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Long-tail Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing models from a large number of images that follow a long-tailed class distribution.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xytpai/retinanet
https://github.com/yangyucheng000/DiVE
https://github.com/yhenon/pytorch-retinanet
https://github.com/yijiaceline/Final-Project-Group4
https://github.com/yiningzeng/darknet-fork-from-AlexeyAB
https://github.com/ylqi/count-anything
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/yudie433/darknet
https://github.com/yuliani29/yolotraining
https://github.com/yuuun/clip_pytorch
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/yytang2012/darknet
https://github.com/zhangxu0963/npc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/zhmiao/OpenLongTailRecognition-OLTR
https://github.com/zhongzisha/object_detection
https://github.com/zj463261929/darknet_mAP
https://github.com/zzhuolun/IRL
https://gitlab.com/birder/birder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.