YOLOv7

YOLOv7 is the new state-of-the-art object detector in the YOLO family. According to the paper, it is the most accurate and fastest real-time object detection to date.

About YOLOv7

The YOLOv7 algorithm is a big advancement in the field of computer vision and machine learning. It is more accurate and faster than any other object detection models or YOLO versions. It is also much cheaper to train on small datasets without any pre-trained weights. Hence, it's expected to become the industry standard for object detection in the near future.

The official paper named โ€œYOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorsโ€ was released in July 2022 by Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. The research paper has become immensely popular in a matter of days. The source code was released as open source under the GPL-3.0 license, a free copyleft license. You can find the code in the official YOLOv7 GitHub repository. The repository was awarded over 4.3k stars in the first month after release.

What's new in YOLOv7?

Serveral architectural reforms imporved speed and accuraracy in YOLOv7. Compared to the previously most accurate YOLOv6 model (56.8% AP), the YOLOv7 real-time model achieves a 13.7% higher AP (43.1% AP) on the COCO dataset.

  • Architectural Reforms
    • Model Scaling for Concatenation based Models
    • E-ELAN (Extended Efficient Layer Aggregation Network)
  • Trainable BoF
    • Planned re-parameterized convolution
    • Coarse for auxiliary and Fine for lead loss

The paper discusses the YOLOv7 architecture in great detail and provides intuition into how the model works.

YOLOv7 Tutorials

Explore the coding tutorials and how-to guides available on our website to help you get started and learn to build with YOLOv7 artificial intelligence technology

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