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/Manugoyal12345/Yet-Another-EfficientDet-Pytorch
https://github.com/Marcos14Almeida/darknet
https://github.com/MariefW/6.-trainingYOLO
https://github.com/Maskify/darknet
https://github.com/MasoumehVahedi/Yolo-Object-Detection
https://github.com/MaximKuklin/3D_Object_Detection_Diploma
https://github.com/MaximeBataille/tennis_tracking
https://github.com/Mayurji/Image-Classification-PyTorch
https://github.com/MegEngine/Models/tree/master/official/vision/detection
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/MegEngine/YOLOX
https://github.com/Mersive-Technologies/yolov3
https://github.com/MikelBros/Yolov3_DeepSort_Pytorch
https://github.com/Mind23-2/MindCode-101/tree/main/IPT
https://github.com/Mind23-2/MindCode-101/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-101/tree/main/nasnet
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/Mind23-2/MindCode-105
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-117
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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-130
https://github.com/Mind23-2/MindCode-15
https://github.com/Mind23-2/MindCode-155
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Mind23-2/MindCode-165
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Mind23-2/MindCode-176
https://github.com/Mind23-2/MindCode-28
https://github.com/Mind23-2/MindCode-29
https://github.com/Mind23-2/MindCode-3/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-3/tree/main/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/Mind23-2/MindCode-3/tree/main/nasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-36
https://github.com/Mind23-2/MindCode-37
https://github.com/Mind23-2/MindCode-43
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Mind23-2/MindCode-5/tree/main/PVAnet
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
https://github.com/Mind23-2/MindCode-51
https://github.com/MindCode-4/code-3/tree/main/convnext
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.