---
title: clip-ViT-B-32
canonical_url: "https://www.modelscope.cn/models/sentence-transformers/clip-ViT-B-32"
md_url: "https://www.modelscope.cn/models/sentence-transformers/clip-ViT-B-32.md"
repository: sentence-transformers/clip-ViT-B-32
last_updated: 2025-07-17
pipeline_tag: sentence-similarity
tasks:
  - sentence-similarity
parameters: 151.3M
tensor_type:
  - F32
  - I64
library_name:
  - sentence-transformers
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 5245
stars: 4
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
---

# clip-ViT-B-32

> clip-ViT-B-32 - sentence-transformers 在 ModelScope 开源的模型。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

- **Repository**: sentence-transformers/clip-ViT-B-32
- **Tasks**: sentence-similarity
- **Parameters**: 151.3M
- **Tags**: sentence-transformers, feature-extraction, sentence-similarity
- **Downloads**: 5245
- **Stars**: 4
- **Last updated**: 2025-07-17

Source: https://www.modelscope.cn/models/sentence-transformers/clip-ViT-B-32

---

# 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)