All You Need to Know About Training Image Retrieval Models
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910878749687808 |
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| author | Berton, Gabriele Musgrave, Kevin Masone, Carlo |
| author_facet | Berton, Gabriele Musgrave, Kevin Masone, Carlo |
| contents | Image retrieval is the task of finding images in a database that are most similar to a given query image. The performance of an image retrieval pipeline depends on many training-time factors, including the embedding model architecture, loss function, data sampler, mining function, learning rate(s), and batch size. In this work, we run tens of thousands of training runs to understand the effect each of these factors has on retrieval accuracy. We also discover best practices that hold across multiple datasets. The code is available at https://github.com/gmberton/image-retrieval |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13045 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | All You Need to Know About Training Image Retrieval Models Berton, Gabriele Musgrave, Kevin Masone, Carlo Computer Vision and Pattern Recognition Image retrieval is the task of finding images in a database that are most similar to a given query image. The performance of an image retrieval pipeline depends on many training-time factors, including the embedding model architecture, loss function, data sampler, mining function, learning rate(s), and batch size. In this work, we run tens of thousands of training runs to understand the effect each of these factors has on retrieval accuracy. We also discover best practices that hold across multiple datasets. The code is available at https://github.com/gmberton/image-retrieval |
| title | All You Need to Know About Training Image Retrieval Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.13045 |