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---
base_model:
- laion/CLIP-ViT-H-14-laion2B-s32B-b79K
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
license: mit
pipeline_tag: feature-extraction
library_name: transformers
---
[[Paper]](https://www.arxiv.org/abs/2506.03355) [[Code]](https://github.com/LIONS-EPFL/LEAF)
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.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/OpenCLIP-ViT-H-rho50-k1-constrained-FARE2"
processor_name = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |