How can I better train yolo-detect?

[SnifferCaptain]

I've got a hard task in my private dataset. In validation of every epoch, the box's P , map50 and map50-95 are in a reasonable range(about 0.5 in P, 0.38 in map50, 0.28 in map50-95) However, it somehow act badly in recall(just about 0.3 after 30 epochs). Is there any hyper-settings that can make the net put more focus on recall loss? Or should I code a loss function myself?

epoch	train/box_loss	train/cls_loss	train/dfl_loss	metrics/precision(B)	metrics/recall(B)	metrics/mAP50(B)	metrics/mAP50-95(B)	val/box_loss	val/cls_loss	val/dfl_loss	lr/pg0	lr/pg1	lr/pg2
7	1.3777	1.6216	1.4158	0.50245	0.33914	0.38075	0.28003	1.2912	1.6536	1.4309	0.0004523	0.0004523	0.0004523

[UltralyticsAssistant]

👋 Hello @SnifferCaptain, thank you for reaching out with your training question! It sounds like you're working on an exciting yet challenging project with your dataset 🚀. We recommend checking our Docs where you might find useful insights, especially our Tips for Best Training Results that could help address the recall issue you're experiencing.

If you think a custom loss function might be necessary, our community members on Discord 🎧 enjoy discussions around custom training and might offer novel insights.

Also, note that this is an automated response, and an Ultralytics engineer will assist you shortly. If you wish, please provide additional details about your training setup or a minimum reproducible example to help us support you further.

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Your patience is greatly appreciated while awaiting additional support, and we’re excited to see what you'll achieve with your YOLO training! 🎉

[glenn-jocher]

@SnifferCaptain based on your validation metrics, I can suggest a few targeted adjustments to improve recall while maintaining precision. First, try adjusting the confidence threshold during training to a lower value (e.g., conf=0.1) which can help detect more potential objects. You can also modify the hyp.yaml file to increase the obj (objectness) loss weight, which can encourage the model to be more aggressive in detecting objects.

For specific guidance on adjusting these hyperparameters, see the model training tips at https://docs.ultralytics.com/guides/model-training-tips/. If these adjustments don't achieve the desired results, you may want to review your dataset for class imbalance and consider data augmentation techniques.

Let us know if you need help implementing these changes or if you'd like to explore additional optimization strategies.