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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: gender |
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dtype: string |
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- name: category |
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dtype: string |
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- name: sub-category |
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dtype: string |
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- name: text |
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dtype: string |
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- name: item_ID |
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dtype: string |
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splits: |
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- name: data |
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num_bytes: 4175875723.02 |
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num_examples: 78370 |
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download_size: 2702808196 |
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dataset_size: 4175875723.02 |
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configs: |
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- config_name: default |
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data_files: |
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- split: data |
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path: data/data-* |
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license: mit |
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--- |
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**Disclaimer**: We do not own this dataset. Atlas is a public dataset which can be downloaded from its [Github Page](https://github.com/vumaasha/Atlas/tree/master). |
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This dataset was used to evaluate Marqo-FashionCLIP and Marqo-FashionSigLIP - see details below. |
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# Marqo-FashionSigLIP Model Card |
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Marqo-FashionSigLIP leverages Generalised Contrastive Learning ([GCL](https://www.marqo.ai/blog/generalized-contrastive-learning-for-multi-modal-retrieval-and-ranking)) which allows the model to be trained on not just text descriptions but also categories, style, colors, materials, keywords and fine-details to provide highly relevant search results on fashion products. |
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The model was fine-tuned from ViT-B-16-SigLIP (webli). |
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**Github Page**: [Marqo-FashionCLIP](https://github.com/marqo-ai/marqo-FashionCLIP) |
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**Blog**: [Marqo Blog](https://www.marqo.ai/blog/search-model-for-fashion) |
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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 |
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import open_clip |
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model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP') |
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tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP') |
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import torch |
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from PIL import Image |
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image = preprocess_val(Image.open("docs/fashion-hippo.png")).unsqueeze(0) |
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text = tokenizer(["a hat", "a t-shirt", "shoes"]) |
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with torch.no_grad(), torch.cuda.amp.autocast(): |
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image_features = model.encode_image(image) |
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text_features = model.encode_text(text) |
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image_features /= image_features.norm(dim=-1, keepdim=True) |
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text_features /= text_features.norm(dim=-1, keepdim=True) |
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text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) |
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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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**Text-To-Image (Averaged across 6 datasets)** |
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| Model | AvgRecall | Recall@1 | Recall@10 | MRR | |
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|----------------------------|-------------|------------|-------------|-----------| |
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| Marqo-FashionSigLIP | **0.231** | **0.121** | **0.340** | **0.239** | |
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| FashionCLIP2.0 | 0.163 | 0.077 | 0.249 | 0.165 | |
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| OpenFashionCLIP | 0.132 | 0.060 | 0.204 | 0.135 | |
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| ViT-B-16-laion2b_s34b_b88k | 0.174 | 0.088 | 0.261 | 0.180 | |
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| ViT-B-16-SigLIP-webli | 0.212 | 0.111 | 0.314 | 0.214 | |
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**Category-To-Product (Averaged across 5 datasets)** |
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| Model | AvgP | P@1 | P@10 | MRR | |
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|----------------------------|-----------|-----------|-----------|-----------| |
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| Marqo-FashionSigLIP | **0.737** | **0.758** | **0.716** | **0.812** | |
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| FashionCLIP2.0 | 0.684 | 0.681 | 0.686 | 0.741 | |
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| OpenFashionCLIP | 0.646 | 0.653 | 0.639 | 0.720 | |
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| ViT-B-16-laion2b_s34b_b88k | 0.662 | 0.673 | 0.652 | 0.743 | |
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| ViT-B-16-SigLIP-webli | 0.688 | 0.690 | 0.685 | 0.751 | |
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**Sub-Category-To-Product (Averaged across 4 datasets)** |
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| Model | AvgP | P@1 | P@10 | MRR | |
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|----------------------------|-----------|-----------|-----------|-----------| |
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| Marqo-FashionSigLIP | **0.725** | **0.767** | **0.683** | **0.811** | |
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| FashionCLIP2.0 | 0.657 | 0.676 | 0.638 | 0.733 | |
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| OpenFashionCLIP | 0.598 | 0.619 | 0.578 | 0.689 | |
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| ViT-B-16-laion2b_s34b_b88k | 0.638 | 0.651 | 0.624 | 0.712 | |
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| ViT-B-16-SigLIP-webli | 0.643 | 0.643 | 0.643 | 0.726 | |
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