YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors

Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have spawned when dealing with these latest state-of-the...

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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 7464 - 7475
Hauptverfasser: Wang, Chien-Yao, Bochkovskiy, Alexey, Liao, Hong-Yuan Mark
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2023
Schlagworte:
Computer architecture
Computer vision
detection
Detectors
Object detection
Real-time systems
Recognition: Categorization
retrieval
Source coding
Training
ISSN:1063-6919
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Zusammenfassung:Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have spawned when dealing with these latest state-of-the-art methods. To address the topics, we propose a trainable bag-of-freebies oriented solution. We combine the flexible and efficient training tools with the proposed architecture and the compound scaling method. YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 120 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. Source code is released in https://github.com/WongKinYiu/yolov7.
ISSN:1063-6919
DOI:10.1109/CVPR52729.2023.00721