clip-ViT-B-32
This is the Image & Text model CLIP, 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
Usage
After installing sentence-transformers (pip install sentence-transformers), the usage of this model is easy:
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 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 | 63.3 |
| clip-ViT-B-16 | 68.1 |
| 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
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