---
tags:
- vision

library_name: transformers
---


## Model Details

### The CLIP model was pretrained from openai/clip-vit-base-patch32 , to learn about what contributes to robustness in computer vision tasks.
### The model has the ability to generalize to arbitrary image classification tasks in a zero-shot manner.


Top predictions:

           Saree: 64.89%
         Dupatta: 25.81%
         Lehenga: 7.51%
Leggings and Salwar: 0.84%
     Women Kurta: 0.44%

![image/png](https://cdn-uploads.huggingface.co/production/uploads/660bc03b5294ca0aada80fb9/Kl8Yd8fwFLtmeDbBLi4Fz.png)




### Use with Transformers

```python3
from PIL import Image
import requests

from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("samim2024/clip")
processor = CLIPProcessor.from_pretrained("samim2024/clip")

url = "https://www.istockphoto.com/photo/indian-saris-gm93355119-10451468"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(text=["a photo of a saree", "a photo of a blouse"], 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
```