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LEAF-CLIP
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OpenCLIP-ViT-H-rho50-k1-constrained-FARE2

Feature Extraction
Transformers
Safetensors
clip
zero-shot-image-classification
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megaelius nielsr HF Staff commited on
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Add pipeline tag and library name (#1)

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- Add pipeline tag and library name (5740e051d0711eb1a5d1cc31f4e4471a065eae3e)


Co-authored-by: Niels Rogge <[email protected]>

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  1. README.md +6 -3
README.md CHANGED
@@ -1,11 +1,14 @@
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  ---
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- license: mit
 
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  datasets:
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  - ILSVRC/imagenet-1k
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  - mlfoundations/datacomp_small
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- base_model:
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- - laion/CLIP-ViT-H-14-laion2B-s32B-b79K
 
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  ---
 
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  [[Paper]](https://www.arxiv.org/abs/2506.03355) &nbsp; [[Code]](https://github.com/LIONS-EPFL/LEAF)
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  Model Initialized from `laion/CLIP-ViT-H-14-laion2B-s32B-b79K`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$ and semantic constraints.
 
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  ---
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+ base_model:
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+ - laion/CLIP-ViT-H-14-laion2B-s32B-b79K
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  datasets:
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  - ILSVRC/imagenet-1k
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  - mlfoundations/datacomp_small
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+ license: mit
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+ pipeline_tag: feature-extraction
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+ library_name: transformers
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  ---
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+
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  [[Paper]](https://www.arxiv.org/abs/2506.03355) &nbsp; [[Code]](https://github.com/LIONS-EPFL/LEAF)
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  Model Initialized from `laion/CLIP-ViT-H-14-laion2B-s32B-b79K`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$ and semantic constraints.