Marqo
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marqo-ecommerce-embeddings-L

multimodal retrieval

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elliesleightholm commited on Nov 8, 2024

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@@ -16,7 +16,7 @@ In this work, we introduce two state-of-the-art embedding models for ecommerce p
 The benchmarking results highlight a remarkable performance by marqo-ecommerce models, which both consistently outperformed all other models across various metrics. Specifically, for the Google Shopping Text-to-Image task, marqo-ecommerce-L achieved an improvement of 43% in MRR, 41% in nDCG@10 and 33% in Recall@10 when compared to ViT-B-16-SigLIP which is our baseline model for these benchmarks. For the Google Shopping Category-to-Image task, we saw an improvement of 67% in mAP, 41% in nDCG@10 and 42% in Precision@10.
-<img src="https://raw.githubusercontent.com/marqo-ai/marqo-ecommerce-embeddings/refs/heads/main/performance.png?token=GHSAT0AAAAAACZY3OVLSH7USBXOC3SYCQ3OZZL6LVQ" alt="multi split visual" width="700"/>
 More benchmarking results can be found below.

 The benchmarking results highlight a remarkable performance by marqo-ecommerce models, which both consistently outperformed all other models across various metrics. Specifically, for the Google Shopping Text-to-Image task, marqo-ecommerce-L achieved an improvement of 43% in MRR, 41% in nDCG@10 and 33% in Recall@10 when compared to ViT-B-16-SigLIP which is our baseline model for these benchmarks. For the Google Shopping Category-to-Image task, we saw an improvement of 67% in mAP, 41% in nDCG@10 and 42% in Precision@10.
+<img src="https://raw.githubusercontent.com/marqo-ai/marqo-ecommerce-embeddings/main/performance.png?token=GHSAT0AAAAAACZY3OVL7HD6UZTBOJ7FLG7MZZOCJSA" alt="multi split visual" width="700"/>
 More benchmarking results can be found below.