YOLOv7 by Academia Sinica
The 2016 release of YOLO caused a furor among computer vision professionals. It provided high speed and accuracy for a fundamental problem with applications in autonomous driving, robotics, safety, medical image analysis, and other fields. Then, it also provided multiscale predictions, a better baseline classifier, and other tricks to improve
![YOLOv7 by Academia Sinica](/content/images/2022/08/1_54s9-MybT8yAnmI9MKwydA.png)
The 2016 release of YOLO caused a furor among computer vision professionals. It provided high speed and accuracy for a fundamental problem with applications in autonomous driving, robotics, safety, medical image analysis, and other fields. Then, it also provided multiscale predictions, a better baseline classifier, and other tricks to improve YOLO learning and performance.
![](https://dataphoenix.info/content/images/2022/08/0_PD9dWzEzQfmFiDZW.png)
Now, Academia Sinica continues the development of YOLO in its new work YOLOv7, which introduces new methods of "extension" and "combined scaling" — they make efficient use of parameters and computation, and outperform all known real-time object detectors in speed and accuracy.
Thus, the proposed combined scaling method improves the real-time object detection accuracy without increasing the output cost, while preserving the properties of the original model design and its optimal structure.