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Dense Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/detectron2
https://github.com/feidfoe/AdjustBnd4Imbalance
https://github.com/fizyr/keras-retinanet
https://github.com/fznsakib/great-ape-behaviour-detector
https://github.com/gau-nernst/CenterNet
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
https://github.com/ghadahamed/darknet
https://github.com/ghaniskn/GCorp-Darknet
https://github.com/gokulprasadthekkel/pytorch-multi-class-focal-loss
https://github.com/gyes00205/waymo_tf_object_detection
https://github.com/haithink/myWindowsDarknet
https://github.com/hamzaMahdi/darknet
https://github.com/hankpark0706/darknet
https://github.com/hhuaibo/darknet
https://github.com/hisiter97/darknet
https://github.com/huytranvan2010/RetinaNet-Understanding
https://github.com/hweersot/darknet-custom
https://github.com/hyang0129/foodclassapp
https://github.com/iskandari/darknet
https://github.com/istvanmegyeri/audio_tagging
https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
https://github.com/jfc4050/detect-to-track
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jndeng/DACSDC-DeepZ
https://github.com/joheras/darknet-colab
https://github.com/jolibrain/caffe
https://github.com/juergenlandauer/Maya-Challenge
https://github.com/kalelpark/ral
https://github.com/karan96/NewOne
https://github.com/kawshik8/DL-project
https://github.com/khaled2ahmed/k2a
https://github.com/kongzhiyou/darknet-master
https://github.com/kornia/kornia
https://github.com/kuangliu/pytorch-fpn
https://github.com/kvijaykrish/Bosch_AI_VehicleClassification
https://github.com/kyunghwan/darknet_v3
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.