forked from ailab/clip-ViT-L-14
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}
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{
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"crop_size": 224,
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"do_center_crop": true,
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "CLIPFeatureExtractor",
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"image_mean": [
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],
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],
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"processor_class": "CLIPProcessor",
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"size": 224
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}
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{"bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": "<|endoftext|>"}
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{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": "<|endoftext|>", "add_prefix_space": false, "errors": "replace", "do_lower_case": true, "name_or_path": "openai/clip-vit-large-patch14", "model_max_length": 77, "special_tokens_map_file": "./special_tokens_map.json", "tokenizer_class": "CLIPTokenizer", "processor_class": "CLIPProcessor"}
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---
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library_name: sentence-transformers
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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pipeline_tag: sentence-similarity
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---
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# clip-ViT-L-14
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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)
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## Usage
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After installing [sentence-transformers](https://sbert.net) (`pip install sentence-transformers`), the usage of this model is easy:
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```python
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from sentence_transformers import SentenceTransformer, util
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from PIL import Image
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#Load CLIP model
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model = SentenceTransformer('clip-ViT-L-14')
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#Encode an image:
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img_emb = model.encode(Image.open('two_dogs_in_snow.jpg'))
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#Encode text descriptions
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text_emb = model.encode(['Two dogs in the snow', 'A cat on a table', 'A picture of London at night'])
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#Compute cosine similarities
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cos_scores = util.cos_sim(img_emb, text_emb)
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print(cos_scores)
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```
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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.
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## Performance
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In the following table we find the zero-shot ImageNet validation set accuracy:
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| Model | Top 1 Performance |
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| --- | :---: |
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| [clip-ViT-B-32](https://huggingface.co/sentence-transformers/clip-ViT-B-32) | 63.3 |
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| [clip-ViT-B-16](https://huggingface.co/sentence-transformers/clip-ViT-B-16) | 68.1 |
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| [clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) | 75.4 |
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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)
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{
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"__version__": {
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"sentence_transformers": "2.2.0",
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"transformers": "4.18.0",
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"pytorch": "1.11.0"
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}
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}
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[
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{
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"idx": 0,
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"name": "0",
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"path": "0_CLIPModel",
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"type": "sentence_transformers.models.CLIPModel"
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}
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]
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