| library_name: sentence-transformers | |
| tags: | |
| - sentence-transformers | |
| - feature-extraction | |
| - sentence-similarity | |
| pipeline_tag: sentence-similarity | |
| # clip-ViT-L-14 | |
| 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-L-14') | |
| #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) | |