Comments (1)
Hello! 👋
Yes, you can modify the build_optimizer
function in trainer.py
to integrate a new optimizer for your YOLO training. This change should not negatively affect the running of YOLO as long as the new optimizer is implemented correctly and is compatible with the model's requirements.
Here's a basic example of how you might modify the function:
def build_optimizer(self):
if self.optimizer_name == 'new_optimizer':
return NewOptimizer(self.model.parameters(), lr=self.lr, ...)
else:
# existing optimizers
return torch.optim.SGD(self.model.parameters(), lr=self.lr, ...)
Just ensure that your new optimizer adheres to the expected interface and that any specific requirements of the optimizer are met. If you encounter any issues, feel free to share them here!
Happy coding! 🚀
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