readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  ---
  
  # clip-ViT-B-32
  
  This is the Image & Text model [CLIP](https://arxiv.org/abs/2103.00020), which maps text and images to a shared vector space. For applications of the models, have a look in our documentation [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html)
  
  ## Usage
  
  After installing [sentence-transformers](https://sbert.net) (`pip install sentence-transformers`), the usage of this model is easy:
  
    
  ```python
  from sentence_transformers import SentenceTransformer, util
  from PIL import Image
  
  #Load CLIP model
  model = SentenceTransformer('clip-ViT-B-32')
  
  #Encode an image:
  img_emb = model.encode(Image.open('two_dogs_in_snow.jpg'))
  
  #Encode text descriptions
  text_emb = model.encode(['Two dogs in the snow', 'A cat on a table', 'A picture of London at night'])
  
  #Compute cosine similarities 
  cos_scores = util.cos_sim(img_emb, text_emb)
  print(cos_scores)
  ```
  
  See our [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html) documentation for more examples how the model can be used for image search, zero-shot image classification, image clustering and image deduplication.
  
  ## Performance
  
  In the following table we find the zero-shot ImageNet validation set accuracy:
  
  | Model | Top 1 Performance |
  | --- | :---: |
  | [clip-ViT-B-32](https://huggingface.co/sentence-transformers/clip-ViT-B-32) | 63.3 |
  | [clip-ViT-B-16](https://huggingface.co/sentence-transformers/clip-ViT-B-16) | 68.1 |
  | [clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) | 75.4 |
  
  For a multilingual version of the CLIP model for 50+ languages have a look at: [clip-ViT-B-32-multilingual-v1](https://huggingface.co/sentence-transformers/clip-ViT-B-32-multilingual-v1)