # used to default client-side parallelism to the GPU's capability.
+        self._max_num_seqs: int = 8
+
+    def capacity(self) -> int:
+        return self._max_num_seqs
+
+    def _client_parallel(self) -> int:
+        if settings.SURYA_INFERENCE_PARALLEL is not None:
+            return settings.SURYA_INFERENCE_PARALLEL
+        return min(self._max_num_seqs, self.MAX_AUTO_PARALLEL)
+
+    def start(self) -> ServerHandle:
+        if self.handle is not None:
+            return self.handle
+
+        # Best-effort server capacity for defaulting client concurrency, even
+        # when attaching to an external server.
+        try:
+            self._max_num_seqs = _gpu_settings(settings.VLLM_GPU_TYPE)[1]
+        except SpawnError:
+            pass
+
+        # If user pinned an external server, attach without spawning docker.
+        if settings.SURYA_INFERENCE_URL:
+            spawned = attach_or_spawn(
+                backend=self.name,
+                expected_model_name=settings.SURYA_MODEL_CHECKPOINT,
+                spawn_fn=lambda port: SpawnHandle(
+                    pid=None, cleanup_id="", cleanup_kind="docker"
+                ),
+                health_url_for=_health_url,
+                openai_url_for=_openai_url,
+                startup_timeout=settings.SURYA_INFERENCE_STARTUP_TIMEOUT,
+            )
+            self.handle = ServerHandle(
+                base_url=spawned.base_url,
+                model_name=spawned.model_name,
+                spawned_by_us=spawned.spawned_by_us,
+            )
+            self._client = OpenAI(
+                api_key=settings.VLLM_API_KEY, base_url=self.handle.base_url
+            )
+            return self.handle
+
+        docker = _resolve_docker_binary()
+        max_batched_tokens, max_num_seqs = _gpu_settings(settings.VLLM_GPU_TYPE)
+        self._max_num_seqs = max_num_seqs
+
+        def spawn_fn(port: int) -> SpawnHandle:
+            container_name = f"surya-vllm-{port}"
+            hf_cache = os.path.expanduser(settings.DOCKER_HF_CACHE_PATH)
+            cmd = [
+                docker,
+                "run",
+                "--rm",
+                "-d",
+                "--name",
+                container_name,
+                "--runtime",
+                "nvidia",
+                "--gpus",
+                f"device={settings.VLLM_GPUS}",
+                "-v",
+                f"{hf_cache}:/root/.cache/huggingface",
+                "-p",
+                f"{port}:8000",
+                "--ipc=host",
+                settings.VLLM_DOCKER_IMAGE,
+                "--model",
+                settings.SURYA_MODEL_CHECKPOINT,
+                "--no-enforce-eager",
+                "--max-num-seqs",
+                str(max_num_seqs),
+                "--dtype",
+                settings.VLLM_DTYPE,
+                "--max-model-len",
+                str(settings.VLLM_MAX_MODEL_LEN),
+                "--max-num-batched-tokens",
+                str(max_batched_tokens),
+                "--gpu-memory-utilization",
+                str(settings.VLLM_GPU_MEMORY_UTILIZATION),
+                "--enable-prefix-caching",
+                "--mm-processor-kwargs",
+                json.dumps({"min_pixels": 3136, "max_pixels": 6291456}),
+                "--served-model-name",
+                settings.SURYA_MODEL_CHECKPOINT,
+            ]
+            if settings.VLLM_ENABLE_MTP:
+                spec_config = json.dumps(
+                    {
+                        "method": "mtp",
+                        "num_speculative_tokens": settings.VLLM_MTP_TOKENS,
+                    }
+                )
+                cmd.extend(["--speculative-config", spec_config])
+            for extra in (settings.VLLM_EXTRA_ARGS or "").split():
+                cmd.append(extra)
+            logger.info(f"Spawning: {' '.join(cmd)}")
+            result = subprocess.run(cmd, capture_output=True, text=True, check=False)
+            if result.returncode != 0:
+                raise SpawnError(f"docker run failed: {result.stderr or result.stdout}")
+            return SpawnHandle(
+                pid=None, cleanup_id=container_name, cleanup_kind="docker"
+            )
+
+        spawned = attach_or_spawn(
+            backend=self.name,
+            expected_model_name=settings.SURYA_MODEL_CHECKPOINT,
+            spawn_fn=spawn_fn,
+            health_url_for=_health_url,
+            openai_url_for=_openai_url,
+            startup_timeout=settings.SURYA_INFERENCE_STARTUP_TIMEOUT,
+        )
+        self.handle = ServerHandle(
+            base_url=spawned.base_url,
+            model_name=spawned.model_name,
+            spawned_by_us=spawned.spawned_by_us,
+        )
+        self._client = OpenAI(
+            api_key=settings.VLLM_API_KEY,
+            base_url=self.handle.base_url,
+        )
+        return self.handle
+
+    def stop(self) -> None:
+        self.handle = None
+        self._client = None
+
+    def generate(self, batch: List[BatchInputItem]) -> List[BatchOutputItem]:
+        if self.handle is None or self._client is None:
+            self.start()
+        return chat_completions_batch(
+            batch,
+            client=self._client,
+            model_name=self.handle.model_name,
+            timeout=settings.SURYA_INFERENCE_TIMEOUT_SECONDS,
+            max_workers=self._client_parallel(),
+            request_logprobs_default=settings.SURYA_INFERENCE_LOGPROBS,
+        )
diff --git a/surya/inference/parsers.py b/surya/inference/parsers.py
new file mode 100644
index 0000000..728d3a6
--- /dev/null
+++ b/surya/inference/parsers.py
@@ -0,0 +1,182 @@
+"""Parsers for the three task outputs."""
+
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass
+from typing import List, Tuple
+
+
+from surya.logging import get_logger
+
+logger = get_logger()
+
+
+# ---- Layout (LAYOUT_PROMPT) -------------------------------------------------
+
+
+@dataclass
+class ParsedLayoutBlock:
+    label: str
+    bbox: Tuple[float, float, float, float]  # 0-1000 normalized
+    count: int  # multiple of 50, model's token estimate
+
+
+_JSON_ARRAY_RE = re.compile(r"\[.*\]", re.DOTALL)
+
+
+def _strip_fences(text: str) -> str:
+    cleaned = text.strip()
+    if cleaned.startswith("```"):
+        cleaned = re.sub(r"^```[a-zA-Z]*\n", "", cleaned)
+        cleaned = re.sub(r"\n```\s*$", "", cleaned)
+    return cleaned
+
+
+def _coerce_bbox(bbox) -> Tuple[float, float, float, float]:
+    if isinstance(bbox, str):
+        parts = [float(x) for x in bbox.replace(",", " ").split()]
+    else:
+        parts = [float(x) for x in bbox]
+    if len(parts) != 4:
+        raise ValueError(f"Bad bbox: {bbox!r}")
+    return (parts[0], parts[1], parts[2], parts[3])
+
+
+def _coerce_count(value) -> int:
+    if value is None:
+        return 0
+    try:
+        return max(0, int(value))
+    except (TypeError, ValueError):
+        return 0
+
+
+def parse_layout(text: str) -> List[ParsedLayoutBlock]:
+    """Pull the JSON array out of LAYOUT_PROMPT output and convert to typed blocks.
+
+    Tolerates code fences, missing fields, and stringified bboxes.
+    """
+    cleaned = _strip_fences(text)
+    m = _JSON_ARRAY_RE.search(cleaned)
+    if not m:
+        raise ValueError(f"No JSON array found in layout output: {text[:500]!r}")
+    raw = json.loads(m.group(0))
+    out: List[ParsedLayoutBlock] = []
+    for item in raw:
+        try:
+            bbox = _coerce_bbox(item["bbox"])
+        except (KeyError, ValueError) as e:
+            logger.warning(f"Skipping layout block with bad bbox: {e}")
+            continue
+        label = str(item.get("label", "block"))
+        count = _coerce_count(item.get("count"))
+        out.append(ParsedLayoutBlock(label=label, bbox=bbox, count=count))
+    return out
+
+
+# ---- Table rec (TABLE_REC_PROMPT) ------------------------------------------
+
+
+@dataclass
+class ParsedTableElement:
+    label: str  # "Row" or "Col"
+    bbox: Tuple[float, float, float, float]
+
+
+def parse_table_rec(text: str) -> List[ParsedTableElement]:
+    """Parse JSON array of {label: "Row"|"Col", bbox: "x0 y0 x1 y1"} from
+    TABLE_REC_PROMPT output. Returns a flat list of Row + Col elements;
+    cell derivation is the caller's job."""
+    cleaned = _strip_fences(text)
+    m = _JSON_ARRAY_RE.search(cleaned)
+    if not m:
+        raise ValueError(f"No JSON array found in table_rec output: {text[:500]!r}")
+    raw = json.loads(m.group(0))
+    out: List[ParsedTableElement] = []
+    for item in raw:
+        label = str(item.get("label", "")).strip()
+        if label not in ("Row", "Col"):
+            continue
+        try:
+            bbox = _coerce_bbox(item["bbox"])
+        except (KeyError, ValueError):
+            continue
+        out.append(ParsedTableElement(label=label, bbox=bbox))
+    return out
+
+
+# ---- Block HTML (BLOCK_PROMPT for full table path / general block path) ---
+
+
+def clean_block_html(html: str) -> str:
+    """Light cleanup of model-emitted HTML for a single block.
+
+    Strips code fences, leading/trailing whitespace. Does NOT validate against
+    ALLOWED_TAGS — the model is expected to comply, and downstream consumers
+    can sanitize further if needed.
+    """
+    cleaned = _strip_fences(html).strip()
+    return cleaned
+
+
+# ---- Full-page fallback (HIGH_ACCURACY_BBOX_PROMPT) -----------------------
+
+
+@dataclass
+class ParsedFullPageBlock:
+    label: str
+    bbox: Tuple[float, float, float, float]  # 0-1000 normalized
+    html: str  # inner HTML of the wrapping div
+
+
+def parse_full_page_html(text: str) -> List[ParsedFullPageBlock]:
+    """Parse output of HIGH_ACCURACY_BBOX_PROMPT — top-level <div data-bbox=...
+    data-label=...>inner HTML</div> blocks. Returns one entry per top-level div."""
+    from bs4 import BeautifulSoup
+
+    cleaned = _strip_fences(text).strip()
+    if not cleaned:
+        return []
+    # The model outputs a sequence of top-level divs (no surrounding root).
+    # BeautifulSoup parses fine without one.
+    soup = BeautifulSoup(cleaned, "html.parser")
+    divs = soup.find_all("div", recursive=False)
+    out: List[ParsedFullPageBlock] = []
+    for div in divs:
+        label = div.get("data-label")
+        bbox_str = div.get("data-bbox")
+        if not label or not bbox_str:
+            continue
+        try:
+            parts = [float(x) for x in bbox_str.split()]
+        except ValueError:
+            continue
+        if len(parts) != 4:
+            continue
+        # Strip nested data-bbox attrs from the inner HTML so downstream
+        # consumers don't see model debug info on every child element.
+        for tag in div.find_all(attrs={"data-bbox": True}):
+            del tag["data-bbox"]
+        for tag in div.find_all(attrs={"data-label": True}):
+            del tag["data-label"]
+        inner = "".join(str(c) for c in div.contents).strip()
+        out.append(
+            ParsedFullPageBlock(
+                label=str(label),
+                bbox=(parts[0], parts[1], parts[2], parts[3]),
+                html=inner,
+            )
+        )
+    return out
+
+
+def denorm_bbox(bbox, img_w: int, img_h: int, scale: int = 1000):
+    x0, y0, x1, y1 = bbox
+    return (
+        x0 / scale * img_w,
+        y0 / scale * img_h,
+        x1 / scale * img_w,
+        y1 / scale * img_h,
+    )
diff --git a/surya/inference/prompts.py b/surya/inference/prompts.py
new file mode 100644
index 0000000..c9a1425
--- /dev/null
+++ b/surya/inference/prompts.py
@@ -0,0 +1,158 @@
+"""Prompt strings for surya2. The exact wording is the model's training-time
+contract — do not paraphrase without retraining."""
+
+from surya.inference.schema import PROMPT_TYPE_BLOCK as PROMPT_TYPE_BLOCK
+from surya.inference.schema import (
+    PROMPT_TYPE_HIGH_ACCURACY_BBOX as PROMPT_TYPE_HIGH_ACCURACY_BBOX,
+)
+from surya.inference.schema import PROMPT_TYPE_LAYOUT as PROMPT_TYPE_LAYOUT
+from surya.inference.schema import PROMPT_TYPE_TABLE_REC as PROMPT_TYPE_TABLE_REC
+
+ALLOWED_TAGS = [
+    "math",
+    "br",
+    "i",
+    "b",
+    "u",
+    "del",
+    "sup",
+    "sub",
+    "table",
+    "tr",
+    "td",
+    "p",
+    "th",
+    "div",
+    "pre",
+    "h1",
+    "h2",
+    "h3",
+    "h4",
+    "h5",
+    "ul",
+    "ol",
+    "li",
+    "input",
+    "a",
+    "span",
+    "img",
+    "hr",
+    "tbody",
+    "small",
+    "caption",
+    "strong",
+    "thead",
+    "big",
+    "code",
+    "chem",
+]
+
+ALLOWED_ATTRIBUTES = [
+    "class",
+    "colspan",
+    "rowspan",
+    "display",
+    "checked",
+    "type",
+    "border",
+    "value",
+    "style",
+    "href",
+    "alt",
+    "align",
+    "data-bbox",
+    "data-label",
+]
+
+# Block labels we don't run OCR on.
+SKIP_OCR_LABELS = {"Figure", "Image", "Diagram", "Blank-Page"}
+
+LAYOUT_PROMPT = (
+    "Output the layout of this image as JSON. Each entry is a dict with "
+    '"label", "bbox", and "count" fields. Bbox is x0 y0 x1 y1, normalized 0-1000.'
+)
+
+BLOCK_PROMPT = "OCR this block image to HTML."
+
+TABLE_REC_PROMPT = (
+    "Output the table rows then columns as JSON. Each entry is a dict with "
+    '"label" ("Row" or "Col") and "bbox" (x0 y0 x1 y1, normalized 0-1000).'
+)
+
+HIGH_ACCURACY_BBOX_PROMPT = (
+    "OCR this image to HTML. Each block is a div with data-label and data-bbox "
+    "(x0 y0 x1 y1, normalized 0-1000)."
+)
+
+
+PROMPT_MAPPING = {
+    "layout": LAYOUT_PROMPT,
+    "block": BLOCK_PROMPT,
+    "table_rec": TABLE_REC_PROMPT,
+    "high_accuracy_bbox": HIGH_ACCURACY_BBOX_PROMPT,
+}
+
+
+# JSON schema for LAYOUT_PROMPT — enforced via vllm guided decoding so the
+# model can't emit malformed JSON. bbox is a "x0 y0 x1 y1" string (model's
+# training-time format); count is a non-negative integer.
+LAYOUT_LABEL_SET = [
+    "Caption",
+    "Footnote",
+    "Equation-Block",
+    "List-Group",
+    "Page-Header",
+    "Page-Footer",
+    "Image",
+    "Section-Header",
+    "Table",
+    "Text",
+    "Complex-Block",
+    "Code-Block",
+    "Form",
+    "Table-Of-Contents",
+    "Figure",
+    "Chemical-Block",
+    "Diagram",
+    "Bibliography",
+    "Blank-Page",
+]
+
+LAYOUT_JSON_SCHEMA = {
+    "type": "array",
+    "maxItems": 200,
+    "items": {
+        "type": "object",
+        "properties": {
+            "label": {"type": "string", "enum": LAYOUT_LABEL_SET},
+            "bbox": {
+                "type": "string",
+                "pattern": r"^\d{1,4} \d{1,4} \d{1,4} \d{1,4}$",
+            },
+            "count": {"type": "integer", "minimum": 0, "maximum": 10000},
+        },
+        "required": ["label", "bbox", "count"],
+        "additionalProperties": False,
+    },
+}
+
+
+# JSON schema for TABLE_REC_PROMPT — array of {label: Row|Col, bbox: "x0 y0 x1 y1"}.
+TABLE_REC_LABEL_SET = ["Row", "Col"]
+
+TABLE_REC_JSON_SCHEMA = {
+    "type": "array",
+    "maxItems": 200,
+    "items": {
+        "type": "object",
+        "properties": {
+            "label": {"type": "string", "enum": TABLE_REC_LABEL_SET},
+            "bbox": {
+                "type": "string",
+                "pattern": r"^\d{1,4} \d{1,4} \d{1,4} \d{1,4}$",
+            },
+        },
+        "required": ["label", "bbox"],
+        "additionalProperties": False,
+    },
+}
diff --git a/surya/inference/schema.py b/surya/inference/schema.py
new file mode 100644
index 0000000..370b9f4
--- /dev/null
+++ b/surya/inference/schema.py
@@ -0,0 +1,49 @@
+from dataclasses import dataclass, field
+from typing import Any, List, Optional
+
+from PIL import Image
+
+
+PROMPT_TYPE_LAYOUT = "layout"
+PROMPT_TYPE_BLOCK = "block"
+PROMPT_TYPE_TABLE_REC = "table_rec"
+PROMPT_TYPE_HIGH_ACCURACY_BBOX = "high_accuracy_bbox"
+
+
+@dataclass
+class BatchInputItem:
+    image: Image.Image
+    prompt_type: str
+    prompt: Optional[str] = None  # If set, overrides the default prompt for prompt_type
+    max_tokens: Optional[int] = None
+    temperature: Optional[float] = (
+        None  # If set, overrides the backend default temperature
+    )
+    top_p: Optional[float] = None  # If set, overrides the backend default top_p
+    request_logprobs: bool = False
+    # vllm-native guided decoding — JSON schema, regex, or grammar string.
+    # When set, the server constrains the decode tokens to match the schema.
+    guided_json: Optional[dict] = None
+    guided_regex: Optional[str] = None
+    metadata: dict = field(default_factory=dict)  # Free-form, passes through to output
+
+
+@dataclass
+class GenerationResult:
+    raw: str
+    token_count: int
+    error: bool = False
+    # Mean of exp(logprob) across response tokens, if logprobs requested
+    mean_token_prob: Optional[float] = None
+    # Per-token logprobs (raw OpenAI-style content list), if requested - phase 2 use
+    logprobs: Optional[List[Any]] = None
+
+
+@dataclass
+class BatchOutputItem:
+    raw: str
+    token_count: int
+    error: bool
+    mean_token_prob: Optional[float] = None
+    logprobs: Optional[List[Any]] = None
+    metadata: dict = field(default_factory=dict)
diff --git a/surya/inference/util.py b/surya/inference/util.py
new file mode 100644
index 0000000..1691170
--- /dev/null
+++ b/surya/inference/util.py
@@ -0,0 +1,96 @@
+from typing import Tuple
+
+from PIL import Image
+
+
+def scale_to_fit(
+    img: Image.Image,
+    max_size: Tuple[int, int] = (3072, 2048),
+    min_size: Tuple[int, int] = (1792, 28),
+    grid_size: int = 28,
+) -> Image.Image:
+    resample_method = Image.Resampling.LANCZOS
+
+    width, height = img.size
+
+    if width <= 0 or height <= 0:
+        return img
+
+    original_ar = width / height
+    current_pixels = width * height
+    max_pixels = max_size[0] * max_size[1]
+    min_pixels = min_size[0] * min_size[1]
+
+    scale = 1.0
+    if current_pixels > max_pixels:
+        scale = (max_pixels / current_pixels) ** 0.5
+    elif current_pixels < min_pixels:
+        scale = (min_pixels / current_pixels) ** 0.5
+
+    w_blocks = max(1, round((width * scale) / grid_size))
+    h_blocks = max(1, round((height * scale) / grid_size))
+
+    while (w_blocks * h_blocks * grid_size * grid_size) > max_pixels:
+        if w_blocks == 1 and h_blocks == 1:
+            break
+
+        if w_blocks == 1:
+            h_blocks -= 1
+            continue
+        if h_blocks == 1:
+            w_blocks -= 1
+            continue
+
+        ar_w_loss = abs(((w_blocks - 1) / h_blocks) - original_ar)
+        ar_h_loss = abs((w_blocks / (h_blocks - 1)) - original_ar)
+
+        if ar_w_loss < ar_h_loss:
+            w_blocks -= 1
+        else:
+            h_blocks -= 1
+
+    new_width = w_blocks * grid_size
+    new_height = h_blocks * grid_size
+
+    if (new_width, new_height) == (width, height):
+        return img
+
+    return img.resize((new_width, new_height), resample=resample_method)
+
+
+def detect_repeat_token(
+    predicted_tokens: str,
+    base_max_repeats: int = 4,
+    window_size: int = 500,
+    cut_from_end: int = 0,
+    scaling_factor: float = 3.0,
+) -> bool:
+    if cut_from_end > 0:
+        predicted_tokens = predicted_tokens[:-cut_from_end]
+
+    for seq_len in range(1, window_size // 2 + 1):
+        candidate_seq = predicted_tokens[-seq_len:]
+
+        max_repeats = int(base_max_repeats * (1 + scaling_factor / seq_len))
+
+        repeat_count = 0
+        pos = len(predicted_tokens) - seq_len
+        if pos < 0:
+            continue
+
+        while pos >= 0:
+            if predicted_tokens[pos : pos + seq_len] == candidate_seq:
+                repeat_count += 1
+                pos -= seq_len
+            else:
+                break
+
+        if repeat_count > max_repeats:
+            return True
+
+    return False
+
+
+def image_token_budget(block_count: int, ceiling: int = 4096, floor: int = 64) -> int:
+    """Per-block max_tokens: count + 100, clamped to [floor, ceiling]."""
+    return min(max(block_count + 100, floor), ceiling)
diff --git a/surya/input/load.py b/surya/input/load.py
new file mode 100644
index 0000000..4b5fd27
--- /dev/null
+++ b/surya/input/load.py
@@ -0,0 +1,77 @@
+from typing import List
+import PIL
+
+from surya.input.processing import open_pdf, get_page_images
+from surya.logging import get_logger
+from surya.settings import settings
+import os
+import filetype
+from PIL import Image
+
+logger = get_logger()
+
+
+def get_name_from_path(path):
+    return os.path.basename(path).split(".")[0]
+
+
+def load_pdf(pdf_path, page_range: List[int] | None = None, dpi=settings.IMAGE_DPI):
+    doc = open_pdf(pdf_path)
+    last_page = len(doc)
+
+    if page_range:
+        assert all([0 <= page < last_page for page in page_range]), (
+            f"Invalid page range: {page_range}"
+        )
+    else:
+        page_range = list(range(last_page))
+
+    images = get_page_images(doc, page_range, dpi=dpi)
+    doc.close()
+    names = [get_name_from_path(pdf_path) for _ in page_range]
+    return images, names
+
+
+def load_image(image_path):
+    image = Image.open(image_path).convert("RGB")
+    name = get_name_from_path(image_path)
+    return [image], [name]
+
+
+def load_from_file(
+    input_path, page_range: List[int] | None = None, dpi=settings.IMAGE_DPI
+):
+    input_type = filetype.guess(input_path)
+    if input_type and input_type.extension == "pdf":
+        return load_pdf(input_path, page_range, dpi=dpi)
+    else:
+        return load_image(input_path)
+
+
+def load_from_folder(
+    folder_path, page_range: List[int] | None = None, dpi=settings.IMAGE_DPI
+):
+    image_paths = [
+        os.path.join(folder_path, image_name)
+        for image_name in os.listdir(folder_path)
+        if not image_name.startswith(".")
+    ]
+    image_paths = [ip for ip in image_paths if not os.path.isdir(ip)]
+
+    images = []
+    names = []
+    for path in image_paths:
+        extension = filetype.guess(path)
+        if extension and extension.extension == "pdf":
+            image, name = load_pdf(path, page_range, dpi=dpi)
+            images.extend(image)
+            names.extend(name)
+        else:
+            try:
+                image, name = load_image(path)
+                images.extend(image)
+                names.extend(name)
+            except PIL.UnidentifiedImageError:
+                logger.warning(f"Could not load image {path}")
+                continue
+    return images, names
diff --git a/surya/input/processing.py b/surya/input/processing.py
new file mode 100644
index 0000000..03a6003
--- /dev/null
+++ b/surya/input/processing.py
@@ -0,0 +1,17 @@
+from typing import List
+
+import pypdfium2
+
+from surya.settings import settings
+
+
+def open_pdf(pdf_filepath):
+    return pypdfium2.PdfDocument(pdf_filepath)
+
+
+def get_page_images(doc, indices: List, dpi=settings.IMAGE_DPI):
+    images = [
+        doc[i].render(scale=dpi / 72, draw_annots=False).to_pil() for i in indices
+    ]
+    images = [image.convert("RGB") for image in images]
+    return images
diff --git a/surya/layout/__init__.py b/surya/layout/__init__.py
new file mode 100644
index 0000000..d9decb7
--- /dev/null
+++ b/surya/layout/__init__.py
@@ -0,0 +1,137 @@
+from __future__ import annotations
+
+from typing import List, Optional
+
+from PIL import Image
+
+from surya.common.blank import is_blank_region
+from surya.inference import SuryaInferenceManager, get_default_manager
+from surya.inference.parsers import denorm_bbox, parse_layout
+from surya.inference.prompts import LAYOUT_JSON_SCHEMA, PROMPT_TYPE_LAYOUT
+from surya.inference.schema import BatchInputItem
+from surya.layout.label import LAYOUT_PRED_RELABEL, TEXT_LABELS
+from surya.layout.schema import LayoutBox, LayoutResult
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+class LayoutPredictor:
+    """Run LAYOUT_PROMPT on full pages, parse JSON, return LayoutResult per image."""
+
+    def __init__(self, manager: Optional[SuryaInferenceManager] = None):
+        self.manager = manager  # If None, get_default_manager() is used at call time
+        self._disable_tqdm = settings.DISABLE_TQDM
+
+    @property
+    def disable_tqdm(self) -> bool:
+        return self._disable_tqdm
+
+    @disable_tqdm.setter
+    def disable_tqdm(self, value: bool) -> None:
+        self._disable_tqdm = bool(value)
+
+    def to(self, *args, **kwargs):
+        # Manager-backed; .to() is a no-op for compatibility with BasePredictor callers.
+        return
+
+    def __call__(
+        self,
+        images: List[Image.Image],
+        target_image_sizes: Optional[List[tuple]] = None,
+        max_tokens: Optional[int] = None,
+    ) -> List[LayoutResult]:
+        """Run layout on a batch of images.
+
+        target_image_sizes: optional list of (width, height) tuples — if
+        provided, bboxes are denormalized to these sizes instead of each
+        input image's size. Useful when layout runs on a low-DPI render but
+        you want bboxes in the OCR image's coordinate space.
+        """
+        if not images:
+            return []
+        manager = self.manager or get_default_manager()
+
+        max_tokens = max_tokens or settings.SURYA_MAX_TOKENS_LAYOUT
+        guided = LAYOUT_JSON_SCHEMA if settings.SURYA_GUIDED_LAYOUT else None
+        batch = [
+            BatchInputItem(
+                image=img,
+                prompt_type=PROMPT_TYPE_LAYOUT,
+                max_tokens=max_tokens,
+                guided_json=guided,
+            )
+            for img in images
+        ]
+        outputs = manager.generate(batch)
+
+        if target_image_sizes is not None and len(target_image_sizes) != len(images):
+            raise ValueError("target_image_sizes must match images length")
+
+        results: List[LayoutResult] = []
+        for idx, (img, out) in enumerate(zip(images, outputs)):
+            if target_image_sizes is not None:
+                w, h = target_image_sizes[idx]
+            else:
+                w, h = img.size
+            page_bbox = [0, 0, float(w), float(h)]
+            if out.error or not out.raw:
+                results.append(
+                    LayoutResult(
+                        bboxes=[], image_bbox=page_bbox, raw=out.raw, error=True
+                    )
+                )
+                continue
+            try:
+                parsed = parse_layout(out.raw)
+            except Exception as e:
+                logger.warning(f"Layout parse failed: {e}; raw[:300]={out.raw[:300]!r}")
+                results.append(
+                    LayoutResult(
+                        bboxes=[], image_bbox=page_bbox, raw=out.raw, error=True
+                    )
+                )
+                continue
+
+            confidence = out.mean_token_prob if out.mean_token_prob is not None else 1.0
+            img_w, img_h = img.size
+            boxes: List[LayoutBox] = []
+            dropped_blank = 0
+            for blk in parsed:
+                canon = LAYOUT_PRED_RELABEL.get(blk.label, blk.label)
+                # Drop text-labeled blocks the model hallucinated over an
+                # essentially-blank region (mostly white OR near-uniform
+                # color). Visual blocks (Picture / Figure / Table / etc.)
+                # are allowed to be uniform — that's normal content.
+                if canon in TEXT_LABELS:
+                    img_bbox = denorm_bbox(
+                        blk.bbox, img_w, img_h, scale=settings.BBOX_SCALE
+                    )
+                    x0, y0, x1, y1 = (max(0, int(v)) for v in img_bbox)
+                    if x1 > x0 and y1 > y0:
+                        if is_blank_region(img.crop((x0, y0, x1, y1))):
+                            dropped_blank += 1
+                            continue
+                pixel_bbox = denorm_bbox(blk.bbox, w, h, scale=settings.BBOX_SCALE)
+                boxes.append(
+                    LayoutBox(
+                        polygon=list(pixel_bbox),
+                        label=canon,
+                        raw_label=blk.label,
+                        position=len(boxes),
+                        count=blk.count,
+                        confidence=confidence,
+                    )
+                )
+            if dropped_blank:
+                logger.info(
+                    f"dropped {dropped_blank} text-labeled layout block(s) over "
+                    f"blank/uniform regions"
+                )
+            results.append(
+                LayoutResult(
+                    bboxes=boxes, image_bbox=page_bbox, raw=out.raw, error=False
+                )
+            )
+        return results
diff --git a/surya/layout/label.py b/surya/layout/label.py
new file mode 100644
index 0000000..bb4c6e3
--- /dev/null
+++ b/surya/layout/label.py
@@ -0,0 +1,44 @@
+"""Surya2 layout labels emitted by the model + canonicalization to surya's
+public label vocabulary."""
+
+# Canonical text-bearing labels — used by blank-region filters to decide
+# which blocks may be dropped when their underlying image region is empty.
+# Excludes Picture/Figure/Diagram/Table/Form/Equation/etc., which can legitimately
+# contain whitespace or solid fills.
+TEXT_LABELS = frozenset(
+    {
+        "Text",
+        "SectionHeader",
+        "PageHeader",
+        "PageFooter",
+        "Caption",
+        "Footnote",
+        "Code",
+        "Bibliography",
+    }
+)
+
+
+# Canonicalize raw model labels to public surya label names. Marker and other
+# downstream consumers depend on these names.
+LAYOUT_PRED_RELABEL = {
+    "Caption": "Caption",
+    "Footnote": "Footnote",
+    "Equation-Block": "Equation",
+    "List-Group": "ListGroup",
+    "Page-Header": "PageHeader",
+    "Page-Footer": "PageFooter",
+    "Image": "Picture",
+    "Section-Header": "SectionHeader",
+    "Table": "Table",
+    "Text": "Text",
+    "Complex-Block": "Figure",
+    "Code-Block": "Code",
+    "Form": "Form",
+    "Table-Of-Contents": "TableOfContents",
+    "Figure": "Figure",
+    "Chemical-Block": "ChemicalBlock",
+    "Diagram": "Diagram",
+    "Bibliography": "Bibliography",
+    "Blank-Page": "BlankPage",
+}
diff --git a/surya/layout/schema.py b/surya/layout/schema.py
new file mode 100644
index 0000000..d58acdd
--- /dev/null
+++ b/surya/layout/schema.py
@@ -0,0 +1,19 @@
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+from surya.common.polygon import PolygonBox
+
+
+class LayoutBox(PolygonBox):
+    label: str  # canonicalized via LAYOUT_PRED_RELABEL
+    raw_label: str  # original model label, before canonicalization
+    position: int  # reading order index
+    count: int = 0  # model's token estimate for OCR output (multiple of 50)
+
+
+class LayoutResult(BaseModel):
+    bboxes: List[LayoutBox]
+    image_bbox: List[float]
+    raw: Optional[str] = None  # raw model output, useful for debugging
+    error: bool = False
diff --git a/surya/logging.py b/surya/logging.py
new file mode 100644
index 0000000..ee51dbd
--- /dev/null
+++ b/surya/logging.py
@@ -0,0 +1,27 @@
+import logging
+import warnings
+from surya.settings import settings
+
+
+def configure_logging():
+    logger = get_logger()
+
+    # Remove any existing handlers to prevent duplicates
+    for handler in logger.handlers[:]:
+        logger.removeHandler(handler)
+
+    # Add our handler
+    handler = logging.StreamHandler()
+    formatter = logging.Formatter("%(asctime)s [%(levelname)s] %(name)s: %(message)s")
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+
+    # Prevent propagation to parent loggers to avoid double logging
+    logger.propagate = False
+
+    logger.setLevel(settings.LOGLEVEL)
+    warnings.simplefilter(action="ignore", category=FutureWarning)
+
+
+def get_logger():
+    return logging.getLogger("surya")
diff --git a/surya/ocr_error/__init__.py b/surya/ocr_error/__init__.py
new file mode 100644
index 0000000..54e0756
--- /dev/null
+++ b/surya/ocr_error/__init__.py
@@ -0,0 +1,56 @@
+import math
+from typing import List, Optional
+
+from tqdm import tqdm
+
+from surya.common.predictor import BasePredictor
+from surya.ocr_error.loader import OCRErrorModelLoader
+from surya.ocr_error.model.config import ID2LABEL
+from surya.ocr_error.schema import OCRErrorDetectionResult
+from surya.settings import settings
+
+
+class OCRErrorPredictor(BasePredictor):
+    model_loader_cls = OCRErrorModelLoader
+    batch_size = settings.OCR_ERROR_BATCH_SIZE
+    default_batch_sizes = {"cpu": 8, "mps": 8, "cuda": 64}
+
+    def __call__(self, texts: List[str], batch_size: Optional[int] = None):
+        return self.batch_ocr_error_detection(texts, batch_size)
+
+    def batch_ocr_error_detection(
+        self, texts: List[str], batch_size: Optional[int] = None
+    ):
+        if batch_size is None:
+            batch_size = self.get_batch_size()
+
+        num_batches = math.ceil(len(texts) / batch_size)
+        texts_processed = self.processor(
+            texts, padding="longest", truncation=True, return_tensors="pt"
+        )
+        predictions = []
+        scores = []
+        for batch_idx in tqdm(
+            range(num_batches),
+            desc="Running OCR Error Detection",
+            disable=self.disable_tqdm,
+        ):
+            start_idx, end_idx = batch_idx * batch_size, (batch_idx + 1) * batch_size
+            batch_input_ids = texts_processed.input_ids[start_idx:end_idx].to(
+                self.model.device
+            )
+            batch_attention_mask = texts_processed.attention_mask[start_idx:end_idx].to(
+                self.model.device
+            )
+
+            with settings.INFERENCE_MODE():
+                pred = self.model(batch_input_ids, attention_mask=batch_attention_mask)
+                probs = pred.logits.softmax(dim=1)
+                predictions.extend(probs.argmax(dim=1).cpu().tolist())
+                scores.extend(probs[:, 1].cpu().tolist())
+
+        return OCRErrorDetectionResult(
+            texts=texts,
+            labels=[ID2LABEL[p] for p in predictions],
+            scores=scores,
+        )
diff --git a/surya/ocr_error/loader.py b/surya/ocr_error/loader.py
new file mode 100644
index 0000000..7defb11
--- /dev/null
+++ b/surya/ocr_error/loader.py
@@ -0,0 +1,81 @@
+from typing import Optional
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+from surya.common.load import ModelLoader
+from surya.common.s3 import S3DownloaderMixin, download_directory
+from surya.logging import get_logger
+from surya.settings import settings
+
+logger = get_logger()
+
+
+def _resolve_checkpoint(checkpoint: str) -> str:
+    """Resolve an ``s3://`` checkpoint to a local dir (downloading if needed);
+    pass hub ids / local paths through unchanged."""
+    if not checkpoint.startswith(S3DownloaderMixin.s3_prefix):
+        return checkpoint
+    local_path = S3DownloaderMixin.get_local_path(checkpoint)
+    remote = checkpoint.replace(S3DownloaderMixin.s3_prefix, "")
+    retries, delay, attempt = 3, 5, 0
+    while attempt < retries:
+        try:
+            download_directory(remote, local_path)
+            break
+        except Exception as e:  # noqa: BLE001 - retried below
+            attempt += 1
+            logger.error(
+                f"Error downloading ocr-error model from {remote}. "
+                f"Attempt {attempt} of {retries}. Error: {e}"
+            )
+            if attempt < retries:
+                import time
+
+                time.sleep(delay)
+            else:
+                raise
+    return local_path
+
+
+class OCRErrorModelLoader(ModelLoader):
+    """Loads the ocr-error DistilBert via stock transformers.
+
+    The checkpoint is a standard ``DistilBertForSequenceClassification`` and loads
+    correctly with ``AutoModelForSequenceClassification`` on transformers 5.x. The
+    previously-vendored encoder copy (surya.ocr_error.model.encoder) silently
+    produces near-constant logits (~0.47 for any input) on transformers 5.x, so it
+    must not be used. Stock transformers also supports flash-attention via
+    ``attn_implementation``, so nothing is lost.
+    """
+
+    def __init__(self, checkpoint: Optional[str] = None):
+        super().__init__(checkpoint)
+
+        if self.checkpoint is None:
+            self.checkpoint = settings.OCR_ERROR_MODEL_CHECKPOINT
+
+    def model(
+        self,
+        device=settings.TORCH_DEVICE_MODEL,
+        dtype=settings.MODEL_DTYPE,
+        attention_implementation: Optional[str] = None,
+    ):
+        if device is None:
+            device = settings.TORCH_DEVICE_MODEL
+        if dtype is None:
+            dtype = settings.MODEL_DTYPE
+
+        local_path = _resolve_checkpoint(self.checkpoint)
+        kwargs = {"dtype": dtype}
+        if attention_implementation is not None:
+            kwargs["attn_implementation"] = attention_implementation
+        model = (
+            AutoModelForSequenceClassification.from_pretrained(local_path, **kwargs)
+            .to(device)
+            .eval()
+        )
+        logger.debug(f"Loaded ocr-error model from {local_path} onto device {device}")
+        return model
+
+    def processor(self, device=settings.TORCH_DEVICE_MODEL, dtype=settings.MODEL_DTYPE):
+        return AutoTokenizer.from_pretrained(_resolve_checkpoint(self.checkpoint))
diff --git a/surya/ocr_error/model/__init__.py b/surya/ocr_error/model/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/surya/ocr_error/model/config.py b/surya/ocr_error/model/config.py
new file mode 100644
index 0000000..721f78d
--- /dev/null
+++ b/surya/ocr_error/model/config.py
@@ -0,0 +1,47 @@
+from transformers.configuration_utils import PretrainedConfig
+
+from surya.common.s3 import S3DownloaderMixin
+
+ID2LABEL = {0: "good", 1: "bad"}
+
+
+class DistilBertConfig(S3DownloaderMixin, PretrainedConfig):
+    model_type = "distilbert"
+    attribute_map = {
+        "hidden_size": "dim",
+        "num_attention_heads": "n_heads",
+        "num_hidden_layers": "n_layers",
+    }
+
+    def __init__(
+        self,
+        vocab_size=30522,
+        max_position_embeddings=512,
+        sinusoidal_pos_embds=False,
+        n_layers=6,
+        n_heads=12,
+        dim=768,
+        hidden_dim=4 * 768,
+        dropout=0.1,
+        attention_dropout=0.1,
+        activation="gelu",
+        initializer_range=0.02,
+        qa_dropout=0.1,
+        seq_classif_dropout=0.2,
+        pad_token_id=0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.sinusoidal_pos_embds = sinusoidal_pos_embds
+        self.n_layers = n_layers
+        self.n_heads = n_heads
+        self.dim = dim
+        self.hidden_dim = hidden_dim
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation = activation
+        self.initializer_range = initializer_range
+        self.qa_dropout = qa_dropout
+        self.seq_classif_dropout = seq_classif_dropout
+        super().__init__(**kwargs, pad_token_id=pad_token_id)
diff --git a/surya/ocr_error/schema.py b/surya/ocr_error/schema.py
new file mode 100644
index 0000000..da1fe92
--- /dev/null
+++ b/surya/ocr_error/schema.py
@@ -0,0 +1,11 @@
+from typing import List
+
+from pydantic import BaseModel
+
+
+class OCRErrorDetectionResult(BaseModel):
+    texts: List[str]
+    labels: List[str]
+    # P(bad) per text (softmax of the "bad" class). Lets callers gate expensive
+    # follow-up work on confidence instead of just the argmax label.
+    scores: List[float] = []
diff --git a/surya/recognition/__init__.py b/surya/recognition/__init__.py
new file mode 100644
index 0000000..8af3947
--- /dev/null
+++ b/surya/recognition/__init__.py
@@ -0,0 +1,388 @@
+"""RecognitionPredictor: per-block OCR via BLOCK_PROMPT.
+
+Given page images and corresponding LayoutResult (or any list of LayoutBox),
+crops each block, runs BLOCK_PROMPT, returns PageOCRResult per page.
+"""
+
+from __future__ import annotations
+
+from typing import List, Optional
+
+from PIL import Image
+
+from surya.common.blank import is_blank_region
+from surya.inference import SuryaInferenceManager, get_default_manager
+from surya.inference.parsers import clean_block_html, parse_full_page_html
+from surya.inference.prompts import (
+    PROMPT_TYPE_BLOCK,
+    PROMPT_TYPE_HIGH_ACCURACY_BBOX,
+    SKIP_OCR_LABELS,
+)
+from surya.inference.schema import BatchInputItem
+from surya.inference.util import image_token_budget
+from surya.layout.label import LAYOUT_PRED_RELABEL, TEXT_LABELS
+from surya.layout.schema import LayoutResult
+from surya.logging import get_logger
+from surya.recognition.schema import (
+    BlockOCRResult,
+    PageOCRResult,
+)
+from surya.settings import settings
+
+logger = get_logger()
+
+
+# Surya's canonical labels we shouldn't OCR (mirrors model-emitted SKIP_OCR_LABELS
+# after canonicalization).
+SKIP_CANON_LABELS = {LAYOUT_PRED_RELABEL.get(lbl, lbl) for lbl in SKIP_OCR_LABELS}
+
+# Full-page OCR regeneration schedule (chandra-style), used ONLY when
+# settings.SURYA_FULLPAGE_REGEN is True. Round 0 is greedy; a page whose output
+# loops / fails to parse is re-requested at escalating temperature (top_p 0.95)
+# before resorting to the (slower) block-mode fallback. Mirrors chandra's
+# retry_temperature = min(0.2*(n+1), 0.8) over MAX_VLLM_RETRIES=6 retries.
+_REGEN_ROUNDS = [(0.0, None)] + [(min(0.2 * (n + 1), 0.8), 0.95) for n in range(6)]
+
+
+def _crop_block(image: Image.Image, polygon, pad: int = 4) -> Image.Image:
+    xs = [p[0] for p in polygon]
+    ys = [p[1] for p in polygon]
+    x0 = max(0, int(min(xs)) - pad)
+    y0 = max(0, int(min(ys)) - pad)
+    x1 = min(image.size[0], int(max(xs)) + pad)
+    y1 = min(image.size[1], int(max(ys)) + pad)
+    if x1 <= x0 or y1 <= y0:
+        return image.crop((0, 0, 1, 1))
+    return image.crop((x0, y0, x1, y1))
+
+
+def _drop_blank_text_blocks(
+    image: Image.Image,
+    blocks: List[BlockOCRResult],
+) -> List[BlockOCRResult]:
+    """Drop text-labeled blocks whose source page region is essentially blank.
+
+    Full-page OCR can emit text divs for regions that are visually empty
+    (margins, gutter space) — the model hallucinates a paragraph where there
+    is none. We crop the region, count near-white pixels, and drop the block
+    when the fraction exceeds ``blank_pixel_fraction``. Only text-like labels
+    (see ``TEXT_LABELS``) are eligible: tables, forms, equations, and visual
+    blocks may legitimately contain large whitespace and are left untouched.
+    """
+    kept: List[BlockOCRResult] = []
+    dropped = 0
+    for blk in blocks:
+        if blk.label not in TEXT_LABELS or blk.skipped or blk.error:
+            kept.append(blk)
+            continue
+        crop = _crop_block(image, blk.polygon)
+        if not is_blank_region(crop):
+            kept.append(blk)
+            continue
+        dropped += 1
+    if dropped:
+        logger.info(f"dropped {dropped} blank text block(s) from full-page OCR")
+    return kept
+
+
+def _detect_repeat_loop(
+    text: str,
+    base_max_repeats: int = 4,
+    window_size: int = 500,
+    scaling_factor: float = 3.0,
+) -> bool:
+    """True iff the tail of ``text`` ends in a repeating sequence.
+
+    Ported from chandra's detect_repeat_token. For each candidate length
+    1..window_size/2, takes that many trailing chars and counts consecutive
+    identical preceding blocks. Shorter loops need many repeats to count;
+    longer ones only need a few. Catches the typical decoder failure mode
+    where a page output gets stuck emitting the same div / phrase until it
+    hits max_tokens.
+    """
+    if not text:
+        return False
+    for seq_len in range(1, window_size // 2 + 1):
+        candidate = text[-seq_len:]
+        max_repeats = int(base_max_repeats * (1 + scaling_factor / seq_len))
+        repeats = 0
+        pos = len(text) - seq_len
+        while pos >= 0 and text[pos : pos + seq_len] == candidate:
+            repeats += 1
+            pos -= seq_len
+        if repeats > max_repeats:
+            return True
+    return False
+
+
+class RecognitionPredictor:
+    """Per-block OCR. Construct with a SuryaInferenceManager (or rely on default)."""
+
+    def __init__(self, manager: Optional[SuryaInferenceManager] = None):
+        self.manager = manager
+        self._disable_tqdm = settings.DISABLE_TQDM
+
+    @property
+    def disable_tqdm(self) -> bool:
+        return self._disable_tqdm
+
+    @disable_tqdm.setter
+    def disable_tqdm(self, value: bool) -> None:
+        self._disable_tqdm = bool(value)
+
+    def to(self, *args, **kwargs):
+        return
+
+    def __call__(
+        self,
+        images: List[Image.Image],
+        layout_results: Optional[List[LayoutResult]] = None,
+        *,
+        full_page: Optional[bool] = None,
+    ) -> List[PageOCRResult]:
+        """Run OCR on each page.
+
+        Mode resolution:
+          - ``full_page=None`` (default): block mode if ``layout_results`` is
+            given, else full-page mode. This is the most-do-what-I-mean form.
+          - ``full_page=True``: full-page OCR (single HIGH_ACCURACY_BBOX_PROMPT
+            request per page). ``layout_results`` is ignored — a warning is
+            logged if it was supplied.
+          - ``full_page=False``: block mode (per-layout-block OCR request).
+            ``layout_results`` is required.
+
+        Full-page is the more accurate path; block mode is for callers that
+        specifically need per-block crops (e.g. for downstream merging with
+        text-line detection).
+        """
+        if not images:
+            return []
+        if full_page is None:
+            full_page = layout_results is None
+        if full_page:
+            if layout_results is not None:
+                logger.info(
+                    "RecognitionPredictor called with full_page=True and "
+                    "layout_results; layout will be used as fallback if the "
+                    "full-page output devolves into a repetition loop."
+                )
+            return self._full_page_ocr(images, fallback_layout=layout_results)
+        if layout_results is None:
+            raise ValueError("layout_results required when full_page=False")
+        if len(images) != len(layout_results):
+            raise ValueError(
+                f"images and layout_results must be same length "
+                f"({len(images)} vs {len(layout_results)})"
+            )
+        manager = self.manager or get_default_manager()
+
+        # Build a flat batch across all pages for max concurrency
+        batch: List[BatchInputItem] = []
+        block_index_map: List[tuple[int, int]] = []  # (page_idx, block_idx)
+        skipped_flags: List[bool] = []
+
+        for page_idx, (img, layout) in enumerate(zip(images, layout_results)):
+            for block_idx, box in enumerate(layout.bboxes):
+                skip = box.label in SKIP_CANON_LABELS
+                skipped_flags.append(skip)
+                if skip:
+                    continue
+                crop = _crop_block(img, box.polygon)
+                max_tokens = image_token_budget(
+                    box.count, ceiling=settings.SURYA_MAX_TOKENS_BLOCK_CEILING
+                )
+                batch.append(
+                    BatchInputItem(
+                        image=crop,
+                        prompt_type=PROMPT_TYPE_BLOCK,
+                        max_tokens=max_tokens,
+                        metadata={"page_idx": page_idx, "block_idx": block_idx},
+                    )
+                )
+                block_index_map.append((page_idx, block_idx))
+
+        outputs = manager.generate(batch) if batch else []
+
+        # Index outputs by (page_idx, block_idx)
+        out_by_key = {}
+        for out in outputs:
+            key = (out.metadata["page_idx"], out.metadata["block_idx"])
+            out_by_key[key] = out
+
+        # Assemble PageOCRResult per page
+        results: List[PageOCRResult] = []
+        for page_idx, (img, layout) in enumerate(zip(images, layout_results)):
+            w, h = img.size
+            blocks: List[BlockOCRResult] = []
+            for block_idx, box in enumerate(layout.bboxes):
+                skip = box.label in SKIP_CANON_LABELS
+                if skip:
+                    blocks.append(
+                        BlockOCRResult(
+                            polygon=box.polygon,
+                            label=box.label,
+                            raw_label=box.raw_label,
+                            reading_order=box.position,
+                            html="",
+                            skipped=True,
+                            confidence=1.0,
+                        )
+                    )
+                    continue
+                out = out_by_key.get((page_idx, block_idx))
+                if out is None or out.error:
+                    blocks.append(
+                        BlockOCRResult(
+                            polygon=box.polygon,
+                            label=box.label,
+                            raw_label=box.raw_label,
+                            reading_order=box.position,
+                            html="",
+                            skipped=False,
+                            error=True,
+                            confidence=0.0,
+                        )
+                    )
+                    continue
+                html = clean_block_html(out.raw)
+                conf = out.mean_token_prob if out.mean_token_prob is not None else 1.0
+                blocks.append(
+                    BlockOCRResult(
+                        polygon=box.polygon,
+                        label=box.label,
+                        raw_label=box.raw_label,
+                        reading_order=box.position,
+                        html=html,
+                        skipped=False,
+                        error=False,
+                        confidence=conf,
+                        raw_logprobs=out.logprobs,
+                    )
+                )
+            results.append(
+                PageOCRResult(blocks=blocks, image_bbox=[0, 0, float(w), float(h)])
+            )
+        return results
+
+    def _full_page_ocr(
+        self,
+        images: List[Image.Image],
+        fallback_layout: Optional[List[LayoutResult]] = None,
+    ) -> List[PageOCRResult]:
+        """One HIGH_ACCURACY_BBOX_PROMPT request per page; parses divs into blocks.
+
+        On per-page failure (parse error, empty output, or a detected
+        repetition loop in the decoder output), falls back to layout +
+        block-mode OCR for that page only. ``fallback_layout``, if given,
+        provides per-page LayoutResults to use on fallback; otherwise the
+        LayoutPredictor is invoked lazily for just the affected pages.
+        """
+        manager = self.manager or get_default_manager()
+        results: List[Optional[PageOCRResult]] = [None] * len(images)
+
+        def _build_page(out, img):
+            """A good full-page output -> PageOCRResult; otherwise None (regen/fallback).
+            A genuinely blank page is a valid empty result (not a failure)."""
+            w, h = img.size
+            page_bbox = [0, 0, float(w), float(h)]
+            if out is None or out.error:
+                return None
+            if not out.raw:
+                return (
+                    PageOCRResult(blocks=[], image_bbox=page_bbox)
+                    if is_blank_region(img)
+                    else None
+                )
+            if _detect_repeat_loop(out.raw):
+                return None
+            try:
+                parsed = parse_full_page_html(out.raw)
+            except Exception:
+                return None
+            confidence = out.mean_token_prob if out.mean_token_prob is not None else 1.0
+            blocks: List[BlockOCRResult] = []
+            for idx, item in enumerate(parsed):
+                x0 = item.bbox[0] / settings.BBOX_SCALE * w
+                y0 = item.bbox[1] / settings.BBOX_SCALE * h
+                x1 = item.bbox[2] / settings.BBOX_SCALE * w
+                y1 = item.bbox[3] / settings.BBOX_SCALE * h
+                polygon = [[x0, y0], [x1, y0], [x1, y1], [x0, y1]]
+                canon = LAYOUT_PRED_RELABEL.get(item.label, item.label)
+                skipped = canon in SKIP_CANON_LABELS
+                blocks.append(
+                    BlockOCRResult(
+                        polygon=polygon,
+                        label=canon,
+                        raw_label=item.label,
+                        reading_order=idx,
+                        html="" if skipped else item.html,
+                        skipped=skipped,
+                        error=False,
+                        confidence=confidence,
+                    )
+                )
+            return PageOCRResult(
+                blocks=_drop_blank_text_blocks(img, blocks), image_bbox=page_bbox
+            )
+
+        # Progressive-temperature regeneration (chandra-style), gated by
+        # settings.SURYA_FULLPAGE_REGEN (default off -> single greedy pass, then
+        # block-mode fallback, i.e. surya's prior behavior).
+        rounds = _REGEN_ROUNDS if settings.SURYA_FULLPAGE_REGEN else [(0.0, None)]
+        pending = list(range(len(images)))
+        for round_i, (temp, top_p) in enumerate(rounds):
+            if not pending:
+                break
+            batch = [
+                BatchInputItem(
+                    image=images[i],
+                    prompt_type=PROMPT_TYPE_HIGH_ACCURACY_BBOX,
+                    max_tokens=settings.SURYA_MAX_TOKENS_FULL_PAGE,
+                    temperature=(temp if round_i > 0 else None),
+                    top_p=top_p,
+                    metadata={"page_idx": i},
+                )
+                for i in pending
+            ]
+            out_by_page = {o.metadata["page_idx"]: o for o in manager.generate(batch)}
+            still: List[int] = []
+            for i in pending:
+                r = _build_page(out_by_page.get(i), images[i])
+                if r is not None:
+                    results[i] = r
+                else:
+                    still.append(i)
+            if still and round_i < len(rounds) - 1:
+                logger.info(
+                    f"regenerating {len(still)} full-page output(s) at higher temperature"
+                )
+            pending = still
+        needs_fallback: List[int] = pending
+
+        # Block-mode fallback for any pages whose full-page output failed or looped.
+        if needs_fallback:
+            fb_images = [images[i] for i in needs_fallback]
+            if fallback_layout is not None:
+                fb_layouts = [fallback_layout[i] for i in needs_fallback]
+            else:
+                # Lazy import to avoid the surya.layout ↔ surya.recognition cycle.
+                from surya.layout import LayoutPredictor
+
+                logger.info(
+                    f"running layout for {len(fb_images)} page(s) requiring "
+                    f"block-mode fallback"
+                )
+                fb_layouts = LayoutPredictor(self.manager)(fb_images)
+            fb_results = self.__call__(fb_images, fb_layouts, full_page=False)
+            for fb_idx, page_idx in enumerate(needs_fallback):
+                results[page_idx] = fb_results[fb_idx]
+
+        # Backfill any still-None pages with empty results (defensive — shouldn't happen).
+        out_results: List[PageOCRResult] = []
+        for page_idx, img in enumerate(images):
+            r = results[page_idx]
+            if r is None:
+                w, h = img.size
+                r = PageOCRResult(blocks=[], image_bbox=[0, 0, float(w), float(h)])
+            out_results.append(r)
+        return out_results
diff --git a/surya/recognition/schema.py b/surya/recognition/schema.py
new file mode 100644
index 0000000..e914b5b
--- /dev/null
+++ b/surya/recognition/schema.py
@@ -0,0 +1,19 @@
+from typing import List
+
+from pydantic import BaseModel
+
+from surya.common.polygon import PolygonBox
+
+
+class BlockOCRResult(PolygonBox):
+    label: str  # canonicalized layout label (Picture, Text, ...)
+    raw_label: str = ""  # original model label
+    reading_order: int  # 0-indexed position in layout output
+    html: str = ""  # block HTML (BLOCK_PROMPT output, "" if skipped)
+    skipped: bool = False  # True if label was in SKIP_OCR_LABELS
+    error: bool = False
+
+
+class PageOCRResult(BaseModel):
+    blocks: List[BlockOCRResult]
+    image_bbox: List[float]
diff --git a/surya/scripts/__init__.py b/surya/scripts/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/surya/scripts/build_gguf.py b/surya/scripts/build_gguf.py
new file mode 100644
index 0000000..5f5fe4e
--- /dev/null
+++ b/surya/scripts/build_gguf.py
@@ -0,0 +1,311 @@
+"""Build a GGUF artifact from a surya VLM checkpoint, suitable for stock llama.cpp.
+
+Three patches are applied to llama.cpp's convert_hf_to_gguf.py before
+running the HF→GGUF conversion. All fixes are baked into the output
+artifact, so the resulting GGUF runs on unpatched llama.cpp.
+
+Two checkpoint-side patches are also applied to a working copy:
+  - config.json:           architectures → ["Qwen3_5ForConditionalGeneration"]
+  - tokenizer_config.json: tokenizer_class → "PreTrainedTokenizerFast",
+                           strip backend / extra_special_tokens / is_local
+
+The original checkpoint is never modified — patches land in a sibling
+working dir under --out-dir.
+
+Usage:
+    python -m surya.scripts.build_gguf \\
+        --checkpoint datalab-to/surya-2.1.6 \\
+        --out-dir ./gguf-build
+"""
+
+import argparse
+import json
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+LLAMA_CPP_REPO = "https://github.com/ggerganov/llama.cpp.git"
+# Pinned commit the converter patches were authored against. Bump when
+# the upstream converter drifts; re-validate the anchor strings below.
+LLAMA_CPP_REV = "bbeb89d76c41bc250f16e4a6fefcc9b530d6e3f3"
+
+
+# ---- llama.cpp converter patches -----------------------------------------
+# Each patch is (sentinel, anchor, replacement). Idempotent: if `sentinel`
+# is already in the file, the patch is skipped.
+
+_PATCH_REGISTER_HASH_ANCHOR = """        if chkhsh == "862f827721df956049dff5ca81a57f29e575280bc622e290d3bf4e35eca29015":
+            # ref: https://huggingface.co/codefuse-ai/F2LLM-v2-4B
+            res = "f2llmv2"
+"""
+
+_PATCH_REGISTER_HASH_REPLACEMENT = (
+    _PATCH_REGISTER_HASH_ANCHOR
+    + """        if chkhsh == "11865354be60ff9206694aed04242190f1807029bbd750bfbced09b4d26f1ad2":
+            # surya-2.1.0 char-level WordLevel tokenizer (Split regex=".")
+            res = "default"
+"""
+)
+
+_PATCH_VOCAB_ANCHOR = """    def _set_vocab_gpt2(self) -> None:
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+"""
+
+_PATCH_VOCAB_REPLACEMENT = '''    def _set_vocab_gpt2(self) -> None:
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        # surya: char-level / WordLevel vocabs contain raw bytes (e.g. " ",
+        # "\\n"). llama.cpp's gpt2 vocab decoder applies bytes_to_unicode
+        # when emitting NORMAL tokens, so encode bytes here for round-trip.
+        # Idempotent on already-encoded vocabs.
+        tokens = self._maybe_encode_gpt2_bytes(tokens, toktypes)
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+        # surya: char-level / WordLevel vocabs have no merges, but the gpt2
+        # vocab loader requires the field. Write a single dummy entry.
+        if not special_vocab.merges:
+            self.gguf_writer.add_token_merges(["a a"])
+
+    @staticmethod
+    def _maybe_encode_gpt2_bytes(tokens: list[str], toktypes: list[int]) -> list[str]:
+        """Apply GPT-2 bytes_to_unicode to NORMAL tokens iff any contain raw
+        whitespace/control bytes. Idempotent on already-encoded vocabs."""
+        bs = (list(range(ord("!"), ord("~") + 1))
+              + list(range(ord("¡"), ord("¬") + 1))
+              + list(range(ord("®"), ord("ÿ") + 1)))
+        cs = bs[:]
+        n = 0
+        for b in range(256):
+            if b not in bs:
+                bs.append(b)
+                cs.append(2 ** 8 + n)
+                n += 1
+        byte_to_unicode = {b: chr(c) for b, c in zip(bs, cs)}
+        printable = set(range(ord("!"), ord("~") + 1))
+        needs = False
+        for tok, ttype in zip(tokens, toktypes):
+            if ttype != gguf.TokenType.NORMAL:
+                continue
+            for ch in tok:
+                cb = ord(ch)
+                if cb < 0x80 and cb not in printable:
+                    needs = True
+                    break
+            if needs:
+                break
+        if not needs:
+            return tokens
+        out: list[str] = []
+        for tok, ttype in zip(tokens, toktypes):
+            if ttype == gguf.TokenType.NORMAL:
+                out.append("".join(byte_to_unicode[b] for b in tok.encode("utf-8")))
+            else:
+                out.append(tok)
+        return out
+'''
+
+CONVERTER_PATCHES = [
+    {
+        "name": "register surya-2.1.0 pre-tokenizer hash",
+        "sentinel": '"11865354be60ff9206694aed04242190f1807029bbd750bfbced09b4d26f1ad2"',
+        "anchor": _PATCH_REGISTER_HASH_ANCHOR,
+        "replacement": _PATCH_REGISTER_HASH_REPLACEMENT,
+    },
+    {
+        "name": "byte-encode NORMAL tokens + dummy merges in _set_vocab_gpt2",
+        "sentinel": "_maybe_encode_gpt2_bytes",
+        "anchor": _PATCH_VOCAB_ANCHOR,
+        "replacement": _PATCH_VOCAB_REPLACEMENT,
+    },
+]
+
+
+def patch_converter(convert_py: Path) -> None:
+    text = convert_py.read_text()
+    changed = False
+    for p in CONVERTER_PATCHES:
+        if p["sentinel"] in text:
+            print(f"  [skip]  {p['name']} (already applied)")
+            continue
+        if p["anchor"] not in text:
+            raise RuntimeError(
+                f"converter patch {p['name']!r} could not find its anchor. "
+                f"llama.cpp upstream may have drifted; re-pin LLAMA_CPP_REV "
+                f"and re-validate anchors."
+            )
+        text = text.replace(p["anchor"], p["replacement"], 1)
+        print(f"  [apply] {p['name']}")
+        changed = True
+    if changed:
+        convert_py.write_text(text)
+
+
+# ---- checkpoint-side patches ----------------------------------------------
+
+
+def patch_checkpoint(src: Path, dst: Path) -> None:
+    """Symlink-clone src into dst, with config.json + tokenizer_config.json
+    rewritten for stock transformers/llama.cpp compatibility."""
+    if dst.exists():
+        shutil.rmtree(dst)
+    dst.mkdir(parents=True)
+    overrides = {"config.json", "tokenizer_config.json"}
+    for entry in src.iterdir():
+        if entry.name in overrides:
+            continue
+        os.symlink(entry.resolve(), dst / entry.name)
+
+    cfg = json.loads((src / "config.json").read_text())
+    cfg["architectures"] = ["Qwen3_5ForConditionalGeneration"]
+    (dst / "config.json").write_text(json.dumps(cfg, indent=2))
+
+    tk = json.loads((src / "tokenizer_config.json").read_text())
+    tk["tokenizer_class"] = "PreTrainedTokenizerFast"
+    for k in ("backend", "extra_special_tokens", "is_local"):
+        tk.pop(k, None)
+    (dst / "tokenizer_config.json").write_text(json.dumps(tk, indent=2))
+
+
+# ---- llama.cpp resolution -------------------------------------------------
+
+
+def ensure_llama_cpp(repo_dir: Path, rev: str) -> Path:
+    if not repo_dir.exists():
+        repo_dir.parent.mkdir(parents=True, exist_ok=True)
+        print(f"[clone] {LLAMA_CPP_REPO} → {repo_dir}")
+        subprocess.check_call(["git", "clone", LLAMA_CPP_REPO, str(repo_dir)])
+    head = subprocess.check_output(
+        ["git", "-C", str(repo_dir), "rev-parse", "HEAD"], text=True
+    ).strip()
+    if head != rev:
+        # Discard any prior patches so the checkout is clean.
+        subprocess.check_call(
+            ["git", "-C", str(repo_dir), "reset", "--hard", "--quiet", "HEAD"]
+        )
+        subprocess.check_call(
+            ["git", "-C", str(repo_dir), "fetch", "--quiet", "origin"]
+        )
+        print(f"[checkout] llama.cpp @ {rev}")
+        subprocess.check_call(["git", "-C", str(repo_dir), "checkout", "--quiet", rev])
+    return repo_dir
+
+
+# ---- checkpoint resolution ------------------------------------------------
+
+
+def resolve_checkpoint(checkpoint: str) -> Path:
+    p = Path(checkpoint)
+    if p.exists():
+        return p.resolve()
+    from huggingface_hub import snapshot_download
+
+    print(f"[download] {checkpoint}")
+    return Path(snapshot_download(checkpoint))
+
+
+# ---- main -----------------------------------------------------------------
+
+
+def main() -> int:
+    from surya.settings import settings
+
+    ap = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    ap.add_argument(
+        "--checkpoint",
+        default=settings.SURYA_MODEL_CHECKPOINT,
+        help="HF repo id or local checkpoint dir",
+    )
+    ap.add_argument("--out-dir", type=Path, default=Path("./gguf-build"))
+    ap.add_argument(
+        "--name",
+        default="surya-2",
+        help="Output basename. Produces <name>.gguf and <name>-mmproj.gguf",
+    )
+    ap.add_argument(
+        "--llama-cpp-dir",
+        type=Path,
+        default=Path.home() / ".cache" / "datalab" / "llama.cpp",
+    )
+    ap.add_argument("--llama-cpp-rev", default=LLAMA_CPP_REV)
+    ap.add_argument(
+        "--outtype",
+        default="f16",
+        help="convert_hf_to_gguf --outtype (f16, bf16, q8_0, ...)",
+    )
+    ap.add_argument(
+        "--keep-work",
+        action="store_true",
+        help="Keep the patched-checkpoint working dir on success",
+    )
+    args = ap.parse_args()
+
+    args.out_dir.mkdir(parents=True, exist_ok=True)
+    work = args.out_dir / "_patched_ckpt"
+
+    src = resolve_checkpoint(args.checkpoint)
+    print(f"[checkpoint] {src}")
+
+    print("[patch] checkpoint config + tokenizer_config")
+    patch_checkpoint(src, work)
+
+    repo = ensure_llama_cpp(args.llama_cpp_dir, args.llama_cpp_rev)
+    convert_py = repo / "convert_hf_to_gguf.py"
+    print("[patch] llama.cpp convert_hf_to_gguf.py")
+    patch_converter(convert_py)
+
+    out_llm = (args.out_dir / f"{args.name}.gguf").resolve()
+    out_mmproj = (args.out_dir / f"{args.name}-mmproj.gguf").resolve()
+
+    print(f"[convert] LLM → {out_llm}")
+    subprocess.check_call(
+        [
+            sys.executable,
+            str(convert_py),
+            str(work),
+            "--outfile",
+            str(out_llm),
+            "--outtype",
+            args.outtype,
+        ]
+    )
+    print(f"[convert] mmproj → {out_mmproj}")
+    subprocess.check_call(
+        [
+            sys.executable,
+            str(convert_py),
+            str(work),
+            "--mmproj",
+            "--outfile",
+            str(out_mmproj),
+            "--outtype",
+            args.outtype,
+        ]
+    )
+
+    if not args.keep_work:
+        shutil.rmtree(work)
+
+    print()
+    print(f"  LLM:    {out_llm}")
+    print(f"  mmproj: {out_mmproj}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/surya/scripts/config.py b/surya/scripts/config.py
new file mode 100644
index 0000000..921627a
--- /dev/null
+++ b/surya/scripts/config.py
@@ -0,0 +1,98 @@
+from typing import List
+
+import click
+import os
+from surya.input.load import load_from_folder, load_from_file
+from surya.settings import settings
+
+
+class CLILoader:
+    def __init__(self, filepath: str, cli_options: dict, highres: bool = False):
+        self.page_range = cli_options.get("page_range")
+        if self.page_range:
+            self.page_range = self.parse_range_str(self.page_range)
+        self.filepath = filepath
+        self.config = cli_options
+        self.save_images = cli_options.get("images", False)
+        self.debug = cli_options.get("debug", False)
+        self.output_dir = cli_options.get("output_dir")
+
+        # Opt in to leaving the inference server up so later commands reuse it.
+        if cli_options.get("keep_server"):
+            settings.SURYA_INFERENCE_KEEP_ALIVE = True
+
+        self.load(highres)
+
+    @staticmethod
+    def common_options(fn):
+        fn = click.argument("input_path", type=click.Path(exists=True), required=True)(
+            fn
+        )
+        fn = click.option(
+            "--output_dir",
+            type=click.Path(exists=False),
+            required=False,
+            default=os.path.join(settings.RESULT_DIR, "surya"),
+            help="Directory to save output.",
+        )(fn)
+        fn = click.option(
+            "--page_range",
+            type=str,
+            default=None,
+            help="Page range to convert, specify comma separated page numbers or ranges.  Example: 0,5-10,20",
+        )(fn)
+        fn = click.option(
+            "--images",
+            is_flag=True,
+            help="Save images of detected bboxes.",
+            default=False,
+        )(fn)
+        fn = click.option(
+            "--debug", "-d", is_flag=True, help="Enable debug mode.", default=False
+        )(fn)
+        fn = click.option(
+            "--keep_server",
+            is_flag=True,
+            default=False,
+            help="Keep the inference server (vllm/llama.cpp) running after this command exits so later commands reuse it instead of re-spawning.",
+        )(fn)
+        return fn
+
+    def load(self, highres: bool = False):
+        highres_images = None
+        if os.path.isdir(self.filepath):
+            images, names = load_from_folder(self.filepath, self.page_range)
+            folder_name = os.path.basename(self.filepath)
+            if highres:
+                highres_images, _ = load_from_folder(
+                    self.filepath, self.page_range, settings.IMAGE_DPI_HIGHRES
+                )
+        else:
+            images, names = load_from_file(self.filepath, self.page_range)
+            folder_name = os.path.basename(self.filepath).split(".")[0]
+            if highres:
+                highres_images, _ = load_from_file(
+                    self.filepath, self.page_range, settings.IMAGE_DPI_HIGHRES
+                )
+
+        self.images = images
+        self.highres_images = highres_images
+        self.names = names
+
+        self.result_path = os.path.abspath(os.path.join(self.output_dir, folder_name))
+        os.makedirs(self.result_path, exist_ok=True)
+
+    @staticmethod
+    def parse_range_str(range_str: str) -> List[int]:
+        range_lst = range_str.split(",")
+        page_lst = []
+        for i in range_lst:
+            if "-" in i:
+                start, end = i.split("-")
+                page_lst += list(range(int(start), int(end) + 1))
+            else:
+                page_lst.append(int(i))
+        page_lst = sorted(
+            list(set(page_lst))
+        )  # Deduplicate page numbers and sort in order
+        return page_lst
diff --git a/surya/scripts/detect_layout.py b/surya/scripts/detect_layout.py
new file mode 100644
index 0000000..8eef0ee
--- /dev/null
+++ b/surya/scripts/detect_layout.py
@@ -0,0 +1,58 @@
+import time
+import click
+import copy
+import json
+from collections import defaultdict
+
+from surya.inference import SuryaInferenceManager
+from surya.layout import LayoutPredictor
+from surya.debug.draw import draw_polys_on_image
+from surya.logging import configure_logging, get_logger
+from surya.scripts.config import CLILoader
+import os
+
+configure_logging()
+logger = get_logger()
+
+
+@click.command(help="Detect layout of an input file or folder (PDFs or image).")
+@CLILoader.common_options
+def detect_layout_cli(input_path: str, **kwargs):
+    loader = CLILoader(input_path, kwargs)
+
+    manager = SuryaInferenceManager()
+    layout_predictor = LayoutPredictor(manager)
+
+    start = time.time()
+    layout_predictions = layout_predictor(loader.images)
+
+    if loader.debug:
+        logger.debug(f"Layout took {time.time() - start} seconds")
+
+    if loader.save_images:
+        for idx, (image, layout_pred, name) in enumerate(
+            zip(loader.images, layout_predictions, loader.names)
+        ):
+            polygons = [p.polygon for p in layout_pred.bboxes]
+            labels = [f"{p.label}-{p.position}" for p in layout_pred.bboxes]
+            bbox_image = draw_polys_on_image(
+                polygons, copy.deepcopy(image), labels=labels
+            )
+            bbox_image.save(
+                os.path.join(loader.result_path, f"{name}_{idx}_layout.png")
+            )
+
+    predictions_by_page = defaultdict(list)
+    for idx, (pred, name, image) in enumerate(
+        zip(layout_predictions, loader.names, loader.images)
+    ):
+        out_pred = pred.model_dump()
+        out_pred["page"] = len(predictions_by_page[name]) + 1
+        predictions_by_page[name].append(out_pred)
+
+    with open(
+        os.path.join(loader.result_path, "results.json"), "w+", encoding="utf-8"
+    ) as f:
+        json.dump(predictions_by_page, f, ensure_ascii=False)
+
+    logger.info(f"Wrote results to {loader.result_path}")
diff --git a/surya/scripts/detect_text.py b/surya/scripts/detect_text.py
new file mode 100644
index 0000000..767df08
--- /dev/null
+++ b/surya/scripts/detect_text.py
@@ -0,0 +1,55 @@
+import click
+import copy
+import json
+import time
+from collections import defaultdict
+
+from surya.detection import DetectionPredictor
+from surya.debug.draw import draw_polys_on_image
+from surya.logging import configure_logging, get_logger
+from surya.scripts.config import CLILoader
+import os
+
+configure_logging()
+logger = get_logger()
+
+
+@click.command(help="Detect bboxes in an input file or folder (PDFs or image).")
+@CLILoader.common_options
+def detect_text_cli(input_path: str, **kwargs):
+    loader = CLILoader(input_path, kwargs)
+
+    det_predictor = DetectionPredictor()
+
+    start = time.time()
+    predictions = det_predictor(loader.images, include_maps=loader.debug)
+    end = time.time()
+    if loader.debug:
+        logger.debug(f"Detection took {end - start} seconds")
+
+    if loader.save_images:
+        for idx, (image, pred, name) in enumerate(
+            zip(loader.images, predictions, loader.names)
+        ):
+            polygons = [p.polygon for p in pred.bboxes]
+            bbox_image = draw_polys_on_image(polygons, copy.deepcopy(image))
+            bbox_image.save(os.path.join(loader.result_path, f"{name}_{idx}_bbox.png"))
+
+            if loader.debug:
+                heatmap = pred.heatmap
+                heatmap.save(os.path.join(loader.result_path, f"{name}_{idx}_heat.png"))
+
+    predictions_by_page = defaultdict(list)
+    for idx, (pred, name, image) in enumerate(
+        zip(predictions, loader.names, loader.images)
+    ):
+        out_pred = pred.model_dump(exclude=["heatmap", "affinity_map"])
+        out_pred["page"] = len(predictions_by_page[name]) + 1
+        predictions_by_page[name].append(out_pred)
+
+    with open(
+        os.path.join(loader.result_path, "results.json"), "w+", encoding="utf-8"
+    ) as f:
+        json.dump(predictions_by_page, f, ensure_ascii=False)
+
+    logger.info(f"Wrote results to {loader.result_path}")
diff --git a/surya/scripts/ocr_text.py b/surya/scripts/ocr_text.py
new file mode 100644
index 0000000..2957aec
--- /dev/null
+++ b/surya/scripts/ocr_text.py
@@ -0,0 +1,44 @@
+import os
+import click
+import json
+import time
+from collections import defaultdict
+
+from surya.inference import SuryaInferenceManager
+from surya.logging import configure_logging, get_logger
+from surya.recognition import RecognitionPredictor
+from surya.scripts.config import CLILoader
+
+configure_logging()
+logger = get_logger()
+
+
+@click.command(help="OCR text — full-page OCR (one VLM call per page).")
+@CLILoader.common_options
+def ocr_text_cli(input_path: str, **kwargs):
+    # Full-page OCR is the default path: one VLM call per page returns layout
+    # + content together. Pages whose full-page output fails to parse fall
+    # back to layout + per-block OCR automatically (see RecognitionPredictor).
+    loader = CLILoader(input_path, kwargs, highres=True)
+
+    manager = SuryaInferenceManager()
+    rec_predictor = RecognitionPredictor(manager)
+
+    start = time.time()
+    page_results = rec_predictor(loader.highres_images, full_page=True)
+
+    if loader.debug:
+        logger.debug(f"OCR took {time.time() - start:.2f} seconds")
+
+    out_preds = defaultdict(list)
+    for name, page in zip(loader.names, page_results):
+        out_pred = page.model_dump()
+        out_pred["page"] = len(out_preds[name]) + 1
+        out_preds[name].append(out_pred)
+
+    with open(
+        os.path.join(loader.result_path, "results.json"), "w+", encoding="utf-8"
+    ) as f:
+        json.dump(out_preds, f, ensure_ascii=False)
+
+    logger.info(f"Wrote results to {loader.result_path}")
diff --git a/surya/scripts/run_streamlit_app.py b/surya/scripts/run_streamlit_app.py
new file mode 100644
index 0000000..658b2ce
--- /dev/null
+++ b/surya/scripts/run_streamlit_app.py
@@ -0,0 +1,9 @@
+import subprocess
+import os
+
+
+def streamlit_app_cli():
+    cur_dir = os.path.dirname(os.path.abspath(__file__))
+    ocr_app_path = os.path.join(cur_dir, "streamlit_app.py")
+    cmd = ["streamlit", "run", ocr_app_path, "--server.fileWatcherType", "none", "--server.headless", "true"]
+    subprocess.run(cmd, env={**os.environ, "IN_STREAMLIT": "true"})
\ No newline at end of file
diff --git a/surya/scripts/screenshot_app.py b/surya/scripts/screenshot_app.py
new file mode 100644
index 0000000..34b88ab
--- /dev/null
+++ b/surya/scripts/screenshot_app.py
@@ -0,0 +1,226 @@
+"""Screenshot-friendly Surya viewer.
+
+Shows a PDF/image page on the left and full-page OCR output on the right, side
+by side, for clean screenshots. You can scroll through pages and preview them
+before running OCR, then export the side-by-side view as a PNG.
+
+Run with `surya_screenshot`, then open http://localhost:8504.
+"""
+
+from __future__ import annotations
+
+import base64
+import io
+import os
+import tempfile
+import uuid
+from typing import List, Optional
+
+import pypdfium2
+from flask import Flask, jsonify, render_template, request
+from PIL import Image
+from werkzeug.utils import secure_filename
+
+from surya.inference import SuryaInferenceManager
+from surya.logging import configure_logging, get_logger
+from surya.recognition import RecognitionPredictor
+from surya.recognition.schema import PageOCRResult
+from surya.settings import settings
+
+configure_logging()
+logger = get_logger()
+
+app = Flask(__name__)
+
+ALLOWED_EXT = {".pdf", ".png", ".jpg", ".jpeg", ".gif", ".webp"}
+UPLOAD_DIR = os.path.join(tempfile.gettempdir(), "surya_screenshot")
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+_rec: Optional[RecognitionPredictor] = None
+
+
+def get_rec() -> RecognitionPredictor:
+    """Lazily build the recognition predictor (shared inference manager)."""
+    global _rec
+    if _rec is None:
+        _rec = RecognitionPredictor(SuryaInferenceManager())
+    return _rec
+
+
+# Datalab-flavored palette for layout block overlays, keyed by canonical label.
+LABEL_COLORS = {
+    "Text": "#2563eb",
+    "SectionHeader": "#0ea5e9",
+    "PageHeader": "#7c3aed",
+    "PageFooter": "#7c3aed",
+    "Caption": "#c026d3",
+    "Footnote": "#64748b",
+    "Equation": "#9333ea",
+    "Table": "#f59e0b",
+    "TableOfContents": "#f59e0b",
+    "Form": "#ea580c",
+    "ListGroup": "#10b981",
+    "Picture": "#db2777",
+    "Figure": "#db2777",
+    "Diagram": "#db2777",
+    "Code": "#0d9488",
+    "default": "#ef4444",
+}
+
+
+def _logo_data_url() -> str:
+    path = os.path.join(settings.BASE_DIR, "static", "datalab-logo.png")
+    try:
+        with open(path, "rb") as f:
+            return "data:image/png;base64," + base64.b64encode(f.read()).decode()
+    except Exception:
+        return ""
+
+
+def _pil_to_data_url(img: Image.Image, fmt: str = "PNG") -> str:
+    buf = io.BytesIO()
+    img.save(buf, format=fmt)
+    return (
+        f"data:image/{fmt.lower()};base64," + base64.b64encode(buf.getvalue()).decode()
+    )
+
+
+def _is_pdf(path: str) -> bool:
+    return path.lower().endswith(".pdf")
+
+
+def _page_count(path: str) -> int:
+    if _is_pdf(path):
+        doc = pypdfium2.PdfDocument(path)
+        n = len(doc)
+        doc.close()
+        return n
+    return 1
+
+
+def _render_page(path: str, page: int, dpi: int) -> Image.Image:
+    """Render a 0-indexed page of a PDF (or load an image file) as RGB."""
+    if _is_pdf(path):
+        doc = pypdfium2.PdfDocument(path)
+        try:
+            pil = doc[page].render(scale=dpi / 72).to_pil().convert("RGB")
+        finally:
+            doc.close()
+        return pil
+    return Image.open(path).convert("RGB")
+
+
+def _assemble_page_html(page: PageOCRResult) -> str:
+    """Whole-page HTML from a PageOCRResult (math stays in <math> tags)."""
+    parts: List[str] = []
+    for blk in page.blocks:
+        if blk.skipped:
+            continue
+        x0, y0, x1, y1 = (int(c) for c in blk.bbox)
+        parts.append(
+            f'<div data-bbox="{x0} {y0} {x1} {y1}" '
+            f'data-label="{blk.label}">{blk.html or ""}</div>'
+        )
+    return "\n".join(parts)
+
+
+@app.route("/")
+def index():
+    return render_template("surya_screenshot.html", logo=_logo_data_url())
+
+
+@app.route("/info", methods=["POST"])
+def info():
+    path = (request.json or {}).get("file_path", "").strip()
+    if not path:
+        return jsonify({"error": "file_path is required"}), 400
+    if not os.path.exists(path):
+        return jsonify({"error": f"File not found: {path}"}), 400
+    try:
+        return jsonify({"page_count": _page_count(path)})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+@app.route("/upload", methods=["POST"])
+def upload():
+    """Accept a drag/drop (or browsed) file, save to a temp path, return it."""
+    f = request.files.get("file")
+    if f is None or not f.filename:
+        return jsonify({"error": "no file uploaded"}), 400
+    ext = os.path.splitext(f.filename)[1].lower()
+    if ext not in ALLOWED_EXT:
+        return jsonify({"error": f"unsupported file type: {ext or '(none)'}"}), 400
+    safe = secure_filename(f.filename) or f"upload{ext}"
+    dest = os.path.join(UPLOAD_DIR, f"{uuid.uuid4().hex}_{safe}")
+    f.save(dest)
+    try:
+        return jsonify(
+            {"file_path": dest, "page_count": _page_count(dest), "name": f.filename}
+        )
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+@app.route("/page", methods=["POST"])
+def page():
+    """Render a single page for preview (no OCR)."""
+    data = request.json or {}
+    path = data.get("file_path", "").strip()
+    page_num = int(data.get("page", 0))
+    if not path or not os.path.exists(path):
+        return jsonify({"error": "valid file_path is required"}), 400
+    try:
+        img = _render_page(path, page_num, settings.IMAGE_DPI_HIGHRES)
+        return jsonify(
+            {
+                "image_base64": _pil_to_data_url(img),
+                "width": img.size[0],
+                "height": img.size[1],
+            }
+        )
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+@app.route("/process", methods=["POST"])
+def process():
+    """Run full-page OCR on one page; return the page image + OCR HTML + blocks."""
+    data = request.json or {}
+    path = data.get("file_path", "").strip()
+    page_num = int(data.get("page", 0))
+    if not path or not os.path.exists(path):
+        return jsonify({"error": "valid file_path is required"}), 400
+    try:
+        img = _render_page(path, page_num, settings.IMAGE_DPI_HIGHRES)
+        page_result = get_rec()([img], full_page=True)[0]
+        blocks = [
+            {
+                "bbox": [int(c) for c in blk.bbox],
+                "label": blk.label,
+                "color": LABEL_COLORS.get(blk.label, LABEL_COLORS["default"]),
+            }
+            for blk in page_result.blocks
+            if not blk.skipped
+        ]
+        return jsonify(
+            {
+                "image_base64": _pil_to_data_url(img),
+                "width": img.size[0],
+                "height": img.size[1],
+                "html": _assemble_page_html(page_result),
+                "blocks": blocks,
+                "n_blocks": len(page_result.blocks),
+            }
+        )
+    except Exception as e:
+        logger.exception("Full-page OCR failed")
+        return jsonify({"error": str(e)}), 500
+
+
+def main():
+    app.run(host="0.0.0.0", port=8504)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surya/scripts/streamlit_app.py b/surya/scripts/streamlit_app.py
new file mode 100644
index 0000000..44f7b9c
--- /dev/null
+++ b/surya/scripts/streamlit_app.py
@@ -0,0 +1,506 @@
+"""Surya2 streamlit app — exercise layout, recognition, table_rec via the
+inference manager. Detection + OCR-error stay in their own torch paths."""
+
+from __future__ import annotations
+
+import io
+import re
+import tempfile
+import time
+from typing import List
+
+import pypdfium2
+import streamlit as st
+import streamlit.components.v1 as components
+from PIL import Image, ImageDraw
+
+from surya.debug.draw import draw_polys_on_image, draw_bboxes_on_image
+from surya.detection import TextDetectionResult
+from surya.inference import SuryaInferenceManager
+from surya.layout import LayoutPredictor
+from surya.layout.schema import LayoutResult
+from surya.recognition import RecognitionPredictor
+from surya.recognition.schema import PageOCRResult
+from surya.settings import settings
+from surya.table_rec import TableRecPredictor
+from surya.table_rec.schema import TableResult
+
+
+# KaTeX-enabled HTML wrapper. The OCR HTML wraps math in <math>...</math>
+# (KaTeX-compatible LaTeX inside), which a browser would otherwise show as
+# raw text. We convert those tags to \( \) / \[ \] delimiters and let KaTeX
+# auto-render typeset them inside an iframe component.
+_KATEX_HEAD = r"""<!doctype html><html><head>
+<meta charset="utf-8">
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css">
+<script src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js"></script>
+<style>
+/* White "paper" card so the text stays readable in both light and dark
+   Streamlit themes (the iframe is otherwise transparent and our text is dark). */
+html,body{background:#ffffff;}
+body{font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif;font-size:15px;line-height:1.55;color:#111111;margin:0;padding:14px;}
+table{border-collapse:collapse;margin:6px 0;} td,th{border:1px solid #bbb;padding:3px 6px;color:#111111;}
+[data-label="SectionHeader"],[data-label="PageHeader"]{font-weight:600;}
+</style></head><body>
+"""
+
+_KATEX_TAIL = r"""
+<script>
+renderMathInElement(document.body, {
+  delimiters: [
+    {left: "\\[", right: "\\]", display: true},
+    {left: "\\(", right: "\\)", display: false}
+  ],
+  throwOnError: false
+});
+</script></body></html>
+"""
+
+_MATH_RE = re.compile(r"<math\b([^>]*)>(.*?)</math>", re.DOTALL | re.IGNORECASE)
+
+
+def _math_to_katex(html_str: str) -> str:
+    """Rewrite <math>...</math> tags into KaTeX \\( \\) / \\[ \\] delimiters."""
+
+    def repl(m: "re.Match") -> str:
+        attrs, inner = m.group(1), m.group(2)
+        if re.search(r"""display\s*=\s*["']block["']""", attrs):
+            return "\\[" + inner + "\\]"
+        return "\\(" + inner + "\\)"
+
+    return _MATH_RE.sub(repl, html_str or "")
+
+
+def render_ocr_html(html_str: str, height: int = 400) -> None:
+    """Render OCR HTML with math typeset by KaTeX (iframe component)."""
+    components.html(
+        _KATEX_HEAD + _math_to_katex(html_str) + _KATEX_TAIL,
+        height=height,
+        scrolling=True,
+    )
+
+
+def _assemble_page_html(page: PageOCRResult) -> str:
+    """Reconstruct a div-block whole-page HTML from a PageOCRResult."""
+    parts: List[str] = []
+    for blk in page.blocks:
+        if blk.skipped:
+            continue
+        x0, y0, x1, y1 = (int(c) for c in blk.bbox)
+        body = blk.html or ""
+        parts.append(
+            f'<div data-bbox="{x0} {y0} {x1} {y1}" data-label="{blk.label}">{body}</div>'
+        )
+    return "\n".join(parts)
+
+
+def _show_timing(label: str, elapsed_s: float, extra: str = "") -> None:
+    """Render a small caption with wall-clock + optional extra detail."""
+    detail = f" — {extra}" if extra else ""
+    st.caption(f"⏱ {label}: {elapsed_s * 1000:.0f} ms ({elapsed_s:.2f}s){detail}")
+
+
+@st.cache_resource()
+def load_predictors_cached():
+    manager = SuryaInferenceManager()
+    layout_predictor = LayoutPredictor(manager)
+    rec_predictor = RecognitionPredictor(manager)
+    table_rec_predictor = TableRecPredictor(manager)
+
+    # Lazy-import detection / ocr_error to keep startup snappy when the user
+    # only wants VLM modes
+    from surya.detection import DetectionPredictor
+    from surya.ocr_error import OCRErrorPredictor
+
+    return {
+        "manager": manager,
+        "layout": layout_predictor,
+        "recognition": rec_predictor,
+        "table_rec": table_rec_predictor,
+        "detection": DetectionPredictor(),
+        "ocr_error": OCRErrorPredictor(),
+    }
+
+
+@st.cache_resource()
+def load_fast_layout():
+    from surya.fast_layout import FastLayoutPredictor
+
+    return FastLayoutPredictor()
+
+
+def _layout_predictor(use_fast: bool):
+    return load_fast_layout() if use_fast else predictors["layout"]
+
+
+def text_detection(img) -> tuple[Image.Image, TextDetectionResult, float]:
+    t = time.perf_counter()
+    text_pred = predictors["detection"]([img])[0]
+    elapsed = time.perf_counter() - t
+    text_polygons = [p.polygon for p in text_pred.bboxes]
+    det_img = draw_polys_on_image(text_polygons, img.copy())
+    return det_img, text_pred, elapsed
+
+
+def layout_detection(
+    img, use_fast: bool = False
+) -> tuple[Image.Image, LayoutResult, float]:
+    t = time.perf_counter()
+    pred = _layout_predictor(use_fast)([img])[0]
+    elapsed = time.perf_counter() - t
+    polygons = [p.polygon for p in pred.bboxes]
+    labels = [
+        f"{p.label}-{p.position}-c{p.count}-{round(p.confidence or 0, 2)}"
+        for p in pred.bboxes
+    ]
+    annotated = draw_polys_on_image(
+        polygons, img.copy(), labels=labels, label_font_size=14
+    )
+    return annotated, pred, elapsed
+
+
+def block_ocr(img) -> tuple[Image.Image, PageOCRResult, LayoutResult, float, float]:
+    """Layout → block crops → BLOCK_PROMPT. Returns layout + block-OCR timings."""
+    t_layout = time.perf_counter()
+    layout = predictors["layout"]([img])[0]
+    layout_elapsed = time.perf_counter() - t_layout
+
+    t_blocks = time.perf_counter()
+    page_results = predictors["recognition"]([img], [layout])
+    blocks_elapsed = time.perf_counter() - t_blocks
+    page = page_results[0]
+
+    annotated = img.copy()
+    draw = ImageDraw.Draw(annotated)
+    for blk in page.blocks:
+        x0, y0, x1, y1 = blk.bbox
+        color = "red" if blk.error else ("orange" if blk.skipped else "green")
+        draw.rectangle((x0, y0, x1, y1), outline=color, width=3)
+        draw.text((x0 + 4, y0 + 4), f"{blk.reading_order} {blk.label}", fill=color)
+    return annotated, page, layout, layout_elapsed, blocks_elapsed
+
+
+def full_page_ocr(img) -> tuple[Image.Image, PageOCRResult, float]:
+    """Single HIGH_ACCURACY_BBOX_PROMPT call on the whole page."""
+    t = time.perf_counter()
+    page_results = predictors["recognition"]([img], full_page=True)
+    elapsed = time.perf_counter() - t
+    page = page_results[0]
+    annotated = img.copy()
+    draw = ImageDraw.Draw(annotated)
+    for blk in page.blocks:
+        x0, y0, x1, y1 = blk.bbox
+        color = "red" if blk.error else ("orange" if blk.skipped else "green")
+        draw.rectangle((x0, y0, x1, y1), outline=color, width=3)
+        draw.text((x0 + 4, y0 + 4), f"{blk.reading_order} {blk.label}", fill=color)
+    return annotated, page, elapsed
+
+
+def table_recognition(
+    img: Image.Image,
+    mode: str,
+    skip_table_detection: bool,
+    use_fast_layout: bool = False,
+) -> tuple[Image.Image, List[TableResult], float, float]:
+    """Returns (annotated_img, table_preds, layout_elapsed, table_rec_elapsed)."""
+    layout_elapsed = 0.0
+    if skip_table_detection:
+        table_imgs = [img]
+        table_bboxes = [(0, 0, img.size[0], img.size[1])]
+    else:
+        t = time.perf_counter()
+        layout = _layout_predictor(use_fast_layout)([img])[0]
+        layout_elapsed = time.perf_counter() - t
+        tables = [b for b in layout.bboxes if b.label in ("Table", "TableOfContents")]
+        if not tables:
+            return img.copy(), [], layout_elapsed, 0.0
+        table_bboxes = [tuple(int(c) for c in b.bbox) for b in tables]
+        table_imgs = [img.crop(b) for b in table_bboxes]
+
+    t = time.perf_counter()
+    if mode == "full":
+        table_preds = predictors["table_rec"].predict_full(table_imgs)
+    else:
+        table_preds = predictors["table_rec"].predict_simple(table_imgs)
+    table_rec_elapsed = time.perf_counter() - t
+
+    out_img = img.copy()
+    for pred, table_img, tbbox in zip(table_preds, table_imgs, table_bboxes):
+        if pred.error or pred.mode != "simple" or not pred.rows:
+            continue
+        row_bboxes = [r.bbox for r in pred.rows]
+        col_bboxes = [c.bbox for c in pred.cols]
+        row_labels = [r.label for r in pred.rows]
+        col_labels = [c.label for c in pred.cols]
+        annot = table_img.copy()
+        annot = draw_bboxes_on_image(
+            row_bboxes, annot, labels=row_labels, label_font_size=14, color="blue"
+        )
+        annot = draw_bboxes_on_image(
+            col_bboxes, annot, labels=col_labels, label_font_size=14, color="red"
+        )
+        # Paste annotated crop back at the table's position in the page.
+        out_img.paste(annot, (tbbox[0], tbbox[1]))
+    return out_img, table_preds, layout_elapsed, table_rec_elapsed
+
+
+def ocr_errors(pdf_file, page_count, sample_len=512, max_samples=10, max_pages=15):
+    from pdftext.extraction import plain_text_output
+
+    with tempfile.NamedTemporaryFile(suffix=".pdf") as f:
+        f.write(pdf_file.getvalue())
+        f.seek(0)
+
+        page_middle = page_count // 2
+        page_range = range(
+            max(page_middle - max_pages, 0), min(page_middle + max_pages, page_count)
+        )
+        text = plain_text_output(f.name, page_range=page_range)
+
+    sample_gap = len(text) // max_samples
+    if len(text) == 0 or sample_gap == 0:
+        return "This PDF has no text or very little text", ["no text"]
+
+    if sample_gap < sample_len:
+        sample_gap = sample_len
+
+    samples = []
+    for i in range(0, len(text), sample_gap):
+        samples.append(text[i : i + sample_len])
+
+    results = predictors["ocr_error"](samples)
+    label = "This PDF has good text."
+    if results.labels.count("bad") / len(results.labels) > 0.2:
+        label = "This PDF may have garbled or bad OCR text."
+    return label, results.labels
+
+
+def open_pdf(pdf_file):
+    stream = io.BytesIO(pdf_file.getvalue())
+    return pypdfium2.PdfDocument(stream)
+
+
+@st.cache_data()
+def get_page_image(pdf_file, page_num, dpi=settings.IMAGE_DPI):
+    doc = open_pdf(pdf_file)
+    renderer = doc.render(
+        pypdfium2.PdfBitmap.to_pil,
+        page_indices=[page_num - 1],
+        scale=dpi / 72,
+    )
+    png = list(renderer)[0]
+    png_image = png.convert("RGB")
+    doc.close()
+    return png_image
+
+
+@st.cache_data()
+def page_counter(pdf_file):
+    doc = open_pdf(pdf_file)
+    doc_len = len(doc)
+    doc.close()
+    return doc_len
+
+
+st.set_page_config(layout="wide")
+col1, col2 = st.columns([0.55, 0.45])
+
+predictors = load_predictors_cached()
+
+st.markdown(
+    """
+# Surya 2 Demo
+
+VLM-backed layout, OCR, and table recognition. The model runs in a local
+`llama-server` (or vllm) process, started on first use.
+
+Modes:
+- **Layout**: page → list of blocks with label + bbox + token count
+- **Block OCR**: layout + per-block HTML
+- **Table Rec (simple)**: row + column bboxes only
+- **Table Rec (full)**: full HTML for each detected table
+"""
+)
+
+in_file = st.sidebar.file_uploader(
+    "PDF file or image:", type=["pdf", "png", "jpg", "jpeg", "gif", "webp"]
+)
+
+if in_file is None:
+    st.stop()
+
+filetype = in_file.type
+page_count = None
+if "pdf" in filetype:
+    page_count = page_counter(in_file)
+    page_number = st.sidebar.number_input(
+        f"Page number out of {page_count}:", min_value=1, value=1, max_value=page_count
+    )
+    # Render at high DPI so the OCR / table-rec demos see fine glyphs.
+    # Layout + detection internally downsample (or accept the small perf hit
+    # at demo scale); we always render and display the high-DPI page here.
+    pil_image = get_page_image(in_file, page_number, settings.IMAGE_DPI_HIGHRES)
+else:
+    pil_image = Image.open(in_file).convert("RGB")
+    page_number = None
+
+run_full_page_ocr = st.sidebar.button("Run Full-Page OCR")
+run_text_det = st.sidebar.button("Run Text Detection")
+run_layout = st.sidebar.button("Run Layout Analysis")
+run_table_rec = st.sidebar.button("Run Table Rec")
+run_block_ocr = st.sidebar.button("Run Block OCR")
+run_ocr_errors = st.sidebar.button("Run bad-PDF-text detection")
+
+use_fast_layout = st.sidebar.checkbox(
+    "Fast layout",
+    value=True,
+    help="Use the fast layout detector.",
+)
+table_mode = st.sidebar.radio(
+    "Table mode",
+    options=["simple", "full"],
+    index=0,
+    help="simple: rows+cols only. full: full HTML.",
+)
+skip_table_detection = st.sidebar.checkbox(
+    "Skip table detection",
+    value=False,
+    help="Treat the entire page/image as a single table.",
+)
+
+if pil_image is None:
+    st.stop()
+
+
+if run_text_det:
+    det_img, text_pred, elapsed = text_detection(pil_image)
+    with col1:
+        _show_timing("Text detection", elapsed, f"{len(text_pred.bboxes)} polys")
+        st.image(det_img, caption="Detected Text", use_container_width=True)
+        st.json(
+            text_pred.model_dump(exclude=["heatmap", "affinity_map"]), expanded=False
+        )
+
+
+if run_layout:
+    annotated, pred, elapsed = layout_detection(pil_image, use_fast=use_fast_layout)
+    with col1:
+        label = "Layout (fast)" if use_fast_layout else "Layout"
+        _show_timing(label, elapsed, f"{len(pred.bboxes)} blocks")
+        st.image(annotated, caption="Detected Layout", use_container_width=True)
+        st.json(pred.model_dump(), expanded=False)
+
+
+if run_block_ocr:
+    annotated, page, layout, t_layout, t_blocks = block_ocr(pil_image)
+    with col1:
+        n_blocks = len(page.blocks)
+        n_ok = sum(1 for b in page.blocks if not b.skipped and not b.error)
+        _show_timing("Block OCR — layout", t_layout, f"{n_blocks} blocks")
+        _show_timing("Block OCR — per-block OCR", t_blocks, f"{n_ok} OCR'd")
+        _show_timing("Block OCR — total", t_layout + t_blocks)
+        st.image(
+            annotated,
+            caption="Block OCR (green=ok, orange=skipped, red=error)",
+            use_container_width=True,
+        )
+        full_html = _assemble_page_html(page)
+        with st.expander("Full page HTML (rendered)", expanded=False):
+            render_ocr_html(full_html, height=600)
+        with st.expander("Full page HTML (source)", expanded=False):
+            st.code(full_html, language="html")
+        for blk in page.blocks:
+            with st.expander(
+                f"#{blk.reading_order} {blk.label} (conf {blk.confidence:.2f})"
+            ):
+                # Diagnostics: show numeric bbox + polygon + a thumbnail with the
+                # drawn rectangle highlighted, then the actual crop fed to OCR.
+                xs = [p[0] for p in blk.polygon]
+                ys = [p[1] for p in blk.polygon]
+                bbox_drawn = [int(min(xs)), int(min(ys)), int(max(xs)), int(max(ys))]
+                cx0 = max(0, int(min(xs)) - 4)
+                cy0 = max(0, int(min(ys)) - 4)
+                cx1 = min(pil_image.size[0], int(max(xs)) + 4)
+                cy1 = min(pil_image.size[1], int(max(ys)) + 4)
+                st.text(
+                    f"bbox(drawn) = {bbox_drawn}\n"
+                    f"crop(ocr)  = {(cx0, cy0, cx1, cy1)}  (= bbox ± 4px pad)"
+                )
+                # Thumbnail with this block's rectangle highlighted in red.
+                thumb = pil_image.copy()
+                ImageDraw.Draw(thumb).rectangle(bbox_drawn, outline="red", width=4)
+                st.image(thumb, caption="this block's drawn rect (red)", width=300)
+                # The actual crop fed to OCR
+                if cx1 > cx0 and cy1 > cy0:
+                    st.image(pil_image.crop((cx0, cy0, cx1, cy1)), caption="OCR crop")
+                if blk.skipped:
+                    st.info("Block skipped (visual label)")
+                elif blk.error:
+                    st.error("Block OCR errored")
+                else:
+                    render_ocr_html(blk.html, height=160)
+                    st.code(blk.html, language="html")
+
+
+if run_full_page_ocr:
+    annotated, page, elapsed = full_page_ocr(pil_image)
+    with col1:
+        n_blocks = len(page.blocks)
+        n_ok = sum(1 for b in page.blocks if not b.skipped and not b.error)
+        _show_timing("Full-Page OCR", elapsed, f"{n_blocks} blocks parsed, {n_ok} OK")
+        st.image(
+            annotated,
+            caption="Full-Page OCR (green=ok, orange=skipped, red=error)",
+            use_container_width=True,
+        )
+        full_html = _assemble_page_html(page)
+        with st.expander("Full page HTML (rendered)", expanded=False):
+            render_ocr_html(full_html, height=600)
+        with st.expander("Full page HTML (source)", expanded=False):
+            st.code(full_html, language="html")
+        for blk in page.blocks:
+            with st.expander(
+                f"#{blk.reading_order} {blk.label} (conf {blk.confidence:.2f})"
+            ):
+                if blk.skipped:
+                    st.info("Block skipped (visual label)")
+                elif blk.error:
+                    st.error("Block OCR errored")
+                else:
+                    render_ocr_html(blk.html, height=160)
+                    st.code(blk.html, language="html")
+
+
+if run_table_rec:
+    table_img, preds, t_layout, t_table = table_recognition(
+        pil_image, table_mode, skip_table_detection, use_fast_layout=use_fast_layout
+    )
+    with col1:
+        if not skip_table_detection:
+            _show_timing("Table Rec — layout", t_layout, f"{len(preds)} tables found")
+        _show_timing(f"Table Rec — {table_mode}", t_table)
+        if not skip_table_detection:
+            _show_timing("Table Rec — total", t_layout + t_table)
+        st.image(table_img, caption="Table Recognition", use_container_width=True)
+        for pred in preds:
+            if pred.mode == "full" and pred.html:
+                with st.expander("Table HTML"):
+                    render_ocr_html(pred.html, height=400)
+                    st.code(pred.html, language="html")
+            else:
+                st.json(pred.model_dump(), expanded=False)
+
+
+if run_ocr_errors:
+    if "pdf" not in filetype:
+        st.error("This feature only works with PDFs.")
+    else:
+        label, results = ocr_errors(in_file, page_count)
+        with col1:
+            st.write(label)
+            st.json(results)
+
+
+with col2:
+    st.image(pil_image, caption="Uploaded Image", use_container_width=True)
diff --git a/surya/scripts/table_recognition.py b/surya/scripts/table_recognition.py
new file mode 100644
index 0000000..70f164a
--- /dev/null
+++ b/surya/scripts/table_recognition.py
@@ -0,0 +1,135 @@
+import os
+import click
+import copy
+import json
+from collections import defaultdict
+
+from surya.common.util import expand_bbox
+from surya.debug.draw import draw_bboxes_on_image
+from surya.inference import SuryaInferenceManager
+from surya.layout import LayoutPredictor
+from surya.logging import configure_logging, get_logger
+from surya.scripts.config import CLILoader
+from surya.table_rec import TableRecPredictor
+
+configure_logging()
+logger = get_logger()
+
+
+@click.command(help="Run table recognition on an input file or folder.")
+@CLILoader.common_options
+@click.option(
+    "--skip_table_detection",
+    is_flag=True,
+    help="Tables are already cropped, so don't re-detect tables.",
+    default=False,
+)
+@click.option(
+    "--mode",
+    type=click.Choice(["simple", "full"]),
+    default="simple",
+    help="simple: rows+cols only (geometric cells). full: full HTML (BLOCK_PROMPT).",
+)
+def table_recognition_cli(
+    input_path: str, skip_table_detection: bool, mode: str, **kwargs
+):
+    # Layout runs on the low-DPI render; table crops come from the high-DPI
+    # image so the table_rec model sees readable cell content.
+    loader = CLILoader(input_path, kwargs, highres=True)
+
+    manager = SuryaInferenceManager()
+    layout_predictor = LayoutPredictor(manager)
+    table_rec_predictor = TableRecPredictor(manager)
+
+    pnums = []
+    prev_name = None
+    for name in loader.names:
+        if prev_name is None or prev_name != name:
+            pnums.append(0)
+        else:
+            pnums.append(pnums[-1] + 1)
+        prev_name = name
+
+    table_imgs = []
+    table_counts = []
+    table_counts_per_img = []
+
+    if skip_table_detection:
+        for img in loader.highres_images:
+            table_imgs.append(img)
+            table_counts.append(1)
+            table_counts_per_img.append(0)
+    else:
+        layout_predictions = layout_predictor(
+            loader.images,
+            target_image_sizes=[img.size for img in loader.highres_images],
+        )
+        for layout_pred, img in zip(layout_predictions, loader.highres_images):
+            tables_on_page = [
+                line
+                for line in layout_pred.bboxes
+                if line.label in ("Table", "TableOfContents")
+            ]
+            table_counts.append(len(tables_on_page))
+            for line in tables_on_page:
+                bbox = expand_bbox(line.bbox)
+                table_imgs.append(img.crop(bbox))
+                table_counts_per_img.append(line.count)
+
+    table_preds = table_rec_predictor(table_imgs, mode=mode)
+
+    img_idx = 0
+    prev_count = 0
+    table_predictions = defaultdict(list)
+    for i in range(sum(table_counts)):
+        while i >= prev_count + table_counts[img_idx]:
+            prev_count += table_counts[img_idx]
+            img_idx += 1
+
+        pred = table_preds[i]
+        orig_name = loader.names[img_idx]
+        pnum = pnums[img_idx]
+        table_img = table_imgs[i]
+
+        out_pred = pred.model_dump()
+        out_pred["page"] = pnum + 1
+        table_idx = i - prev_count
+        out_pred["table_idx"] = table_idx
+        table_predictions[orig_name].append(out_pred)
+
+        if loader.save_images and pred.rows:
+            rows = [line.bbox for line in pred.rows]
+            cols = [line.bbox for line in pred.cols]
+            row_labels = [f"Row {line.row_id}" for line in pred.rows]
+            col_labels = [f"Col {line.col_id}" for line in pred.cols]
+            cells = [line.bbox for line in pred.cells]
+
+            rc_image = copy.deepcopy(table_img)
+            rc_image = draw_bboxes_on_image(
+                rows, rc_image, labels=row_labels, label_font_size=20, color="blue"
+            )
+            rc_image = draw_bboxes_on_image(
+                cols, rc_image, labels=col_labels, label_font_size=20, color="red"
+            )
+            rc_image.save(
+                os.path.join(
+                    loader.result_path,
+                    f"{orig_name}_page{pnum + 1}_table{table_idx}_rc.png",
+                )
+            )
+
+            cell_image = copy.deepcopy(table_img)
+            cell_image = draw_bboxes_on_image(cells, cell_image, color="green")
+            cell_image.save(
+                os.path.join(
+                    loader.result_path,
+                    f"{orig_name}_page{pnum + 1}_table{table_idx}_cells.png",
+                )
+            )
+
+    with open(
+        os.path.join(loader.result_path, "results.json"), "w+", encoding="utf-8"
+    ) as f:
+        json.dump(table_predictions, f, ensure_ascii=False)
+
+    logger.info(f"Wrote results to {loader.result_path}")
diff --git a/surya/scripts/templates/surya_screenshot.html b/surya/scripts/templates/surya_screenshot.html
new file mode 100644
index 0000000..7a95e31
--- /dev/null
+++ b/surya/scripts/templates/surya_screenshot.html
@@ -0,0 +1,365 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Surya · Full-Page OCR</title>
+  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css">
+  <script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js"></script>
+  <script src="https://cdn.jsdelivr.net/npm/html2canvas@1.4.1/dist/html2canvas.min.js"></script>
+  <style>
+    :root {
+      --ink: #0f1115;
+      --bg: #f4f6f9;
+      --panel: #ffffff;
+      --border: #e4e7ec;
+      --accent: #2563eb;
+      --accent-hover: #1d4ed8;
+      --muted: #667085;
+    }
+    * { margin: 0; padding: 0; box-sizing: border-box; }
+    body {
+      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+      background: var(--bg);
+      color: var(--ink);
+      height: 100vh;
+      display: flex;
+      flex-direction: column;
+      overflow: hidden;
+    }
+    header {
+      display: flex; align-items: center; gap: 16px;
+      padding: 12px 20px;
+      background: var(--panel);
+      border-bottom: 1px solid var(--border);
+      box-shadow: 0 1px 3px rgba(16,24,40,.04);
+      z-index: 10;
+    }
+    .brand { display: flex; align-items: center; gap: 10px; }
+    .brand img { height: 30px; width: auto; }
+    .brand .title { font-size: 18px; font-weight: 700; letter-spacing: -.01em; }
+    .brand .sub { font-size: 13px; color: var(--muted); font-weight: 500;
+      padding-left: 10px; margin-left: 4px; border-left: 1px solid var(--border); }
+    .controls { display: flex; align-items: center; gap: 10px; flex: 1; flex-wrap: wrap; }
+    input[type=text] {
+      flex: 1; min-width: 260px; max-width: 560px;
+      padding: 9px 12px; font-size: 14px;
+      border: 1px solid var(--border); border-radius: 8px; background: #fff;
+    }
+    input[type=text]:focus { outline: none; border-color: var(--accent); box-shadow: 0 0 0 3px rgba(37,99,235,.12); }
+    button {
+      padding: 9px 14px; font-size: 14px; font-weight: 600;
+      border: none; border-radius: 8px; cursor: pointer;
+      background: var(--accent); color: #fff; transition: background .15s;
+    }
+    button:hover:not(:disabled) { background: var(--accent-hover); }
+    button.secondary { background: #fff; color: var(--ink); border: 1px solid var(--border); }
+    button.secondary:hover:not(:disabled) { background: #f2f4f7; }
+    button:disabled { opacity: .45; cursor: not-allowed; }
+    .pager { display: flex; align-items: center; gap: 6px; }
+    .pager .pageind { font-size: 13px; color: var(--muted); min-width: 96px; text-align: center; }
+    .pager button { padding: 7px 11px; }
+    .toggle { display: flex; align-items: center; gap: 7px; font-size: 13px; color: var(--muted); cursor: pointer; user-select: none; }
+    .status { font-size: 13px; font-weight: 600; min-width: 80px; }
+    .status.loading { color: #b45309; }
+    .status.error { color: #d92d20; }
+    .status.ok { color: #079455; }
+
+    .stage {
+      flex: 1; display: flex; gap: 16px; padding: 16px; overflow: hidden;
+    }
+    .panel {
+      flex: 1; min-width: 0; display: flex; flex-direction: column;
+      background: var(--panel); border: 1px solid var(--border);
+      border-radius: 12px; overflow: hidden;
+      box-shadow: 0 1px 2px rgba(16,24,40,.05);
+    }
+    .panel-head {
+      padding: 11px 16px; font-size: 13px; font-weight: 700;
+      letter-spacing: .02em; text-transform: uppercase; color: var(--muted);
+      border-bottom: 1px solid var(--border); background: #fcfcfd;
+    }
+    .panel-body { flex: 1; overflow: auto; }
+    .img-wrap {
+      display: flex; align-items: flex-start; justify-content: center;
+      padding: 16px; background: #f0f2f5; min-height: 100%;
+    }
+    #pageCanvas { max-width: 100%; height: auto; border-radius: 6px;
+      box-shadow: 0 2px 10px rgba(16,24,40,.12); background: #fff; }
+    .ocr {
+      padding: 28px 32px; line-height: 1.6; font-size: 16px; color: #1d2433;
+    }
+    .ocr [data-label="SectionHeader"], .ocr [data-label="Title"] { font-weight: 700; font-size: 1.15em; margin: .5em 0 .3em; }
+    .ocr [data-label="PageHeader"], .ocr [data-label="PageFooter"] { color: var(--muted); font-size: .9em; }
+    .ocr table { border-collapse: collapse; margin: 14px 0; width: 100%; }
+    .ocr th, .ocr td { border: 1px solid #d0d5dd; padding: 6px 10px; text-align: left; }
+    .ocr th { background: #f2f4f7; font-weight: 600; }
+    .ocr img { max-width: 100%; height: auto; }
+    .placeholder { padding: 48px 32px; color: var(--muted); font-size: 15px; text-align: center; }
+    #dropOverlay { position: fixed; inset: 0; z-index: 100; display: none;
+      align-items: center; justify-content: center; background: rgba(15,17,21,.55); }
+    #dropOverlay.active { display: flex; }
+    #dropOverlay .drop-card { padding: 40px 64px; border: 3px dashed #fff;
+      border-radius: 16px; color: #fff; font-size: 22px; font-weight: 700;
+      background: rgba(37,99,235,.30); }
+    /* Screenshot mode: size each panel to its content (no scroll clipping),
+       show the page image at full resolution, and flow long OCR output into
+       newspaper-style columns capped to the page height — so a tall transcript
+       extends rightward instead of leaving dead space beside the page. */
+    body.capturing { height: auto !important; overflow: visible !important; }
+    body.capturing .stage { height: auto !important; overflow: visible !important;
+      align-items: flex-start !important; width: max-content !important; }
+    body.capturing .panel { flex: 0 0 auto !important; }
+    body.capturing .panel-body { height: auto !important; overflow: visible !important; }
+    body.capturing #pageCanvas { max-width: none !important; }
+    body.capturing #ocr table, body.capturing #ocr img,
+    body.capturing #ocr .katex-display { break-inside: avoid; }
+  </style>
+</head>
+<body>
+  <header>
+    <div class="brand">
+      {% if logo %}<img src="{{ logo }}" alt="Datalab">{% endif %}
+      <span class="title">Surya</span>
+      <span class="sub">Full-Page OCR</span>
+    </div>
+    <div class="controls">
+      <input type="text" id="filePath" placeholder="Drop a file, Browse, or type a server path…">
+      <input type="file" id="fileInput" accept=".pdf,.png,.jpg,.jpeg,.gif,.webp" style="display:none" onchange="onPick(this)">
+      <button class="secondary" onclick="document.getElementById('fileInput').click()">Browse</button>
+      <button class="secondary" id="loadBtn" onclick="loadFile()">Load</button>
+      <div class="pager">
+        <button class="secondary" id="prevBtn" onclick="changePage(-1)" disabled>◀</button>
+        <span class="pageind" id="pageInd">—</span>
+        <button class="secondary" id="nextBtn" onclick="changePage(1)" disabled>▶</button>
+      </div>
+      <button id="runBtn" onclick="runOCR()" disabled>Run Full-Page OCR</button>
+      <label class="toggle"><input type="checkbox" id="showBoxes" checked onchange="drawLeft()"> Layout boxes</label>
+      <button class="secondary" id="copyBtn" onclick="copyHtml()" disabled>Copy HTML</button>
+      <button class="secondary" id="shotBtn" onclick="saveScreenshot()" disabled>Save Screenshot</button>
+      <span class="status" id="status"></span>
+    </div>
+  </header>
+
+  <div class="stage">
+    <div class="panel">
+      <div class="panel-head">PDF Page</div>
+      <div class="panel-body"><div class="img-wrap"><canvas id="pageCanvas"></canvas></div></div>
+    </div>
+    <div class="panel">
+      <div class="panel-head">Full-Page OCR</div>
+      <div class="panel-body"><div class="ocr" id="ocr"><div class="placeholder">Load a file, scroll to a page, then run full-page OCR.</div></div></div>
+    </div>
+  </div>
+
+  <div id="dropOverlay"><div class="drop-card">Drop a PDF or image to load</div></div>
+
+  <script>
+    const S = { path: "", name: "", page: 0, count: 0, img: null, blocks: null, html: null, ocrPage: null };
+
+    const $ = (id) => document.getElementById(id);
+    function setStatus(msg, kind) { const s = $("status"); s.textContent = msg || ""; s.className = "status " + (kind || ""); }
+
+    async function post(url, body) {
+      const r = await fetch(url, { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(body) });
+      const data = await r.json();
+      if (!r.ok) throw new Error(data.error || "Request failed");
+      return data;
+    }
+
+    async function loadFile() {
+      const val = $("filePath").value.trim();
+      if (!val) { setStatus("Drop a file or enter a path", "error"); return; }
+      // If the box still shows the name of an uploaded file, just reload it.
+      if (S.path && val === S.name) { S.page = 0; await showPage(); return; }
+      setStatus("Loading…", "loading");
+      try {
+        const info = await post("/info", { file_path: val });
+        S.path = val; S.name = val; S.count = info.page_count; S.page = 0;
+        await showPage();
+        setStatus("");
+      } catch (e) { setStatus(e.message, "error"); }
+    }
+
+    function onPick(input) { if (input.files && input.files[0]) uploadFile(input.files[0]); }
+
+    async function uploadFile(file) {
+      setStatus("Uploading…", "loading");
+      const fd = new FormData(); fd.append("file", file);
+      try {
+        const r = await fetch("/upload", { method: "POST", body: fd });
+        const data = await r.json();
+        if (!r.ok) throw new Error(data.error || "Upload failed");
+        S.path = data.file_path; S.name = data.name; S.count = data.page_count; S.page = 0;
+        $("filePath").value = data.name;
+        await showPage();
+        setStatus("");
+      } catch (e) { setStatus(e.message, "error"); }
+    }
+
+    async function showPage() {
+      setStatus("Rendering…", "loading");
+      try {
+        const data = await post("/page", { file_path: S.path, page: S.page });
+        // New page → clear any previous OCR output.
+        S.blocks = null; S.html = null; S.ocrPage = null;
+        $("ocr").innerHTML = '<div class="placeholder">Run full-page OCR to see the extracted content.</div>';
+        $("shotBtn").disabled = true;
+        $("copyBtn").disabled = true;
+        loadImage(data.image_base64, () => drawLeft());
+        updatePager();
+        $("runBtn").disabled = false;
+        setStatus("");
+      } catch (e) { setStatus(e.message, "error"); }
+    }
+
+    function loadImage(src, cb) {
+      const im = new Image();
+      im.onload = () => { S.img = im; cb && cb(); };
+      im.src = src;
+    }
+
+    function updatePager() {
+      $("pageInd").textContent = S.count ? `Page ${S.page + 1} of ${S.count}` : "—";
+      $("prevBtn").disabled = S.page <= 0;
+      $("nextBtn").disabled = S.page >= S.count - 1;
+    }
+
+    function changePage(delta) {
+      const next = S.page + delta;
+      if (next < 0 || next >= S.count) return;
+      S.page = next;
+      showPage();
+    }
+
+    function drawLeft() {
+      if (!S.img) return;
+      const c = $("pageCanvas"), ctx = c.getContext("2d");
+      c.width = S.img.naturalWidth; c.height = S.img.naturalHeight;
+      ctx.drawImage(S.img, 0, 0);
+      if (!S.blocks || !$("showBoxes").checked) return;
+      ctx.lineWidth = 3;
+      ctx.font = 'bold 15px -apple-system, "Segoe UI", sans-serif';
+      ctx.textBaseline = "top";
+      S.blocks.forEach((b) => {
+        const [x1, y1, x2, y2] = b.bbox;
+        ctx.strokeStyle = b.color; ctx.fillStyle = b.color + "26";
+        ctx.fillRect(x1, y1, x2 - x1, y2 - y1);
+        ctx.strokeRect(x1, y1, x2 - x1, y2 - y1);
+        const tw = ctx.measureText(b.label).width, ly = Math.max(y1 - 22, 0);
+        ctx.fillStyle = b.color; ctx.fillRect(x1, ly, tw + 12, 21);
+        ctx.fillStyle = "#fff"; ctx.fillText(b.label, x1 + 6, ly + 3);
+      });
+    }
+
+    async function runOCR() {
+      if (!S.path) return;
+      setStatus("Running OCR…", "loading");
+      $("runBtn").disabled = true;
+      try {
+        const data = await post("/process", { file_path: S.path, page: S.page });
+        S.blocks = data.blocks; S.html = data.html || ""; S.ocrPage = S.page;
+        loadImage(data.image_base64, () => drawLeft());
+        const ocr = $("ocr");
+        ocr.innerHTML = data.html || '<div class="placeholder">No content detected on this page.</div>';
+        renderMath(ocr);
+        $("shotBtn").disabled = false;
+        $("copyBtn").disabled = !S.html;
+        setStatus(`${data.n_blocks} blocks`, "ok");
+      } catch (e) { setStatus(e.message, "error"); }
+      finally { $("runBtn").disabled = false; }
+    }
+
+    function renderMath(root) {
+      root.querySelectorAll("math").forEach((el) => {
+        const block = el.getAttribute("display") === "block";
+        try {
+          const span = document.createElement(block ? "div" : "span");
+          span.innerHTML = katex.renderToString(el.textContent, { displayMode: block, throwOnError: false });
+          el.replaceWith(span);
+        } catch (e) { /* leave raw on failure */ }
+      });
+    }
+
+    function copyHtml() {
+      if (!S.html) return;
+      const done = () => setStatus("HTML copied", "ok");
+      if (navigator.clipboard && navigator.clipboard.writeText) {
+        navigator.clipboard.writeText(S.html).then(done).catch(() => fallbackCopy(S.html, done));
+      } else {
+        fallbackCopy(S.html, done);
+      }
+    }
+
+    function fallbackCopy(text, done) {
+      const ta = document.createElement("textarea");
+      ta.value = text; ta.style.position = "fixed"; ta.style.opacity = "0";
+      document.body.appendChild(ta); ta.select();
+      try { document.execCommand("copy"); done(); }
+      catch (e) { setStatus("Copy failed", "error"); }
+      document.body.removeChild(ta);
+    }
+
+    async function saveScreenshot() {
+      setStatus("Capturing…", "loading");
+      const stage = document.querySelector(".stage");
+      const canvas = $("pageCanvas");
+      const ocr = $("ocr");
+      const COLW = 360, GAP = 28, PADX = 64, MAXH = 1600; // PADX = #ocr l+r padding
+      const settle = () => new Promise((r) => requestAnimationFrame(() => requestAnimationFrame(r)));
+      document.body.classList.add("capturing");
+      // Shrink the page to <= MAXH tall (keeps the capture a sane size), and use
+      // its width as the baseline so page + OCR land ~50/50.
+      const aspect = (canvas.width || 1) / (canvas.height || 1);
+      const H = Math.min(canvas.height || MAXH, MAXH);
+      const Wp = Math.round(H * aspect);
+      Object.assign(canvas.style, { maxWidth: "none", width: Wp + "px", height: "auto" });
+      // Measure OCR height in a single COLW-wide column.
+      Object.assign(ocr.style, { width: COLW + "px", height: "auto", columnWidth: "", columnGap: "", columnFill: "" });
+      try {
+        await settle();
+        const totalH = ocr.scrollHeight;
+        const n = Math.max(1, Math.ceil(totalH / H));         // page-height columns needed
+        const blockW = n * COLW + (n - 1) * GAP + PADX;
+        // Never narrower than the page → OCR fills its ~half (extra columns just
+        // leave trailing whitespace); only goes wider than the page when the
+        // transcript genuinely needs more columns than fit at page width.
+        const ocrW = Math.max(Wp, blockW);
+        Object.assign(ocr.style, {
+          width: ocrW + "px", height: H + "px",
+          columnWidth: COLW + "px", columnGap: GAP + "px", columnFill: "auto",
+        });
+        await settle();
+        const w = stage.scrollWidth, h = stage.scrollHeight;
+        const shot = await html2canvas(stage, {
+          backgroundColor: "#f4f6f9", scale: 1.5, useCORS: true, logging: false,
+          width: w, height: h, windowWidth: w, windowHeight: h, scrollX: 0, scrollY: 0,
+        });
+        const a = document.createElement("a");
+        a.href = shot.toDataURL("image/png");
+        a.download = `surya_ocr_page_${(S.ocrPage ?? S.page) + 1}.png`;
+        a.click();
+        setStatus(n > 1 ? `Saved (${n} columns)` : "Saved", "ok");
+      } catch (e) { setStatus("Screenshot failed: " + e.message, "error"); }
+      finally {
+        document.body.classList.remove("capturing");
+        Object.assign(ocr.style, { width: "", height: "", columnWidth: "", columnGap: "", columnFill: "" });
+        Object.assign(canvas.style, { maxWidth: "", width: "", height: "" });
+      }
+    }
+
+    $("filePath").addEventListener("keypress", (e) => { if (e.key === "Enter") loadFile(); });
+
+    // Drag & drop anywhere in the window.
+    let dragDepth = 0;
+    window.addEventListener("dragenter", (e) => { e.preventDefault(); dragDepth++; $("dropOverlay").classList.add("active"); });
+    window.addEventListener("dragover", (e) => { e.preventDefault(); });
+    window.addEventListener("dragleave", (e) => { e.preventDefault(); if (--dragDepth <= 0) { dragDepth = 0; $("dropOverlay").classList.remove("active"); } });
+    window.addEventListener("drop", (e) => {
+      e.preventDefault(); dragDepth = 0; $("dropOverlay").classList.remove("active");
+      const f = e.dataTransfer.files && e.dataTransfer.files[0];
+      if (f) uploadFile(f);
+    });
+  </script>
+</body>
+</html>
diff --git a/surya/settings.py b/surya/settings.py
new file mode 100644
index 0000000..b133361
--- /dev/null
+++ b/surya/settings.py
@@ -0,0 +1,178 @@
+import os
+from typing import Callable, Dict, Optional
+
+import torch
+from dotenv import find_dotenv
+from pydantic import computed_field
+from pydantic_settings import BaseSettings
+from pathlib import Path
+from platformdirs import user_cache_dir
+
+
+class Settings(BaseSettings):
+    # General
+    TORCH_DEVICE: Optional[str] = None
+    IMAGE_DPI: int = 96  # used for layout + text detection (coarse structure)
+    IMAGE_DPI_HIGHRES: int = 192  # used for recognition + table rec (fine glyphs)
+    IN_STREAMLIT: bool = False
+    DISABLE_TQDM: bool = False
+    S3_BASE_URL: str = "https://models.datalab.to"
+    PARALLEL_DOWNLOAD_WORKERS: int = 10
+    MODEL_CACHE_DIR: str = str(Path(user_cache_dir("datalab")) / "models")
+    LOGLEVEL: str = "INFO"
+
+    # Paths
+    RESULT_DIR: str = "results"
+    BASE_DIR: str = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    FONT_DIR: str = os.path.join(BASE_DIR, "static", "fonts")
+
+    @computed_field
+    def TORCH_DEVICE_MODEL(self) -> str:
+        if self.TORCH_DEVICE is not None:
+            return self.TORCH_DEVICE
+        if torch.cuda.is_available():
+            return "cuda"
+        if torch.backends.mps.is_available():
+            return "mps"
+        return "cpu"
+
+    # ---- Surya2 inference (VLM-backed: vllm | llamacpp) ---------------------
+    SURYA_MODEL_CHECKPOINT: str = "datalab-to/surya-ocr-2"
+    SURYA_GGUF_REPO: str = "datalab-to/surya-ocr-2-gguf"
+    SURYA_GGUF_MODEL_FILE: str = "surya-2.gguf"
+    SURYA_GGUF_MMPROJ_FILE: str = "surya-2-mmproj.gguf"
+    # If set, used directly instead of HF download (handy for local-conversion testing)
+    SURYA_GGUF_LOCAL_MODEL_PATH: Optional[str] = None
+    SURYA_GGUF_LOCAL_MMPROJ_PATH: Optional[str] = None
+
+    # Backend selection
+    SURYA_INFERENCE_BACKEND: Optional[str] = None  # "vllm" | "llamacpp" | None (auto)
+    SURYA_INFERENCE_URL: Optional[str] = None  # external server, skip spawn
+    SURYA_INFERENCE_AUTOSTART: bool = True
+    # Leave an auto-spawned server running after the process exits so later
+    # commands attach to it instead of re-spawning (avoids repeated startup /
+    # model-load cost). Stop it manually when done — see `surya/inference`.
+    SURYA_INFERENCE_KEEP_ALIVE: bool = False
+    SURYA_INFERENCE_HOST: str = "127.0.0.1"
+    SURYA_INFERENCE_PORT: Optional[int] = None  # None = pick a free port
+    # Client-side concurrent request count. None = let the backend pick a
+    # sensible default (vllm scales to the server's max_num_seqs / GPU
+    # capacity; llama.cpp uses a conservative slot count). Set an int to
+    # override.
+    SURYA_INFERENCE_PARALLEL: Optional[int] = None
+    # Per-parallel-slot KV-cache budget for the llama.cpp backend. Worst-case
+    # one OCR request: ~2k for image prefill + SURYA_MAX_TOKENS_FULL_PAGE
+    # (8192) generation + ~2k prompt/chat-template overhead ≈ 12k. Below this
+    # llama-server silently truncates outputs once a slot fills.
+    SURYA_INFERENCE_CTX_PER_SLOT: int = 12288
+    # Optional override for the *total* ctx passed to llama-server. When None
+    # (default), total = max(16384, PARALLEL * CTX_PER_SLOT). Set this only
+    # if you've hand-tuned for a specific machine.
+    SURYA_INFERENCE_CTX_SIZE: Optional[int] = None
+    SURYA_INFERENCE_TIMEOUT_SECONDS: float = 600.0
+    SURYA_INFERENCE_STARTUP_TIMEOUT: float = 600.0
+    SURYA_INFERENCE_LOGPROBS: bool = True
+    # Force layout/table_rec output through a JSON schema via guided decoding.
+    # Eliminates malformed-JSON failures at small decode-throughput cost.
+    SURYA_GUIDED_LAYOUT: bool = True
+    # Disabled: with no minItems in TABLE_REC_JSON_SCHEMA, the constrained
+    # decoder closes the array after one element at temperature=0. The model
+    # produces well-formed JSON without the schema.
+    SURYA_GUIDED_TABLE_REC: bool = False
+
+    # Token budgets
+    SURYA_MAX_TOKENS_LAYOUT: int = 3072
+    SURYA_MAX_TOKENS_TABLE_REC: int = 3072
+    SURYA_MAX_TOKENS_BLOCK_CEILING: int = 8192
+    SURYA_MAX_TOKENS_FULL_PAGE: int = 12288
+
+    # Full-page OCR: progressive-temperature regeneration before block-mode fallback.
+    # Off by default (single greedy pass -> block fallback); opt-in for benchmarking.
+    SURYA_FULLPAGE_REGEN: bool = False
+
+    BBOX_SCALE: int = 1000
+
+    # vllm
+    VLLM_DOCKER_IMAGE: str = "vllm/vllm-openai:v0.20.1"
+    VLLM_API_KEY: str = "EMPTY"
+    VLLM_GPUS: str = "0"
+    VLLM_GPU_TYPE: str = "4090"
+    # bfloat16 needs an Ampere+ GPU (compute capability >= 8.0). On older cards
+    # (e.g. T4 / Turing) vllm refuses to start with bf16 — set float16 there.
+    VLLM_DTYPE: str = "bfloat16"
+    VLLM_MAX_MODEL_LEN: int = 18000
+    VLLM_GPU_MEMORY_UTILIZATION: float = 0.85
+    VLLM_ENABLE_MTP: bool = True
+    VLLM_MTP_TOKENS: int = 2
+    VLLM_EXTRA_ARGS: Optional[str] = None
+    DOCKER_HF_CACHE_PATH: str = "~/.cache/huggingface"
+
+    # llama.cpp
+    LLAMA_CPP_BINARY: str = "llama-server"
+    LLAMA_CPP_NGL: int = 99  # all layers on GPU (Metal on macOS, CUDA on Linux GPU); harmless no-op on pure-CPU builds
+    LLAMA_CPP_NO_MMPROJ_OFFLOAD: bool = False
+    LLAMA_CPP_EXTRA_ARGS: Optional[str] = None
+
+    # ---- Detection (kept) ---------------------------------------------------
+    DETECTOR_BATCH_SIZE: Optional[int] = None
+    DETECTOR_MODEL_CHECKPOINT: str = "s3://text_detection/2025_05_07"
+    DETECTOR_IMAGE_CHUNK_HEIGHT: int = 1400
+    DETECTOR_TEXT_THRESHOLD: float = 0.6
+    DETECTOR_BLANK_THRESHOLD: float = 0.35
+    DETECTOR_POSTPROCESSING_CPU_WORKERS: int = min(8, os.cpu_count())
+    DETECTOR_MIN_PARALLEL_THRESH: int = 3
+    DETECTOR_BOX_Y_EXPAND_MARGIN: float = 0.05
+
+    # ---- OCR Error (kept) ---------------------------------------------------
+    OCR_ERROR_MODEL_CHECKPOINT: str = "s3://ocr_error_detection/2025_02_18"
+    OCR_ERROR_BATCH_SIZE: Optional[int] = None
+
+    # ---- Fast layout (rf-detr, CPU) ------------------------------------------
+    # Lightweight detector. Checkpoint may be a local dir (rf-detr .pth + config.json),
+    # an hf://<repo>/<subfolder> ref, or an s3:// path.
+    # Override via FAST_LAYOUT_MODEL_CHECKPOINT.
+    FAST_LAYOUT_MODEL_CHECKPOINT: str = "hf://datalab-to/surya_layout2"
+    FAST_LAYOUT_BATCH_SIZE: Optional[int] = None
+    FAST_LAYOUT_CONFIDENCE_THRESHOLD: float = 0.4
+    FAST_ORDER_MODEL_CHECKPOINT: str = "hf://datalab-to/surya_layout2/order"
+    # Run the learned reading-order head after fast layout. When False, boxes come
+    # back in raster order (top-to-bottom, left-to-right) and the order model is
+    # neither loaded nor run — saves latency at the cost of reading-order quality.
+    FAST_LAYOUT_USE_ORDER: bool = True
+    # Device for the rf-detr fast detector. None = auto (cuda > mps > cpu). Override to
+    # force "cpu"/"cuda"/"mps".
+    FAST_DETECTOR_DEVICE: Optional[str] = None
+
+    # ---- Debug / draw fonts (label rendering on annotated images) ----------
+    RECOGNITION_RENDER_FONTS: Dict[str, str] = {
+        "all": os.path.join(FONT_DIR, "GoNotoCurrent-Regular.ttf"),
+        "zh": os.path.join(FONT_DIR, "GoNotoCJKCore.ttf"),
+        "ja": os.path.join(FONT_DIR, "GoNotoCJKCore.ttf"),
+        "ko": os.path.join(FONT_DIR, "GoNotoCJKCore.ttf"),
+    }
+    RECOGNITION_FONT_DL_BASE: str = (
+        "https://github.com/satbyy/go-noto-universal/releases/download/v7.0"
+    )
+
+    @computed_field
+    def MODEL_DTYPE(self) -> torch.dtype:
+        if self.TORCH_DEVICE_MODEL == "cpu":
+            return torch.float32
+        return torch.float16
+
+    @computed_field
+    def MODEL_DTYPE_BFLOAT(self) -> torch.dtype:
+        if self.TORCH_DEVICE_MODEL == "cpu":
+            return torch.float32
+        return torch.bfloat16
+
+    @computed_field
+    def INFERENCE_MODE(self) -> Callable:
+        return torch.inference_mode
+
+    class Config:
+        env_file = find_dotenv("local.env")
+        extra = "ignore"
+
+
+settings = Settings()
diff --git a/surya/table_rec/__init__.py b/surya/table_rec/__init__.py
new file mode 100644
index 0000000..c52d19b
--- /dev/null
+++ b/surya/table_rec/__init__.py
@@ -0,0 +1,217 @@
+"""TableRecPredictor: dual-path table structure recognition.
+
+- predict_simple: TABLE_REC_PROMPT → rows + columns only, cells derived
+  geometrically (row × column intersections).
+- predict_full: BLOCK_PROMPT on the table crop → full <table> HTML with
+  colspan / rowspan / <th>. The HTML lives on TableResult.html for marker to
+  consume directly.
+"""
+
+from __future__ import annotations
+
+from typing import List, Optional
+
+from PIL import Image
+
+from surya.inference import SuryaInferenceManager, get_default_manager
+from surya.inference.parsers import clean_block_html, denorm_bbox, parse_table_rec
+from surya.inference.prompts import (
+    PROMPT_TYPE_BLOCK,
+    PROMPT_TYPE_TABLE_REC,
+    TABLE_REC_JSON_SCHEMA,
+)
+from surya.inference.schema import BatchInputItem
+from surya.inference.util import image_token_budget
+from surya.logging import get_logger
+from surya.settings import settings
+from surya.table_rec.schema import TableCell, TableCol, TableResult, TableRow
+
+logger = get_logger()
+
+
+def _polygon_from_bbox(bbox):
+    x0, y0, x1, y1 = bbox
+    return [[x0, y0], [x1, y0], [x1, y1], [x0, y1]]
+
+
+def _intersect_bbox(a, b):
+    x0 = max(a[0], b[0])
+    y0 = max(a[1], b[1])
+    x1 = min(a[2], b[2])
+    y1 = min(a[3], b[3])
+    if x1 <= x0 or y1 <= y0:
+        return None
+    return (x0, y0, x1, y1)
+
+
+class TableRecPredictor:
+    def __init__(self, manager: Optional[SuryaInferenceManager] = None):
+        self.manager = manager
+        self._disable_tqdm = settings.DISABLE_TQDM
+
+    @property
+    def disable_tqdm(self) -> bool:
+        return self._disable_tqdm
+
+    @disable_tqdm.setter
+    def disable_tqdm(self, value: bool) -> None:
+        self._disable_tqdm = bool(value)
+
+    def to(self, *args, **kwargs):
+        return
+
+    def __call__(
+        self, images: List[Image.Image], mode: str = "simple"
+    ) -> List[TableResult]:
+        if mode == "full":
+            return self.predict_full(images)
+        return self.predict_simple(images)
+
+    def predict_simple(self, images: List[Image.Image]) -> List[TableResult]:
+        if not images:
+            return []
+        manager = self.manager or get_default_manager()
+        guided = TABLE_REC_JSON_SCHEMA if settings.SURYA_GUIDED_TABLE_REC else None
+        batch = [
+            BatchInputItem(
+                image=img,
+                prompt_type=PROMPT_TYPE_TABLE_REC,
+                max_tokens=settings.SURYA_MAX_TOKENS_TABLE_REC,
+                guided_json=guided,
+            )
+            for img in images
+        ]
+        outputs = manager.generate(batch)
+
+        results: List[TableResult] = []
+        for img, out in zip(images, outputs):
+            w, h = img.size
+            page_bbox = [0, 0, float(w), float(h)]
+            if out.error or not out.raw:
+                results.append(
+                    TableResult(
+                        rows=[],
+                        cols=[],
+                        cells=[],
+                        image_bbox=page_bbox,
+                        raw=out.raw,
+                        mode="simple",
+                        error=True,
+                    )
+                )
+                continue
+            try:
+                elements = parse_table_rec(out.raw)
+            except Exception as e:
+                logger.warning(
+                    f"Table rec parse failed: {e}; raw[:200]={out.raw[:200]!r}"
+                )
+                results.append(
+                    TableResult(
+                        rows=[],
+                        cols=[],
+                        cells=[],
+                        image_bbox=page_bbox,
+                        raw=out.raw,
+                        mode="simple",
+                        error=True,
+                    )
+                )
+                continue
+
+            rows: List[TableRow] = []
+            cols: List[TableCol] = []
+            for el in elements:
+                pixel_bbox = denorm_bbox(el.bbox, w, h, scale=settings.BBOX_SCALE)
+                poly = _polygon_from_bbox(pixel_bbox)
+                if el.label == "Row":
+                    rows.append(TableRow(polygon=poly, row_id=len(rows)))
+                else:
+                    cols.append(TableCol(polygon=poly, col_id=len(cols)))
+
+            # Derive cells geometrically (row × column intersections)
+            cells: List[TableCell] = []
+            cell_id = 0
+            for row in rows:
+                for col in cols:
+                    inter = _intersect_bbox(row.bbox, col.bbox)
+                    if inter is None:
+                        continue
+                    cells.append(
+                        TableCell(
+                            polygon=_polygon_from_bbox(inter),
+                            row_id=row.row_id,
+                            col_id=col.col_id,
+                            cell_id=cell_id,
+                        )
+                    )
+                    cell_id += 1
+            results.append(
+                TableResult(
+                    rows=rows,
+                    cols=cols,
+                    cells=cells,
+                    image_bbox=page_bbox,
+                    raw=out.raw,
+                    mode="simple",
+                    error=False,
+                )
+            )
+        return results
+
+    def predict_full(
+        self, images: List[Image.Image], counts: Optional[List[int]] = None
+    ) -> List[TableResult]:
+        """Full-HTML path: BLOCK_PROMPT on table crops. Use when complex
+        structure (spanning cells, headers) matters and ground-truth-style
+        HTML is preferred. `counts` (one per image) shapes max_tokens."""
+        if not images:
+            return []
+        manager = self.manager or get_default_manager()
+        if counts is None:
+            counts = [0] * len(images)
+        batch = []
+        for img, count in zip(images, counts):
+            batch.append(
+                BatchInputItem(
+                    image=img,
+                    prompt_type=PROMPT_TYPE_BLOCK,
+                    max_tokens=image_token_budget(
+                        count,
+                        ceiling=settings.SURYA_MAX_TOKENS_BLOCK_CEILING,
+                        floor=1024,
+                    ),
+                )
+            )
+        outputs = manager.generate(batch)
+        results: List[TableResult] = []
+        for img, out in zip(images, outputs):
+            w, h = img.size
+            page_bbox = [0, 0, float(w), float(h)]
+            if out.error:
+                results.append(
+                    TableResult(
+                        rows=[],
+                        cols=[],
+                        cells=[],
+                        image_bbox=page_bbox,
+                        raw=out.raw,
+                        mode="full",
+                        error=True,
+                    )
+                )
+                continue
+            html = clean_block_html(out.raw)
+            results.append(
+                TableResult(
+                    rows=[],
+                    cols=[],
+                    cells=[],
+                    image_bbox=page_bbox,
+                    raw=out.raw,
+                    html=html,
+                    mode="full",
+                    error=False,
+                )
+            )
+        return results
diff --git a/surya/table_rec/schema.py b/surya/table_rec/schema.py
new file mode 100644
index 0000000..d1cefdc
--- /dev/null
+++ b/surya/table_rec/schema.py
@@ -0,0 +1,48 @@
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+from surya.common.polygon import PolygonBox
+
+
+class TableRow(PolygonBox):
+    row_id: int
+
+    @property
+    def label(self) -> str:
+        return f"Row {self.row_id}"
+
+
+class TableCol(PolygonBox):
+    col_id: int
+
+    @property
+    def label(self) -> str:
+        return f"Column {self.col_id}"
+
+
+class TableCell(PolygonBox):
+    """Geometric cell derived from row × column intersection.
+
+    The simple-path TableRecPredictor doesn't return spanning info from the
+    model — colspan/rowspan/header come from the full-path HTML output if
+    needed."""
+
+    row_id: int
+    col_id: int
+    cell_id: int
+
+    @property
+    def label(self) -> str:
+        return f"Cell {self.cell_id}"
+
+
+class TableResult(BaseModel):
+    rows: List[TableRow]
+    cols: List[TableCol]
+    cells: List[TableCell]
+    image_bbox: List[float]
+    raw: Optional[str] = None  # raw model output
+    html: Optional[str] = None  # populated when full-path was used
+    mode: str = "simple"  # "simple" | "full"
+    error: bool = False
diff --git a/tests/assets/test_latex.png b/tests/assets/test_latex.png
new file mode 100644
index 0000000..c095eb3
Binary files /dev/null and b/tests/assets/test_latex.png differ
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..b727a00
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,69 @@
+import os
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
+import pytest
+from PIL import Image, ImageDraw
+
+from surya.detection import DetectionPredictor
+from surya.inference import SuryaInferenceManager
+from surya.layout import LayoutPredictor
+from surya.ocr_error import OCRErrorPredictor
+from surya.recognition import RecognitionPredictor
+from surya.table_rec import TableRecPredictor
+
+
+@pytest.fixture(scope="session")
+def manager() -> SuryaInferenceManager:
+    """Eagerly start the VLM backend. If the runner has neither vllm nor
+    llama-server available (e.g. GitHub Actions ubuntu / windows runners),
+    skip every VLM-dependent test in this session instead of failing them."""
+    m = SuryaInferenceManager(lazy=True)
+    try:
+        m.start()
+    except Exception as exc:  # SpawnError, binary missing, port issues, etc.
+        pytest.skip(f"VLM backend unavailable in this environment: {exc}")
+    yield m
+    try:
+        m.stop()
+    except Exception:
+        pass
+
+
+@pytest.fixture(scope="session")
+def layout_predictor(manager) -> LayoutPredictor:
+    return LayoutPredictor(manager)
+
+
+@pytest.fixture(scope="session")
+def recognition_predictor(manager) -> RecognitionPredictor:
+    return RecognitionPredictor(manager)
+
+
+@pytest.fixture(scope="session")
+def table_rec_predictor(manager) -> TableRecPredictor:
+    return TableRecPredictor(manager)
+
+
+@pytest.fixture(scope="session")
+def detection_predictor() -> DetectionPredictor:
+    return DetectionPredictor()
+
+
+@pytest.fixture(scope="session")
+def ocr_error_predictor() -> OCRErrorPredictor:
+    return OCRErrorPredictor()
+
+
+@pytest.fixture()
+def test_image():
+    image = Image.new("RGB", (1024, 1024), "white")
+    draw = ImageDraw.Draw(image)
+    draw.text((10, 10), "Hello World", fill="black", font_size=72)
+    draw.text(
+        (10, 200),
+        "This is a sentence of text.\nNow it is a paragraph.\nA three-line one.",
+        fill="black",
+        font_size=24,
+    )
+    return image
diff --git a/tests/test_detection.py b/tests/test_detection.py
new file mode 100644
index 0000000..b836586
--- /dev/null
+++ b/tests/test_detection.py
@@ -0,0 +1,8 @@
+def test_detection(detection_predictor, test_image):
+    detection_results = detection_predictor([test_image])
+
+    assert len(detection_results) == 1
+    assert detection_results[0].image_bbox == [0, 0, 1024, 1024]
+
+    bboxes = detection_results[0].bboxes
+    assert len(bboxes) == 4
diff --git a/tests/test_layout.py b/tests/test_layout.py
new file mode 100644
index 0000000..0bca086
--- /dev/null
+++ b/tests/test_layout.py
@@ -0,0 +1,13 @@
+def test_layout_returns_blocks(layout_predictor, test_image):
+    layout_results = layout_predictor([test_image])
+    assert len(layout_results) == 1
+    res = layout_results[0]
+    assert res.image_bbox == [0, 0, 1024, 1024]
+    if res.error:
+        # Server may not be running in CI environments without llama-server
+        return
+    assert isinstance(res.bboxes, list)
+    for box in res.bboxes:
+        assert box.label
+        assert box.count >= 0
+        assert isinstance(box.position, int)
diff --git a/tests/test_ocr_errors.py b/tests/test_ocr_errors.py
new file mode 100644
index 0000000..01c9e13
--- /dev/null
+++ b/tests/test_ocr_errors.py
@@ -0,0 +1,15 @@
+def test_garbled_text(ocr_error_predictor):
+    text = """"
+    ; dh vksj ls mifLFkr vf/koDrk % Jh vfuy dqekj
+    2. vfHk;qDr dh vksj ls mifLFkr vf/koDrk % Jh iznhi d
+    """.strip()
+    results = ocr_error_predictor([text])
+    assert results.labels[0] == "bad"
+
+
+def test_good_text(ocr_error_predictor):
+    text = """"
+    There are professions more harmful than industrial design, but only a very few of them.
+    """.strip()
+    results = ocr_error_predictor([text])
+    assert results.labels[0] == "good"
\ No newline at end of file
diff --git a/tests/test_recognition.py b/tests/test_recognition.py
new file mode 100644
index 0000000..fc65733
--- /dev/null
+++ b/tests/test_recognition.py
@@ -0,0 +1,16 @@
+def test_recognition(recognition_predictor, layout_predictor, test_image):
+    layouts = layout_predictor([test_image])
+    if layouts[0].error:
+        # Server unavailable in test env — skip silently
+        return
+    page_results = recognition_predictor([test_image], layouts)
+
+    assert len(page_results) == 1
+    assert page_results[0].image_bbox == [0, 0, 1024, 1024]
+
+    blocks = page_results[0].blocks
+    # Each layout box should produce one block (skipped or otherwise)
+    assert len(blocks) == len(layouts[0].bboxes)
+    for blk in blocks:
+        assert blk.reading_order >= 0
+        assert isinstance(blk.html, str)
diff --git a/tests/test_table_rec.py b/tests/test_table_rec.py
new file mode 100644
index 0000000..21ee6ef
--- /dev/null
+++ b/tests/test_table_rec.py
@@ -0,0 +1,57 @@
+from PIL import Image, ImageDraw
+
+
+def test_table_rec(table_rec_predictor):
+    data = [
+        ["Name", "Age", "City"],
+        ["Alice", 25, "New York"],
+        ["Bob", 30, "Los Angeles"],
+        ["Charlie", 35, "Chicago"],
+    ]
+    test_image = draw_table(data)
+
+    results = table_rec_predictor([test_image])
+    assert len(results) == 1
+    assert results[0].image_bbox == [0, 0, test_image.size[0], test_image.size[1]]
+    if results[0].error:
+        return
+
+    rows = results[0].rows
+    cols = results[0].cols
+    cells = results[0].cells
+    assert len(rows) >= 1
+    assert len(cols) >= 1
+    # Geometric cells = rows × cols
+    assert len(cells) == len(rows) * len(cols) or len(cells) <= len(rows) * len(cols)
+
+
+def draw_table(data, cell_width=100, cell_height=40):
+    rows = len(data)
+    cols = len(data[0])
+    width = cols * cell_width
+    height = rows * cell_height
+
+    image = Image.new("RGB", (width, height), "white")
+    draw = ImageDraw.Draw(image)
+
+    for i in range(rows + 1):
+        y = i * cell_height
+        draw.line([(0, y), (width, y)], fill="black", width=1)
+
+    for i in range(cols + 1):
+        x = i * cell_width
+        draw.line([(x, 0), (x, height)], fill="black", width=1)
+
+    for i in range(rows):
+        for j in range(cols):
+            text = str(data[i][j])
+            text_bbox = draw.textbbox((0, 0), text)
+            text_width = text_bbox[2] - text_bbox[0]
+            text_height = text_bbox[3] - text_bbox[1]
+
+            x = j * cell_width + (cell_width - text_width) // 2
+            y = i * cell_height + (cell_height - text_height) // 2
+
+            draw.text((x, y), text, fill="black")
+
+    return image
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..31faccb
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,4925 @@
+version = 1
+revision = 1
+requires-python = ">=3.10, <4"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'darwin'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "(python_full_version >= '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "(python_full_version >= '3.12' and python_full_version < '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "async-timeout", marker = "python_full_version < '3.11'" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
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