# sentence-transformers /clip-ViT-B-32

 --- 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)