Description
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 Using This Method
Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery2024-07-27Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models2021-11-14Towards Balanced Learning for Instance Recognition2021-08-23PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy2020-03-10Libra R-CNN: Towards Balanced Learning for Object Detection2019-04-04