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--- |
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tags: |
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- clip |
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- e-commerce |
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- fashion |
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- multimodal retrieval |
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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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datasets: |
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- Marqo/atlas |
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- Marqo/deepfashion-inshop |
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- Marqo/deepfashion-multimodal |
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- Marqo/fashion200k |
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- Marqo/iMaterialist |
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- Marqo/KAGL |
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- Marqo/polyvore |
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language: |
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- en |
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metrics: |
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- precision |
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- recall |
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- MRR |
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--- |
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# Marqo FashionCLIP Model Card |
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Marqo-FashionCLIP 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 (laion2b_s34b_b88k). |
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**Github Page**: [Marqo-FashionCLIP](https://github.com/marqo-ai/marqo-FashionCLIP) |
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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-fashionCLIP') |
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tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionCLIP') |
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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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| FashionCLIP2.0 | 0.163 | 0.077 | 0.249 | 0.165 | |
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| Marqo-FashionCLIP | **0.192** | **0.094** | **0.290** | **0.200** | |
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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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**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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| FashionCLIP2.0 | 0.684 | 0.681 | **0.686** | 0.741 | |
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| Marqo-FashionCLIP | **0.705** | **0.734** | 0.676 | **0.776** | |
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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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**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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| FashionCLIP2.0 | 0.657 | 0.676 | 0.638 | 0.733 | |
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| Marqo-FashionCLIP | **0.707** | **0.747** | **0.667** | **0.772** | |
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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 | |
