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--- |
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title: README |
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emoji: 📚 |
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--- |
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# Robustness in Both Domains: CLIP Needs a Robust Text Encoder |
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<span style="color:rgb(255, 0, 0);">Elias Abad Rocamora</span>, <span style="color:rgb(133 203 210);">Christian Schlarmann</span>, <span style="color:rgb(133 203 210);">Naman Deep Singh</span>, <span style="color:rgb(255, 0, 0);">Yongtao Wu</span>, <span style="color:rgb(133 203 210);">Matthias Hein</span> and <span style="color:rgb(255, 0, 0);">Volkan Cevher</span> |
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<span style="color:rgb(255, 0, 0);">LIONS @ EPFL</span> and <span style="color:rgb(133 203 210);">Tübingen AI Center</span> |
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In this repo, you will find all the models trained for our paper. |
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### Loading CLIPModels |
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You can load our models as any other CLIP model, for example, loading `LEAF-CLIP/CLIP-ViT-L-rho50-k1-constrained-FARE2` can be done by following the "openai/clip-vit-large-patch14" example snippet: |
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```python |
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from PIL import Image |
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import requests |
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from transformers import CLIPProcessor, CLIPModel |
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model_name = "LEAF-CLIP/CLIP-ViT-L-rho50-k1-constrained-FARE2" |
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processor_name = "openai/clip-vit-large-patch14" |
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model = CLIPModel.from_pretrained(model_name) |
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processor = CLIPProcessor.from_pretrained(processor_name) |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True) |
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outputs = model(**inputs) |
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logits_per_image = outputs.logits_per_image # this is the image-text similarity score |
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probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities |
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``` |
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When loading other model sizes, the `processor_name` needs to be changed accordingly as: |
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| Model Size | Processor Name | |
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| - | - | |
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| ViT-L-14 |`"openai/clip-vit-large-patch14"`| |
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| ViT-H-14 |`"laion/CLIP-ViT-H-14-laion2B-s32B-b79K"`| |
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| ViT-g-14 |`"laion/CLIP-ViT-g-14-laion2B-s12B-b42K"`| |
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| ViT-bigG-14 |`"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"`| |
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### Loading CLIPTextModels |
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If just need the text encoder, you can load it with the following snippet: |
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```python |
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from transformers import CLIPTokenizer, CLIPTextModel |
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model_name = "LEAF-CLIP/CLIP-ViT-L-rho50-k1-constrained-FARE2" |
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processor_name = "openai/clip-vit-large-patch14" |
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model = CLIPTextModel.from_pretrained(model_name) |
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tokenizer = CLIPTokenizer.from_pretrained(processor_name) |
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inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") |
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outputs = model(**inputs) |
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last_hidden_state = outputs.last_hidden_state |
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pooled_output = outputs.pooled_output # pooled (EOS token) states |
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``` |
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### Acknowledgements |
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Our codebase is based in the [OpenCLIP codebase](https://github.com/mlfoundations/open_clip), we appreciate the effort of the OpenCLIP team and the release of their code and model weights. |
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