https://github.com/jfc4050/detect-to-track
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.