Comments (2)
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:
- Import your module in the model definition file (commonly
models/yolo.py
or where your model architecture is defined). - Instantiate your module within the YOLOv5 model class, and ensure it's applied before the first convolutional block of YOLOv5.
- Import your module in the model definition file (commonly
-
Adjusting the Forward Pass:
- In the
forward
method of your model, apply your enhancement module to the input images before passing them to the rest of the YOLOv5 network.
- In the
-
Parameter Registration:
- Ensure that the parameters of your external module are properly registered as part of the model's parameters. This is usually handled automatically if your module is a subclass of
nn.Module
and is instantiated as a class attribute in the YOLOv5 model.
- Ensure that the parameters of your external module are properly registered as part of the model's parameters. This is usually handled automatically if your module is a subclass of
-
Training:
- When setting up the optimizer in
train.py
, make sure it includes the parameters of the newly integrated module. You can typically do this by passingmodel.parameters()
to the optimizer, which should now include your module’s parameters if integrated correctly.
- When setting up the optimizer in
-
Configuration:
- Update any configuration files (e.g., YAML files) if your module requires specific configurations or hyperparameters.
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! 🚀
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👋 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:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
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 ⭐
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