Summary of Optimizing Yolov5s Object Detection Through Knowledge Distillation Algorithm, by Guanming Huang et al.
Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm
by Guanming Huang, Aoran Shen, Yuxiang Hu, Junliang Du, Jiacheng Hu, Yingbin Liang
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of knowledge distillation in target detection tasks, specifically investigating how different distillation temperatures affect student model performance. By using YOLOv5l as the teacher network and YOLOv5s as the student network, it was found that increasing distillation temperature improves student accuracy, ultimately achieving better mAP50 and mAP50-95 indicators than the original YOLOv5s model at a specific temperature. The results demonstrate that suitable knowledge distillation strategies not only improve model accuracy but also enhance reliability and stability in practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make machine learning models better by sharing their knowledge with smaller, simpler models. They used two types of YOLO models: one big (YOLOv5l) that’s very good at detecting targets, and a smaller version (YOLOv5s) that needs help getting better. By letting the bigger model teach the smaller one, they found that making the bigger model “share its knowledge” by increasing the temperature makes the smaller model get better too! This helps make the smaller model more reliable and good at detecting targets in real-life situations. |
Keywords
» Artificial intelligence » Distillation » Knowledge distillation » Machine learning » Student model » Temperature » Yolo
