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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.04394 |
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| _version_ | 1866911251625410560 |
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| author | Du, Ke Peng, Yimin Gao, Chao Zhou, Fan Xue, Siqiao |
| author_facet | Du, Ke Peng, Yimin Gao, Chao Zhou, Fan Xue, Siqiao |
| contents | DORAEMON is an open-source PyTorch library that unifies visual object modeling and representation learning across diverse scales. A single YAML-driven workflow covers classification, retrieval and metric learning; more than 1000 pretrained backbones are exposed through a timm-compatible interface, together with modular losses, augmentations and distributed-training utilities. Reproducible recipes match or exceed reference results on ImageNet-1K, MS-Celeb-1M and Stanford online products, while one-command export to ONNX or HuggingFace bridges research and deployment. By consolidating datasets, models, and training techniques into one platform, DORAEMON offers a scalable foundation for rapid experimentation in visual recognition and representation learning, enabling efficient transfer of research advances to real-world applications. The repository is available at https://github.com/wuji3/DORAEMON. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DORAEMON: A Unified Library for Visual Object Modeling and Representation Learning at Scale Du, Ke Peng, Yimin Gao, Chao Zhou, Fan Xue, Siqiao Computer Vision and Pattern Recognition DORAEMON is an open-source PyTorch library that unifies visual object modeling and representation learning across diverse scales. A single YAML-driven workflow covers classification, retrieval and metric learning; more than 1000 pretrained backbones are exposed through a timm-compatible interface, together with modular losses, augmentations and distributed-training utilities. Reproducible recipes match or exceed reference results on ImageNet-1K, MS-Celeb-1M and Stanford online products, while one-command export to ONNX or HuggingFace bridges research and deployment. By consolidating datasets, models, and training techniques into one platform, DORAEMON offers a scalable foundation for rapid experimentation in visual recognition and representation learning, enabling efficient transfer of research advances to real-world applications. The repository is available at https://github.com/wuji3/DORAEMON. |
| title | DORAEMON: A Unified Library for Visual Object Modeling and Representation Learning at Scale |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.04394 |