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

Abstract

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.

Cite this paper

Related papers

1. Deep Residual Learning for Image Recognition2016
2. ImageNet classification with deep convolutional neural networks2017
3. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks2016
4. You Only Look Once: Unified, Real-Time Object Detection2016
5. Going deeper with convolutions2015