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75 lines
2.7 KiB
Markdown
75 lines
2.7 KiB
Markdown
<!--Copyright 2026 The HuggingFace Team. All rights reserved.
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Copyright (c) 2026, NVIDIA CORPORATION. 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 contributed to Hugging Face Transformers on 2026-06-30.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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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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</div>
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# RADIO
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[RADIO](https://huggingface.co/papers/2312.06709) (Reduce All Domains Into One) is a family of vision foundation models from NVIDIA trained by multi-teacher distillation (e.g. CLIP, DINOv2, SAM) into a single ViT backbone. It produces both an image-level `summary` embedding and dense spatial `features`, and supports variable input resolutions through a Cropped Position Embedding (CPE) patch generator.
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The example below demonstrates how to extract image features with the [`RadioModel`] class.
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<hfoptions id="usage">
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<hfoption id="RadioModel">
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```python
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import requests
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import torch
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from PIL import Image
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from transformers import CLIPImageProcessor, RadioModel
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hf_repo = "nvidia/C-RADIOv4-H"
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model = RadioModel.from_pretrained(hf_repo)
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model.eval().cuda()
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image_processor = CLIPImageProcessor(
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size={"height": 224, "width": 224}, do_resize=True, do_center_crop=False, do_normalize=False
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)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
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pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
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pixel_values = pixel_values.cuda()
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with torch.no_grad():
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outputs = model(pixel_values)
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summary = outputs.summary # (1, 2560) image-level embedding
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features = outputs.features # (1, 196, 1280) dense spatial features
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```
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</hfoption>
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</hfoptions>
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## RadioConfig
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[[autodoc]] RadioConfig
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## RadioModel
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[[autodoc]] RadioModel
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- forward
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