Papers with Code 아카이브
Pedestrian Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 9
- 보존된 평가 기록
- 92
Pedestrian detection is the task of detecting pedestrians from a camera.
Further state-of-the-art results (e.g. on the KITTI dataset) can be found at 3D Object Detection.
<span style="color:grey; opacity: 0.6">( Image credit: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KyeongmoonKim/sb
https://github.com/LEGO999/YOLOV3-TF2
https://github.com/LIU1514/Yolov3-
https://github.com/LdDl/go-darknet
https://github.com/LdDl/rust-road-traffic
https://github.com/Li-Chengyang/MSDS-RCNN
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
https://github.com/Lmath11/darknet
https://github.com/Lmonster/darknet
https://github.com/Lornatang/PyTorch-yolov3
https://github.com/Lornatang/YOLOv3-PyTorch
https://github.com/MIC-DKFZ/DetectionAndRegression
https://github.com/MIC-DKFZ/RegRCNN
https://github.com/MINED30/Face_Mask_Detection_YOLO
https://github.com/MRZWD/yolov3_caltech
https://github.com/MSJawad/yolov3
https://github.com/MalongTech/research-fad
https://github.com/MariefW/6.-trainingYOLO
https://github.com/Masao-Taketani/FOTS_OCR
https://github.com/Maskify/darknet
https://github.com/MaximeBataille/tennis_tracking
https://github.com/Mayurji/Image-Classification-PyTorch
https://github.com/MegEngine/Models/tree/master/official/vision/detection
https://github.com/Mersive-Technologies/yolov3
https://github.com/MikelBros/Yolov3_DeepSort_Pytorch
https://github.com/Mind23-2/MindCode-127
https://github.com/Mind23-2/MindCode-129
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/Mind23-2/MindCode-176
https://github.com/Mind23-2/MindCode-46
https://github.com/Mind23-2/MindCode-47
https://github.com/MindSpore-paper-code-2/code3/tree/main/yolov3_tiny
https://github.com/MindSpore-paper-code-3/code10/tree/main/FCOS
https://github.com/MindSpore-paper-code-3/code3/tree/main/FCOS
https://github.com/MindSpore-paper-code-3/code5/tree/main/res2net_yolov3
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet101
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet152
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.