Closed Pedro-Leitek closed 2 months ago
@Pedro-Leitek hi there!
Great question! The number of training epochs you choose for each evolution iteration can significantly impact the quality of the hyperparameters you obtain. Here's a detailed response to help you understand the considerations:
Shorter Epochs for Initial Evolution:
Longer Epochs for Final Training:
Given your scenario, here's a practical approach:
Initial Evolution with Shorter Epochs:
python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --evolve
Final Training with Selected Hyperparameters:
runs/evolve/hyp_evolved.yaml
) to train your model for a longer period, such as 100 epochs.python train.py --epochs 100 --data coco128.yaml --weights yolov5s.pt --hyp runs/evolve/hyp_evolved.yaml
For more detailed information on hyperparameter evolution, you can refer to our Hyperparameter Evolution Guide.
I hope this helps! If you have any further questions, feel free to ask. Happy training! 🚀
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Hi there!
I was wondering, how many epochs should we use, so that when using 300 evolve iterations, we can get good model hyperparameters? In a scenario of 10vs20 epochs and 300 evolve iterations, will the hyperparameters be the same? I mean is the amount of epochs "irrelevant"? My issue with the evolve tool is that it will consume tons of time to produce a result. The idea is to first get the hyperparameters with a few epochs and then use those parameters with a training of, lets say, 100 epochs.
Thank you
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