YOLO YOLOv7 Top Builders
Explore the top contributors showcasing the highest number of YOLO YOLOv7 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 date||July, 2022|
|Type||Real time object detection|
- 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 Hackathon projects
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