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[](https://github.com/marqo-ai/marqo-FashionCLIP)
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Marqo-FashionSigLIP is a multimodal embedding model that provides up to [57% improvement](https://www.marqo.ai/blog/search-model-for-fashion) over [fashion clip](https://huggingface.co/patrickjohncyh/fashion-clip).
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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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[](https://github.com/marqo-ai/marqo-FashionCLIP)
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Marqo-FashionSigLIP is a multimodal embedding model that provides up to [57% improvement in MRR and recall](https://www.marqo.ai/blog/search-model-for-fashion) over [fashion clip](https://huggingface.co/patrickjohncyh/fashion-clip).
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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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