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/westerndigitalcorporation/YOLOv3-in-PyTorch
https://github.com/williamccondori/YOLO-NFPA
https://github.com/witwickey/alexeyDarknet
https://github.com/witwickey/darknet
https://github.com/wodyjowski/colab-training
https://github.com/worldlife123/maskrcnn-benchmark
https://github.com/wrappr/wrappr-core
https://github.com/wslerry/yolov5
https://github.com/wsmlby/darknetnano
https://github.com/wuyangzhang/maskrcnn
https://github.com/xiamenwcy/extended-caffe
https://github.com/xiaogangLi/tensorflow-Darknet53-YOLOv3
https://github.com/xiaoxd16/Palm
https://github.com/xinpingwang/tf-faster-rcnn
https://github.com/xinshasha/YoloV3
https://github.com/xinwei666/mmgenerativeir
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/xiuyu0000/tutorials/tree/main/yolov3
https://github.com/xuannianz/keras-fcos
https://github.com/xuantruongdang/module-yolo
https://github.com/xuanyuzhou98/SqueezeSeg
https://github.com/xxnote/stunning-doodle
https://github.com/xytpai/DetX-FCOS
https://github.com/xytpai/fcos
https://github.com/xytpai/retinanet
https://github.com/xyuan-wyze/darknet
https://github.com/yang-0201/YOLOv6_pro
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
https://github.com/yang-233/waste_detect
https://github.com/yangyucheng000/Paper-3/tree/main/FPNet-MindSpore-master
https://github.com/yanxp/rcnn-discovery
https://github.com/yaoming0712/dalinmask
https://github.com/ycchiusieve/yolo3
https://github.com/yet124/yolov3-pytorch
https://github.com/yfnn/fused-model
https://github.com/yfnn/person_subclass
https://github.com/yhenon/pytorch-retinanet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.