Real-time object detection is an important component in many computer vision systems, including multi-object tracking, autonomous driving, robotics, and medical image analysis. In recent years, real-time object detection development has focused on designing efficient architectures and improving the inference speed of various CPUs, GPUs, and neural processing units (NPUs). YOLOv7 supports both mobile GPU and GPU devices, from the edge to the cloud.
@article{wang2022yolov7,title={YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},journal={arXiv preprint arXiv:2207.02696},year={2022}}
Prepare your dataset and configure the model parameters according to the usage instructions provided in the repository. For further guidance, visit the YOLOv7 GitHub repository for the latest information and updates.
YOLOv7 中引入了哪些主要功能和优化?
YOLOv7 提供了几项关键功能,彻底改变了实时目标检测:
模型重新参数化:通过优化梯度传播路径提高模型性能。
动态标签分配:使用由粗到细的引导方法,为不同分支的输出分配动态目标,提高准确性。
扩展和复合缩放:有效利用参数和计算,为各种实时应用扩展模型。
Efficiency: Reduces parameter count by 40% and computation by 50% compared to other state-of-the-art models while achieving faster inference speeds. For further details on these features, see the YOLOv7 Overview section.