Closed znmzdx-zrh closed 1 week ago
Hello! Integrating external modules into YOLOv5 for joint training involves a few key steps to ensure the new parameters are recognized and updated during training. Here’s a concise guide on how to proceed:
Module Integration: First, ensure your external module (e.g., image enhancement filters) is defined in a way that it can be seamlessly integrated into the YOLOv5 architecture. This typically means wrapping your filters in a PyTorch nn.Module
.
Modify the Model Definition:
models/yolo.py
or where your model architecture is defined).Adjusting the Forward Pass:
forward
method of your model, apply your enhancement module to the input images before passing them to the rest of the YOLOv5 network.Parameter Registration:
nn.Module
and is instantiated as a class attribute in the YOLOv5 model.Training:
train.py
, make sure it includes the parameters of the newly integrated module. You can typically do this by passing model.parameters()
to the optimizer, which should now include your module’s parameters if integrated correctly.Configuration:
By following these steps, your external module’s parameters should be trainable along with the rest of the YOLOv5 model. If you encounter specific issues or errors during this process, feel free to share them here for further assistance. Happy coding! 🚀
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
For additional resources and information, please see the links below:
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
Search before asking
Question
How to integrate some parameters from imported external modules into the entire YOLOv5 model for joint training?I want to introduce some filters as a module into the YOLOv5 model to enhance images. Input the original image of Yolov5 to the result of additional enhancement module, and the enhanced image is obtained in the first layer of the convolution block into Yolov5, and then trained together.How can I merge the parameters inside the filters into the trainable parameter list of YOLOv5 for joint training and updating?Thank you for help. In common.py
In yaml
In train.py
![train_optimizer](https://github.com/ultralytics/yolov5/assets/106502924/e6efed26-0e61-4d17-943c-d1a356804de3)
Additional
No response