Semantic Scholar Open Access 2022 9874 sitasi

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

Chien-Yao Wang Alexey Bochkovskiy H. Liao

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (3)

Chien-Yao Wang

Alexey Bochkovskiy

H. Liao

Format Sitasi

APA MLA BibTeX

Wang, C., Bochkovskiy, A., Liao, H. (2022). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. https://doi.org/10.1109/CVPR52729.2023.00721

Akses Cepat

Lihat di Sumber doi.org/10.1109/CVPR52729.2023.00721

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 9874×
Sumber Database: Semantic Scholar
DOI: 10.1109/CVPR52729.2023.00721
Akses: Open Access ✓