Different IOU thresholds

[Ershebet]

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Hello!
I've trained my model with iou_thres = 0.2 (ref to hyp.scratch-low.yaml)
Which iou threshold should I use for inference? I see 0.45 in detect.py and 0.6 in val.py. Should I use 0.2, 0.45 or 0.6?
Could you please explain the difference between this values.

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[glenn-jocher]

@Ershebet hello!

The IOU threshold used during training is for the NMS step to determine which detections to keep based on their overlap. For inference, you can start with the default value of 0.45 in detect.py. This value is a balance between precision and recall, but you can adjust it based on your specific needs:

• Increase the IOU threshold if you want to reduce false positives (at the risk of missing some true positives).
• Decrease the IOU threshold if you want to capture more true positives (but you might also increase false positives).

The 0.6 value in val.py is typically used for validation purposes to be more strict on the quality of the predictions.

It's often a good idea to experiment with different IOU thresholds during inference to find the best balance for your particular use case. Remember, the optimal value can vary depending on the characteristics of your dataset and the requirements of your application.

Happy experimenting! 😊 If you need more detailed explanations, please refer to our documentation.

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