博客资源:clip-demo测试效果展示

from PIL import Image import requests from transformers import CLIPProcessor, CLIPModel # 挂不挂梯子都试试，看看怎么能下载 model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") # openai/clip-vit-base-patch32 processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") # url = "http://images.cocodataset.org/val2017/000000039769.jpg" # image = Image.open(requests.get(url, stream=True).raw) image = Image.open('benchi.jpg') #text = ["它属于泰迪", "它属于金毛", '它属于藏獒'] #text = ['golden retriever', 'teddy', 'husky'] text = ['Rolls-Royce', 'Audi', 'Benz'] #text = ['God', 'ghost', 'human'] inputs = processor(text=text, 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 for i in range(len(text)): print(text[i], ':', probs[0][i])