Update README.md

README.md

@@ -1,3 +1,37 @@
 ---
 license: cc-by-nc-4.0
 ---
+This is the official Hugging Face repo for PathCLIP
+
+# Usage
+
+```python
+import torch
+from PIL import Image
+
+import open_clip
+
+##load the model
+model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', pretrained='/mnt/Xsky/syx/model/clip/exp/ViT-B-16/23_1227_openpath/epoch_10_ori.pt',
+                                                       cache_dir='/mnt/Xsky/syx/model/open_clip', force_quick_gelu=True)
+tokenizer = open_clip.get_tokenizer('ViT-B-16')
+model = model.cuda()
+
+##load the image and prepare the text prompt
+img_path = 'your_img_path'
+label_description_list = ['label description1', 'label description3', 'label description3'] # specify the label descriptions
+text_label_list = ['An image of {}'.format(i) for i in label_description_list]
+image = Image.open(img_path)
+image = preprocess(image).unsqueeze(0).cuda()
+text = tokenizer(text_label_list).cuda()
+
+##extract the img and text feature and predict the label
+with torch.no_grad(), torch.cuda.amp.autocast():
+    image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+    predict_label = torch.argmax(text_probs).item()
+```
+