Methods › Computer Vision › Object Detection Models › Libra R-CNN
Libra R-CNN
Introduced by Jiangmiao Pang et al. in Libra R-CNN: Towards Balanced Learning for Object Detection
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Libra R-CNN is an object detection model that seeks to achieve a balanced training procedure. The authors motivation is that training in past detectors has suffered from imbalance during the training process, which generally consists in three levels – sample level, feature level, and objective level. To mitigate the adverse effects, Libra R-CNN integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level.
Papers archive 2025-07-28
5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery 27 Jul 2024 · 0 repositories · arXiv:2407.19184
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Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models 14 Nov 2021 · 0 repositories · arXiv:2111.07355
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Towards Balanced Learning for Instance Recognition 23 Aug 2021 · 0 repositories · arXiv:2108.10175
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PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy 10 Mar 2020 · 0 repositories · arXiv:2003.04541
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Libra R-CNN: Towards Balanced Learning for Object Detection 4 Apr 2019 · 6 repositories · arXiv:1904.02701
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Object Detection | 3 |
| object-detection | 3 |
| Ensemble Learning | 1 |
| Fire Detection | 1 |
| Fracture detection | 1 |
| Management | 1 |
| Medical Object Detection | 1 |
| Object Localization | 1 |
| Transfer Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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