Real-Time Object Detection
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Real-Time Object Detection is a computer vision task that involves identifying and locating objects of interest in real-time video sequences with fast inference while maintaining a base level of accuracy.
This is typically solved using algorithms that combine object detection and tracking techniques to accurately detect and track objects in real-time. They use a combination of feature extraction, object proposal generation, and classification to detect and localize objects of interest.
<span style="color:grey; opacity: 0.6">( Image credit: CenterNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Singh-sid930/YOLO_pytorch
https://github.com/Smallflyfly/yolov3_ultralytics
https://github.com/Smiler-Jin/Yolov4
https://github.com/SnailDev/github-hot-hub
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/SonginCV/GMPHD_MAF
https://github.com/SonginCV/GMPHD_SAF
https://github.com/SonginCV/MAF_HDA
https://github.com/Spain2394/darknet
https://github.com/SpanoChristian/darknet
https://github.com/SravanChittupalli/COVID-19-Mask_detector
https://github.com/Ssong24/CenterNet_Custom
https://github.com/Stepan-coder/HandWritenSignatureDetection
https://github.com/StephenEkaputra/Mask_RCNN-TinyPascalVOC
https://github.com/StephenStorm/YOLOX
https://github.com/Stephenfang51/Focal_loss_turtorial
https://github.com/StevenBanama/Yolo-lite-Gesture
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
https://github.com/Stick-To/CenterNet-tensorflow
https://github.com/Suci2609/generate_train_list.py-
https://github.com/Sujith2k/darknet
https://github.com/Sushma07/dancedarknet
https://github.com/SweetyTian/efficientdet
https://github.com/SwinTransformer/Transformer-SSL
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/TKGgunter/yolov4_tiny_rs
https://github.com/TamirShasha/Mars_yolo
https://github.com/TangDL/DCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Techyee/darknet_resource
https://github.com/TejasBajania/Mtech_pro
https://github.com/TejasBajania/Mtech_thesis_project
https://github.com/ThanhDinhDat/center_net_points
https://github.com/TheClub4/car-detection-yolov2
https://github.com/TheHidden1/YOLO.JS
https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow
https://github.com/Tianxiaomo/pytorch-YOLOv4
https://github.com/TimVerion/caffe_rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Tony363/Teamplay_Yolov3
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.