Papers with Code 아카이브
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/NovasMax/R3Det-Refined-Single-Stage-Detector-with-Feature-Refinement-for-RO
https://github.com/OFRIN/Tensorflow_RetinaFace
https://github.com/PaddlePaddle/PaddleYOLO
https://github.com/PolarisAlpha/darknet
https://github.com/Popoooo/darknet_test
https://github.com/RangiLyu/nanodet
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
https://github.com/RichardMathewsII/YOLBO
https://github.com/RuoyuChen10/objectdetection-saliency-maps
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SanketAgrawal/Catagorical-Focal-Loss
https://github.com/ShaharrHorn/Detection-Game-logic
https://github.com/ShaojieJiang/tldr
https://github.com/Shraddha2013/customyolo
https://github.com/Shraddha2013/darknett
https://github.com/Shraddha2013/darknetyol
https://github.com/Shraddha2013/darknetyolo
https://github.com/Shraddha2013/darknetyolo3
https://github.com/Shraddha2013/mydarknetyolo
https://github.com/Shraddha2013/myfileyolo
https://github.com/Stephenfang51/Focal_loss_turtorial
https://github.com/Suci2609/generate_train_list.py-
https://github.com/Sushma07/dancedarknet
https://github.com/TanyaChutani/SSD-Tensorflow2.0
https://github.com/Techyee/darknet_resource
https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow
https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet
https://github.com/Viveksbawa/SARAS-ESAD-Baseline
https://github.com/Wangxy2180/darknetKinectDetect
https://github.com/YIZHE12/fashiontags
https://github.com/YIZHE12/robots
https://github.com/Yashgh7076/Eluvio-ML-Scene-Segmentation
https://github.com/Yonder-OSS/D3M-Primitives
https://github.com/Yuxiang1995/ICDAR2021_MFD
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
https://github.com/ZTao-z/multiflow-resnet-ssd
https://github.com/ZhenJie-Zhang/darknet_shoe
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.