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
Mingliang Liang commited on
Rename DataComp_dinov2_sampling_50k_merger_0.70_probabilities_alpha_0.2_t_0.5_sha256.json to datacomp_dfn_sampling_probs_sha256.json
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DataComp_dinov2_sampling_50k_merger_0.70_probabilities_alpha_0.2_t_0.5_sha256.json filter=lfs diff=lfs merge=lfs -text
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DataComp_dinov2_sampling_50k_merger_0.70_probabilities_alpha_0.2_t_0.5_sha256.json filter=lfs diff=lfs merge=lfs -text
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datacomp_dfn_sampling_probs_sha256.json filter=lfs diff=lfs merge=lfs -text
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DataComp_dinov2_sampling_50k_merger_0.70_probabilities_alpha_0.2_t_0.5_sha256.json → datacomp_dfn_sampling_probs_sha256.json
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