Update README.md

README.md

@@ -29,20 +29,26 @@ import torch.nn.functional as F
 from urllib.request import urlopen
 from PIL import Image
 from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
+
 model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-14-SigLIP-384')
 tokenizer = get_tokenizer('hf-hub:timm/ViT-SO400M-14-SigLIP-384')
+
 image = Image.open(urlopen(
     'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
 ))
 image = preprocess(image).unsqueeze(0)
+
 labels_list = ["a dog", "a cat", "a donut", "a beignet"]
 text = tokenizer(labels_list, context_length=model.context_length)
+
 with torch.no_grad(), torch.cuda.amp.autocast():
     image_features = model.encode_image(image)
     text_features = model.encode_text(text)
     image_features = F.normalize(image_features, dim=-1)
     text_features = F.normalize(text_features, dim=-1)
+
     text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
+
 zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
 print("Label probabilities: ", zipped_list)
 ```