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.
Source: Libra R-CNN: Towards Balanced Learning for Object DetectionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Object Detection | 2 | 40.00% |
Ensemble Learning | 1 | 20.00% |
Medical Object Detection | 1 | 20.00% |
Object Localization | 1 | 20.00% |
Component | Type |
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Balanced Feature Pyramid
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Feature Pyramid Blocks | |
Balanced L1 Loss
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Loss Functions | |
IoU-Balanced Sampling
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Prioritized Sampling |