Update README.md

README.md

@@ -7,20 +7,74 @@ sdk: static
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-## Robustness in Both Domains: CLIP Needs a Robust Text Encoder
+# Robustness in Both Domains: CLIP Needs a Robust Text Encoder
 
 <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>
 
 <span style="color:rgb(255, 0, 0);">LIONS @ EPFL</span> and <span style="color:rgb(133 203 210);">Tübingen AI Center</span>
 
 
-In this repo, you will find all the models trained for our paper. 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 with:
+In this repo, you will find all the models trained for our paper. 
+
+### Loading CLIPModels
+
+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:
 
 ```python
 
+from PIL import Image
+import requests
+
+from transformers import CLIPProcessor, CLIPModel
+
+model_name = "LEAF-CLIP/CLIP-ViT-L-rho50-k1-constrained-FARE2"
+processor_name = "openai/clip-vit-large-patch14"
+
+model = CLIPModel.from_pretrained(model_name)
+processor = CLIPProcessor.from_pretrained(processor_name)
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
+
+outputs = model(**inputs)
+logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
 
 
 ```
 
+When loading other model sizes, the `processor_name` needs to be changed accordingly as:
+| Model Size | Processor Name |
+|     -      |       -        |
+| ViT-L-14   |`"openai/clip-vit-large-patch14"`|
+| ViT-H-14   |`"laion/CLIP-ViT-H-14-laion2B-s32B-b79K"`|
+| ViT-g-14   |`"laion/CLIP-ViT-g-14-laion2B-s12B-b42K"`|
+| ViT-bigG-14   |`"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"`|
+
+### Loading CLIPTextModels
+
+If just need the text encoder, you can load it with the following snippet:
+
+```python
+from transformers import CLIPTokenizer, CLIPTextModel
+
+model_name = "LEAF-CLIP/CLIP-ViT-L-rho50-k1-constrained-FARE2"
+processor_name = "openai/clip-vit-large-patch14"
+
+model = CLIPTextModel.from_pretrained(model_name)
+tokenizer = CLIPTokenizer.from_pretrained(processor_name)
+
+inputs = tokenizer(["a photo of a cat", "a photo of a dog"],  padding=True, return_tensors="pt")
+
+outputs = model(**inputs)
+last_hidden_state = outputs.last_hidden_state
+pooled_output = outputs.pooled_output # pooled (EOS token) states
+```
+
+### Acknowledgements
+
+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.