The question of reproduction

[yunwuxin123]

When using my own data set and my own yaml file for training, although the same file and the same parameters are used for multiple runs, the results are not the same. I wonder if this is due to random seeds? And if I am satisfied with the results of a run, can I find my seed in the run output so I can fix it for use?

[pderrenger]

Hello! Yes, varying results across training runs with the same parameters can result from differing random seeds. To ensure reproducibility, you can set the seed manually in your training command.

Here's an example:

yolo train data=your_dataset.yaml model=yolov8n.yaml seed=42

In this command, seed=42 sets the random seed to 42, which you can adjust as needed. The seed isn't automatically recorded in the run outputs, so it's a good idea to manually set and note it if you achieve desirable results.

Happy training! 😊

[yunwuxin123]

Thank you for your answer! Can I get better performance for example mAP by setting the random seed? Also, what is the scope and span of the seed?

[pderrenger]

Hi! Setting the random seed won't directly improve the performance metrics like mAP, but it ensures that your training is reproducible with the same initialization and batch shuffling across runs.

The seed influences operations like weight initialization and the order in which your data is processed during training. Thus, it helps in achieving consistent results when you're experimenting with different model tweaks or hyperparameters.

If you're looking to enhance performance, consider experimenting with different model architectures, hyperparameters, or augmentations. Keep up the great work! 😊

[yunwuxin123]

Thank you for your teaching! 😊

[pderrenger]

You're very welcome! If you have any more questions or need further assistance as you continue using YOLOv8, feel free to reach out. Happy coding! 😊