I hope this message finds you well. I have been thoroughly examining the remarkable work you've accomplished with the YOLO-Pruning-RKNN repository, particularly the integration of torch-pruning to facilitate model optimisation for RKNN-supported devices. The initiative to maintain the integrity of YOLOv8's original structure whilst enabling pruning is commendable.
However, I would like to propose an enhancement to the current pruning strategy. While the repository allows for a significant reduction in parameters by 75% without accuracy degradation, there is potential for further optimisation through the implementation of a more dynamic pruning approach.
Suggested Enhancements:
Adaptive Pruning: Introduce an adaptive pruning mechanism that adjusts the prune_ratio based on the layer sensitivity analysis. This could potentially lead to a more efficient pruning process, ensuring that only the most redundant parameters are pruned while preserving the critical ones for maintaining accuracy.
Iterative Pruning and Fine-tuning: Although the repository currently supports iterative pruning steps, it would be beneficial to explore a more granular control over the fine-tuning process between each pruning iteration. This could involve a progressive decay in the learning rate or the application of different data augmentation techniques to recover any lost accuracy.
Benchmarking on RKNN Devices: To solidify the efficacy of the pruning process, it would be advantageous to include benchmark results post-pruning on various RKNN-supported devices. This would provide users with a clearer understanding of the performance gains in real-world scenarios.
Documentation on Pruning Best Practices: Lastly, I believe that the community would greatly benefit from a comprehensive guide on best practices for pruning YOLO models tailored for RKNN platforms. This could include case studies, parameter tuning tips, and insights into the trade-offs between model size, speed, and accuracy.
I am eager to hear your thoughts on these suggestions and would be delighted to contribute to the development and testing of these enhancements.
Dear Contributors,
I hope this message finds you well. I have been thoroughly examining the remarkable work you've accomplished with the YOLO-Pruning-RKNN repository, particularly the integration of
torch-pruningto facilitate model optimisation for RKNN-supported devices. The initiative to maintain the integrity of YOLOv8's original structure whilst enabling pruning is commendable.However, I would like to propose an enhancement to the current pruning strategy. While the repository allows for a significant reduction in parameters by 75% without accuracy degradation, there is potential for further optimisation through the implementation of a more dynamic pruning approach.
Suggested Enhancements:
Adaptive Pruning: Introduce an adaptive pruning mechanism that adjusts the
prune_ratiobased on the layer sensitivity analysis. This could potentially lead to a more efficient pruning process, ensuring that only the most redundant parameters are pruned while preserving the critical ones for maintaining accuracy.Iterative Pruning and Fine-tuning: Although the repository currently supports iterative pruning steps, it would be beneficial to explore a more granular control over the fine-tuning process between each pruning iteration. This could involve a progressive decay in the learning rate or the application of different data augmentation techniques to recover any lost accuracy.
Benchmarking on RKNN Devices: To solidify the efficacy of the pruning process, it would be advantageous to include benchmark results post-pruning on various RKNN-supported devices. This would provide users with a clearer understanding of the performance gains in real-world scenarios.
Documentation on Pruning Best Practices: Lastly, I believe that the community would greatly benefit from a comprehensive guide on best practices for pruning YOLO models tailored for RKNN platforms. This could include case studies, parameter tuning tips, and insights into the trade-offs between model size, speed, and accuracy.
I am eager to hear your thoughts on these suggestions and would be delighted to contribute to the development and testing of these enhancements.
Thank you for your time and consideration.
Best regards, yihong1120