Have you ever tried using aimet to lighten the model？

[CangHaiQingYue]

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• [X] I have searched the YOLOv5 issues and found no similar feature requests.

Description

Like QAT or PTQ....

Use case

No response

Additional

No response

Are you willing to submit a PR?

• [ ] Yes I'd like to help by submitting a PR!

[github-actions[bot]]

👋 Hello @CangHaiQingYue, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:

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cd yolov5
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Introducing YOLOv8 🚀

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Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

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[glenn-jocher]

@CangHaiQingYue hello! Thanks for reaching out with your question. 🌟

We're always exploring ways to optimize and enhance YOLOv5, including various quantization and compression techniques. While we haven't specifically documented the use of AIMET with YOLOv5, the idea of employing techniques like QAT (Quantization Aware Training) or PTQ (Post Training Quantization) is certainly in line with our goals for efficiency and performance.

If you're interested in experimenting with AIMET or similar tools on YOLOv5, we encourage you to dive in and share your findings with the community. Your contributions could provide valuable insights and potentially lead to improvements in the model.

For more detailed discussions or to share your results, please feel free to contribute to the issues or discussions on our GitHub repo. Your input is highly appreciated!

Happy experimenting! 🚀

[github-actions[bot]]

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