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/yiningzeng/darknet-fork-from-AlexeyAB
https://github.com/youssef-kishk/ROS-Based-Robot-Object-Detection-Recognition
https://github.com/yqyao/FCOS_PLUS
https://github.com/yrouphail/yolo-independent
https://github.com/yrouphail/yolov3
https://github.com/yudie433/darknet
https://github.com/yuedongli1/yolov3_mindspore
https://github.com/yueruchen/sppnet-pytorch
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
https://github.com/yukaryote/darknet
https://github.com/yuliani29/yolotraining
https://github.com/yushuz/me599_yolo
https://github.com/yytang2012/darknet
https://github.com/yzgrfsy/tf-fastrcnn-crop
https://github.com/zanmange/darknet
https://github.com/zeegeeko/YOLOv3
https://github.com/zephyrcho/darknetV4
https://github.com/zhanghuiyao/yolov3_mindspore
https://github.com/zhanghuiyao/yolov7_mindspore
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/zhangming8/libtorch-yolov3
https://github.com/zhaozhijie1997/Unifed-Lane-and-Traffic-Sign-detection
https://github.com/zhongzisha/object_detection
https://github.com/zhubinQAQ/Ins
https://github.com/zj463261929/darknet_mAP
https://github.com/zjjszj/yolov3
https://github.com/zxqcreations/faster-rcnn-tf
https://github.com/zy0851/FB-m-RCNN
https://github.com/zzfpython/yolov3-change
https://github.com/zzh8829/yolov3-tf2
https://github.com/zzhuolun/IRL
https://gitlab.com/birder/birder
https://gitlab.com/eavise/lightnet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.