Papers with Code 아카이브
Pedestrian Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/hasanirtiza/PedesFormer-SwinTransformer-For-Pedestrian-Detection
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
https://github.com/hellotrik/torch_yolov3
https://github.com/henrytsui000/YOLO
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/hhuaibo/darknet
https://github.com/hirohiro23/Darknet
https://github.com/hisiter97/darknet
https://github.com/hj963434688/Faster_rcnn
https://github.com/hnuzhy/bpjdet
https://github.com/holytrick/AlexeyAB_darknet
https://github.com/hou-yz/multiviewx
https://github.com/huan123/py-fatser-rcnn
https://github.com/huytranvan2010/RetinaNet-Understanding
https://github.com/hweersot/darknet-custom
https://github.com/hwillard98/htmlify-yolo-training
https://github.com/hyang0129/foodclassapp
https://github.com/iamsingh/darknet
https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/iberganzo/darknet
https://github.com/imshamsher/darknet
https://github.com/insigh/Faster-RCNN-Tensorflow
https://github.com/iskandari/darknet
https://github.com/istvanmegyeri/audio_tagging
https://github.com/ithuanhuan/gpu-py3-faster-rcnn
https://github.com/ithuanhuan/py-fatser-rcnn
https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
https://github.com/iyyun/Part-CNN
https://github.com/jahutwb/DL_dosimetry
https://github.com/jayin301/2021AutonomousACCar
https://github.com/jellywangjie/yolov3
https://github.com/jessychen1016/yolov3Onpytorch
https://github.com/jfc4050/detect-to-track
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jianmingwuhasco/yolov3
https://github.com/jih189/yolo3
https://github.com/jndeng/DACSDC-DeepZ
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.