{"url":"/method/libra-r-cnn","slug":"libra-r-cnn","name":"Libra R-CNN","full_name":"Libra R-CNN","full_name_withheld":false,"description_markdown":"**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\r\nsampling, [balanced feature pyramid](https://paperswithcode.com/method/balanced-feature-pyramid), and [balanced L1 loss](https://paperswithcode.com/method/balanced-l1-loss), respectively for reducing the imbalance at sample, feature, and objective level.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Libra R-CNN: Towards Balanced Learning for Object Detection","paper":"/paper/libra-r-cnn-towards-balanced-learning-for","first_author":"Jiangmiao Pang","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/libra-r-cnn-towards-balanced-learning-for"},"source":{"url":"http://arxiv.org/abs/1904.02701v1","title":"Libra R-CNN: Towards Balanced Learning for Object Detection","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/OceanPang/Libra_R-CNN","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Object Detection Models","url":"/methods/category/object-detection-models","pwc_aliases":[]}],"n_papers_tagged":5,"archive_num_papers":5,"papers_newest_first":[{"paper":null,"title":"Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery","date":"2024-07-27","arxiv_id":"2407.19184","n_code_links":0,"syntology":null},{"paper":null,"title":"Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models","date":"2021-11-14","arxiv_id":"2111.07355","n_code_links":0,"syntology":null},{"paper":null,"title":"Towards Balanced Learning for Instance Recognition","date":"2021-08-23","arxiv_id":"2108.10175","n_code_links":0,"syntology":null},{"paper":null,"title":"PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy","date":"2020-03-10","arxiv_id":"2003.04541","n_code_links":0,"syntology":null},{"paper":"/paper/libra-r-cnn-towards-balanced-learning-for","title":"Libra R-CNN: Towards Balanced Learning for Object Detection","date":"2019-04-04","arxiv_id":"1904.02701","n_code_links":6,"syntology":null}],"papers_shown":5,"tasks":[{"task":"/task/object-detection","name":"Object Detection","papers":3},{"task":"/task/object-detection-1","name":"object-detection","papers":3},{"task":"/task/ensemble-learning","name":"Ensemble Learning","papers":1},{"task":"/task/fire-detection","name":"Fire Detection","papers":1},{"task":"/task/fracture-detection","name":"Fracture detection","papers":1},{"task":"/task/management","name":"Management","papers":1},{"task":"/task/medical-object-detection","name":"Medical Object Detection","papers":1},{"task":"/task/object-localization","name":"Object Localization","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2019","papers":1},{"year":"2020","papers":1},{"year":"2021","papers":2},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/libra-r-cnn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}