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--- |
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tags: |
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- vision |
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library_name: transformers |
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--- |
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## Model Details |
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### The CLIP model was pretrained from openai/clip-vit-base-patch32 , to learn about what contributes to robustness in computer vision tasks. |
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### The model has the ability to generalize to arbitrary image classification tasks in a zero-shot manner. |
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Top predictions: |
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Saree: 64.89% |
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Dupatta: 25.81% |
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Lehenga: 7.51% |
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Leggings and Salwar: 0.84% |
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Women Kurta: 0.44% |
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### Use with Transformers |
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```python3 |
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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 = CLIPModel.from_pretrained("samim2024/clip") |
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processor = CLIPProcessor.from_pretrained("samim2024/clip") |
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url = "https://www.istockphoto.com/photo/indian-saris-gm93355119-10451468" |
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image = Image.open(requests.get(url, stream=True).raw) |
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inputs = processor(text=["a photo of a saree", "a photo of a blouse"], 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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