Sync with C-RADIOv2-VLM-H-RC3 (#2)
Browse files- Sync with nvidia/C-RADIOv2-VLM-H-RC3 (a1fc2a754c7ce39bee9f0f1ebdf1ea32dc0f5ff8)
- Update README for release (581f0a8268fb26143fe249849e3f61b53c33a695)
- Remove custom load_state_dict (fe079c89a3bc7b68fb3ede1163d3c90c8679fef9)
- README.md +4 -2
- config.json +12 -2
- model.safetensors +1 -1
README.md
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@@ -6,6 +6,8 @@ license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-l
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# Model Overview
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## Description
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This model performs visual feature extraction.
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from PIL import Image
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from transformers import AutoModel, CLIPImageProcessor
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hf_repo = "nvidia/C-RADIOv2-H"
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image_processor = CLIPImageProcessor.from_pretrained(hf_repo)
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model = AutoModel.from_pretrained(hf_repo, trust_remote_code=True)
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Model Application(s): | Generation of visual embeddings
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Describe the life critical impact (if present). | Not Applicable
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Use Case Restrictions: | Abide by NVIDIA Open Model License Agreement
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Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.
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# Model Overview
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[[**Github**](https://github.com/NVlabs/RADIO)] [[**CVPR 2025**](https://arxiv.org/abs/2412.07679)] [[**CVPR 2024**](https://arxiv.org/abs/2312.06709)]
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## Description
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This model performs visual feature extraction.
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from PIL import Image
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from transformers import AutoModel, CLIPImageProcessor
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hf_repo = "nvidia/C-RADIOv2-VLM-H"
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image_processor = CLIPImageProcessor.from_pretrained(hf_repo)
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model = AutoModel.from_pretrained(hf_repo, trust_remote_code=True)
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Model Application(s): | Generation of visual embeddings
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Describe the life critical impact (if present). | Not Applicable
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Use Case Restrictions: | Abide by NVIDIA Open Model License Agreement
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Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.
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config.json
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@@ -16,7 +16,7 @@
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"cache_dir": null,
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"channels_last": false,
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"checkpoint_hist": 10,
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"chk_keep_forever":
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"class_map": "",
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"clip_grad": null,
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"clip_mode": "norm",
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"crop_pct": null,
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"cutmix": 0.0,
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"cutmix_minmax": null,
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"dataset_download": false,
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"debug_full_knn": false,
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"decay_epochs": 90,
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"force_new_wandb_id": false,
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"force_spectral_reparam": true,
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"freeze_bn": false,
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"fsdp":
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"full_equivariance": false,
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"fuser": "",
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"gp": null,
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"name": "siglip2-g",
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"type": "siglip2",
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"use_summary": true
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}
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],
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"torchcompile": null,
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"cache_dir": null,
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"channels_last": false,
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"checkpoint_hist": 10,
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"chk_keep_forever": 100,
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"class_map": "",
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"clip_grad": null,
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"clip_mode": "norm",
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"crop_pct": null,
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"cutmix": 0.0,
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"cutmix_minmax": null,
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"damp": null,
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"dataset_download": false,
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"debug_full_knn": false,
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"decay_epochs": 90,
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"force_new_wandb_id": false,
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"force_spectral_reparam": true,
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"freeze_bn": false,
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"fsdp": false,
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"full_equivariance": false,
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"fuser": "",
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"gp": null,
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"name": "siglip2-g",
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"type": "siglip2",
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"use_summary": true
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},
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{
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"fd_normalize": false,
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"feature_distillation": true,
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"input_size": 384,
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"model": "siglip2-g-384",
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"name": "siglip2-g-dirty",
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"type": "siglip2",
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"use_summary": false
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}
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],
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"torchcompile": null,
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model.safetensors
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@@ -1,3 +1,3 @@
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