In this work, we introduce two state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. The benchmarking results show that the Marqo-Ecommerce models consistently outperformed *all other models* across various metrics. Specifically, `marqo-ecommerce-L` achieved an average improvement of **17.6% in MRR** and **20.5% in nDCG@10** when compared with the current best open source model, `ViT-SO400M-14-SigLIP` across all three tasks in the `marqo-ecommerce-hard` dataset. When compared with the best private model, `Amazon-Titan-Multimodal`, we saw an average improvement of **38.9% in MRR** and **45.1% in nDCG@10** across all three tasks, and **35.9% in Recall** across the Text-to-Image tasks in the `marqo-ecommerce-hard` dataset. **Released Content**: 1) Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models 2) GoogleShopping-1m and AmazonProducts-3m for evaluation 3) Evaluation Code
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In this work, we introduce two state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. The benchmarking results show that the Marqo-Ecommerce models consistently outperformed *all other models* across various metrics. Specifically, `marqo-ecommerce-L` achieved an average improvement of **17.6% in MRR** and **20.5% in nDCG@10** when compared with the current best open source model, `ViT-SO400M-14-SigLIP` across all three tasks in the `marqo-ecommerce-hard` dataset. When compared with the best private model, `Amazon-Titan-Multimodal`, we saw an average improvement of **38.9% in MRR** and **45.1% in nDCG@10** across all three tasks, and **35.9% in Recall** across the Text-to-Image tasks in the `marqo-ecommerce-hard` dataset. **Released Content**: 1) Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models 2) GoogleShopping-1m and AmazonProducts-3m for evaluation 3) Evaluation Code
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Marqo-FashionCLIP and Marqo-FashionSigLIP outperform the previous state-of-the-art fashion CLIP models (see results below). Marqo-FashionCLIP leverages Generalised Contrastive Learning (GCL) 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. The model was fine-tuned from ViT-B-16 (laion2b_s34b_b88k). **Github Page**: Marqo-FashionCLIP The model can be seamlessly used with OpenCLIP by
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**Marqo Fashion Siglip 2 is available.** marqo-fashion-SigLip-2 has shown a further 78% improvement in MMR and recall vs marqo-fashion-SigLip. Contact Marqo to learn more: https://www.marqo.ai/book-demo Marqo-FashionSigLIP is a multimodal embedding model that provides up to 57% improvement in MRR and recall over fashion clip. Marqo-FashionSigLIP leverages Generalised Contrastive Learning (GCL) 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. The model was fine-tuned from ViT-B-16-SigLIP (webli).
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__NOTE: Like all models, this one can make mistakes. NSFW content can be subjective and contextual, this model is intended to help identify this content, use at your own risk.__ `Marqo/nsfw-image-detection-384` is a lightweight image classification model designed to identify NSFW images. The model is approximately 18–20x smaller than other open-source models and achieves a superior accuracy of 98.56% on our dataset. This model uses 384x384 pixel images for the input with 16x16 pixel patches. This model was trained on a proprietary dataset of 220,000 images. The training set includes 100,000 NSFW examples and 100,000 SFW examples, while the test set contains 10,000 NSFW examples and 10,000 SFW examples. This dataset features a diverse range of content, including: real photos, drawings, Rule 34 material, memes, and AI-generated images. The definition of NSFW can vary and is sometimes contextual, our dataset was constructed to contain challenging examples however this definition may not be 100% aligned with every use case, as such we recommend experimenting and trying different thresholds to determine if this model is suitable for your needs.
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