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83 lines
4.3 KiB
Markdown
83 lines
4.3 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2024-07-24 and contributed to Hugging Face Transformers on 2025-04-15.*
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# MLCD
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<div class="flex flex-wrap space-x-1">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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The [MLCD](https://huggingface.co/papers/2407.17331) models were released by the DeepGlint-AI team in [unicom](https://github.com/deepglint/unicom), which focuses on building foundational visual models for large multimodal language models using large-scale datasets such as LAION400M and COYO700M, and employs sample-to-cluster contrastive learning to optimize performance. MLCD models are primarily used for multimodal visual large language models, such as LLaVA.
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🔥**MLCD-ViT-bigG**🔥 series is the state-of-the-art vision transformer model enhanced with 2D Rotary Position Embedding (RoPE2D), achieving superior performance on document understanding and visual question answering tasks. Developed by DeepGlint AI, this model demonstrates exceptional capabilities in processing complex visual-language interactions.
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Tips:
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- We adopted the official [LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT) and the official training dataset [LLaVA-NeXT-Data](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) for evaluating the foundational visual models.
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- The language model is [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
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Result:
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| Vision Tower | RoPE2D | ChartQA | DocVQA | InfoVQA | OCRBench | MMMU |
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| :-------------------------------------------------------------------------------------------- | :----: | :-------- | :-------- | :-------- | :--------- | :-------- |
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| CLIP (ViT-L-14-336px) | × | 66.52 | 75.21 | 38.88 | 525.00 | 44.20 |
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| SigLIP (ViT-SO400M-384px) | × | 69.28 | 76.71 | 41.38 | 554.00 | 46.78 |
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| DFN5B (ViT-H-14-378px) | × | 64.36 | 70.87 | 38.59 | 473.00 | **48.00** |
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| **[MLCD (ViT-L-14-336px)](https://huggingface.co/DeepGlint-AI/mlcd-vit-large-patch14-336)** | × | 67.84 | 76.46 | 43.48 | 531.00 | 44.30 |
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| **[MLCD (ViT-bigG-14-336px)](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-336)** | √ | 71.07 | 79.63 | 44.38 | 572.00 | 46.78 |
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| **[MLCD (ViT-bigG-14-448px)](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-448)** | √ | **73.80** | **83.34** | **46.59** | **582.00** | 46.00 |
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## Usage
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```python
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import requests
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from PIL import Image
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from transformers import AutoProcessor, MLCDVisionModel
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# Load model and processor
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model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448", device_map="auto")
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processor = AutoProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
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# Process single image
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(images=image, return_tensors="pt").to(model.device)
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# Generate outputs
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with torch.no_grad():
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outputs = model(**inputs)
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# Get visual features
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features = outputs.last_hidden_state
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print(f"Extracted features shape: {features.shape}")
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```
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## MLCDVisionConfig
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[[autodoc]] MLCDVisionConfig
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## MLCDVisionModel
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[[autodoc]] MLCDVisionModel
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- forward
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