how to find why mAP suddenly increased about yolov5 HOT 4 OPEN

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@MiNaMisan hi there! 👋

Thank you for providing detailed information and visuals. The sudden increase in mAP you observed could be due to several factors:

1. Randomness in Training: Neural network training involves stochastic processes, and sometimes, random initialization or data shuffling can lead to unexpected spikes in performance.

2. Learning Rate and Hyperparameters: As you suspected, the learning rate or other hyperparameters might have played a role. You can experiment with different learning rates or use a learning rate scheduler to see if it stabilizes the training process.

3. Data Augmentation: Variations in data augmentation can sometimes lead to sudden improvements. Ensure that your augmentation settings are consistent across all training runs.

4. Batch Size: Changing the batch size can affect the training dynamics. Larger batch sizes generally provide more stable gradients but may require adjustments in learning rate.

To identify the specific reason, you can:

• Review Training Logs: Check for any anomalies or patterns in the training logs around the epochs where the spike occurred.
• Hyperparameter Tuning: Use tools like Hyperparameter Evolution to optimize your hyperparameters systematically.

For more detailed guidance on achieving the best training results, you can refer to our Tips for Best Training Results page.

Good luck with your training! 🍀 If you have any further questions, feel free to ask.

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@glenn-jocher thanks for replying!
I'm trying to solve this problem now by adjusting hyperparameters now.

please allow ask one more question

Review Training Logs: Check for any anomalies or patterns in the training logs around the epochs where the spike occurred.
I've used TensorBoard to check for any suspicious parameters during training, but there are only limited parameters I can monitor: train obj/box/cls loss, val obj/box/cls loss, metrics of precision, recall, and mAP0.5, as well as lr0, lr1, and lr2.

If I want to see more parameters, such as total loss or variable updates for each epoch, is this possible? Also, are there any useful tools that can help me specify these parameters?

Thank a lot

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Hi @MiNaMisan,

Great to hear you're diving into hyperparameter adjustments!

Regarding your question about monitoring more parameters in TensorBoard, YOLOv5's integration with TensorBoard primarily focuses on the key metrics you've listed. If you're looking to track additional details like total loss or specific variable updates per epoch, you might need to modify the logging code in the training script.

For a more customized tracking, you might consider using other tools like Weights & Biases, which integrates well with YOLOv5 and allows for more extensive monitoring and visualization options. You can enable Weights & Biases in your training by setting --wandb in your training command, and it will automatically log more metrics and provide a richer interface for analysis.

Hope this helps, and keep up the great work!

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