YOLO YOLOv7 AI technology Top Builders

Explore the top contributors showcasing the highest number of YOLO YOLOv7 AI technology app submissions within our community.


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 “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors” 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.

Relese dateJuly, 2022
TypeReal time object detection


Discover YOLOv7

  • Research Paper YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, official research paper
  • Hugging Face Spaces Test YOLOv7 in the browser with Hugging Face Spaces
  • GitHub Repository View the GitHub repository for YOLOv7

YOLO YOLOv7 AI technology Hackathon projects

Discover innovative solutions crafted with YOLO YOLOv7 AI technology, developed by our community members during our engaging hackathons.

World need talents

World need talents

This project is an advanced platform that utilizes artificial intelligence to analyze football. The main goal of the platform consists of three main parts: Performance Analysis: The platform gathers data and statistics about players' performance in live matches. This is done using artificial intelligence techniques to analyze players' movements and estimate their positions on the field. This data is analyzed to identify each player's strengths and weaknesses, and how they can improve their performance. Talent Discovery: The platform uses artificial intelligence to discover young talents in football. Machine learning techniques are used to analyze players' performance in matches and training sessions, searching for performance patterns similar to professional players. Therefore, this project uses technology to make football more understandable and accessible, helping improve players' performance, uncover young talents, and make knowledge available to as many fans as possible. The main goal of this project is to use artificial intelligence for our understanding and analysis of football. By specifically analyzing the players' weight boxing statistics, lightweight boxing, strong and lightweight boxing for all players, they can improve them. In addition, the project aims to use artificial intelligence to transform young talents in football. This can help provide greater opportunities for talented young people who may be overlooked by the traditional screening system. Overall, the project aims to use technology to improve how feet are analyzed and understood, and to enable this knowledge to be accessible to as many people as possible