Zero-Shot Image Classification
OpenCLIP
clip
vision-language-model
image-text-retrieval
research
long-tail
datacomp
Instructions to use MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN') tokenizer = open_clip.get_tokenizer('hf-hub:MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN') - Notebooks
- Google Colab
- Kaggle
Download DynamiCS-ViT-B-16-DataComp-DFN-130M-1.28B.pt from MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN: direct link, hf CLI and curl.
- Browser
- Download file 1.8 GB
-
https://huggingface.co/MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN/resolve/main/DynamiCS-ViT-B-16-DataComp-DFN-130M-1.28B.pt
- Command line
-
hf download hf://MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN/DynamiCS-ViT-B-16-DataComp-DFN-130M-1.28B.pt
-
curl -L -o DynamiCS-ViT-B-16-DataComp-DFN-130M-1.28B.pt https://huggingface.co/MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN/resolve/main/DynamiCS-ViT-B-16-DataComp-DFN-130M-1.28B.pt
1.8 GB
- Xet hash:
- 718304b8e418f4f0756345503c7f76fd75af2ce719e36a48fe1a33b6e853085f
- Size of remote file:
- 1.8 GB
- SHA256:
- abf6a49b669098255a0f59845be0030a1ed95cbd33648de3d593c51016df70c1
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