elliesleightholm Xenova HF staff commited on
Commit
ef343cf
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Upload ONNX weights (+ quantizations) + Transformers.js support (#1)

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- Upload ONNX weights (+ quantizations) (31a9a46f0afbbbe34a1668adcd72a03ce37d5b9b)
- Create config.json (a05d487c0285b3c35898c17db40f374058b42d81)
- Create preprocessor_config.json (8690aa35ac67dacfbc214cdff245602eaa83b5e2)
- Update tokenizer_config.json (cc93df47ec2c676dc83e0d3e92e3f56f57366066)
- Update README.md (4a45991a7a637ae0cf9ff467bcd6db7e166247d7)


Co-authored-by: Joshua <[email protected]>

README.md CHANGED
@@ -5,6 +5,7 @@ tags:
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  - fashion
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  - multimodal retrieval
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  - siglip
 
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  library_name: open_clip
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  pipeline_tag: zero-shot-image-classification
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  license: apache-2.0
@@ -25,6 +26,9 @@ The model was fine-tuned from ViT-B-16-SigLIP (webli).
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  ## Usage
 
 
 
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  The model can be seamlessly used with [OpenCLIP](https://github.com/mlfoundations/open_clip) by
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  ```python
@@ -49,6 +53,55 @@ with torch.no_grad(), torch.cuda.amp.autocast():
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  print("Label probs:", text_probs)
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  ```
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  ## Benchmark Results
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  Average evaluation results on 6 public multimodal fashion datasets ([Atlas](https://huggingface.co/datasets/Marqo/atlas), [DeepFashion (In-shop)](https://huggingface.co/datasets/Marqo/deepfashion-inshop), [DeepFashion (Multimodal)](https://huggingface.co/datasets/Marqo/deepfashion-multimodal), [Fashion200k](https://huggingface.co/datasets/Marqo/fashion200k), [KAGL](https://huggingface.co/datasets/Marqo/KAGL), and [Polyvore](https://huggingface.co/datasets/Marqo/polyvore)) are reported below:
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  - fashion
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  - multimodal retrieval
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  - siglip
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+ - transformers.js
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  library_name: open_clip
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  pipeline_tag: zero-shot-image-classification
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  license: apache-2.0
 
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  ## Usage
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+
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+ ### OpenCLIP
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+
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  The model can be seamlessly used with [OpenCLIP](https://github.com/mlfoundations/open_clip) by
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  ```python
 
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  print("Label probs:", text_probs)
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  ```
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+ ### Transformers.js
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+
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+ You can also run the model in JavaScript with the [Transformers.js](https://huggingface.co/docs/transformers.js) library.
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+
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+ First, install it from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
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+
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+ ```bash
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+ npm i @huggingface/transformers
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+ ```
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+
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+ Then, compute embeddings as follows:
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+ ```js
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+ import { SiglipTextModel, SiglipVisionModel, AutoTokenizer, AutoProcessor, RawImage, softmax, dot } from '@huggingface/transformers';
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+
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+ const model_id = 'Marqo/marqo-fashionSigLIP';
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+
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+ // Load tokenizer and text model
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+ const tokenizer = await AutoTokenizer.from_pretrained(model_id);
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+ const text_model = await SiglipTextModel.from_pretrained(model_id);
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+
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+ // Load processor and vision model
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+ const processor = await AutoProcessor.from_pretrained(model_id);
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+ const vision_model = await SiglipVisionModel.from_pretrained(model_id);
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+
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+ // Run tokenization
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+ const texts = ['a hat', 'a t-shirt', 'shoes'];
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+ const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true });
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+
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+ // Compute text embeddings
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+ const { text_embeds } = await text_model(text_inputs);
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+
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+ // Read image and run processor
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+ const image = await RawImage.read('https://raw.githubusercontent.com/marqo-ai/marqo-FashionCLIP/main/docs/fashion-hippo.png');
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+ const image_inputs = await processor(image);
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+
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+ // Compute vision embeddings
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+ const { image_embeds } = await vision_model(image_inputs);
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+
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+ // Compute similarity scores
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+ const normalized_text_embeds = text_embeds.normalize().tolist();
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+ const normalized_image_embeds = image_embeds.normalize().tolist()[0];
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+
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+ const text_probs = softmax(normalized_text_embeds.map((text_embed) =>
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+ 100.0 * dot(normalized_image_embeds, text_embed)
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+ ));
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+ console.log(text_probs);
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+ // [0.9860219105287394, 0.00777916527489097, 0.006198924196369721]
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+ ```
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+
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  ## Benchmark Results
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  Average evaluation results on 6 public multimodal fashion datasets ([Atlas](https://huggingface.co/datasets/Marqo/atlas), [DeepFashion (In-shop)](https://huggingface.co/datasets/Marqo/deepfashion-inshop), [DeepFashion (Multimodal)](https://huggingface.co/datasets/Marqo/deepfashion-multimodal), [Fashion200k](https://huggingface.co/datasets/Marqo/fashion200k), [KAGL](https://huggingface.co/datasets/Marqo/KAGL), and [Polyvore](https://huggingface.co/datasets/Marqo/polyvore)) are reported below:
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preprocessor_config.json ADDED
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+ {
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+ "do_normalize": true,
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+ "do_rescale": true,
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+ "do_resize": true,
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+ "image_processor_type": "SiglipImageProcessor",
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+ "image_mean": [
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+ 0.5,
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+ 0.5
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+ ],
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+ "processor_class": "SiglipProcessor",
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+ "resample": 3,
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+ "rescale_factor": 0.00392156862745098,
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+ "size": {
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+ "height": 224,
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+ },
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+ }
tokenizer_config.json CHANGED
@@ -931,7 +931,7 @@
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  "eos_token": "</s>",
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  "extra_ids": 100,
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  "legacy": false,
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  "pad_token": "</s>",
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  "sp_model_kwargs": {},
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  "tokenizer_class": "T5Tokenizer",
 
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  "eos_token": "</s>",
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  "extra_ids": 100,
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  "legacy": false,
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+ "model_max_length": 64,
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  "pad_token": "</s>",
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  "sp_model_kwargs": {},
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  "tokenizer_class": "T5Tokenizer",