Extremely low precision but high mAP

[eric80739]

Discussed in #12928

^{Originally posted by eric80739 April 16, 2024}
I'm using YOLOv5n to train on a very small dataset with only around 10 images.
Image size is 512*512, and the targets are very clear. I want to detect the only one green dot in the image below:

Therefore, I expect the model to converge quickly, ignoring the overfitting.

However, the training isn't going as expected, and it's taking a long time to converge. During training, I noticed a unreasonable phenomena, very low precision but unusually high mAP:

The training performance of the model on my other dataset is excellent, as shown in the graph below:

So, we can probably rule out issues with parameter settings. I would like to inquire about how to improve the training method without increasing the number of images.
Thank you!

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[glenn-jocher]

Hi Eric! 👋

It's great that you're diving into training YOLOv5n, even with a small dataset. The peculiar case of low precision yet high mAP often indicates a model might be leaning towards making many detections to ensure it doesn't miss the target, but this can lead to a high number of false positives.

Given your unique situation (tiny dataset, clear single-object targets), here are a few suggestions:

• Data Augmentation: To artificially expand your dataset without getting more images. This includes techniques like flipping, scaling, or adding noise.
• Transfer Learning: Utilize a pre-trained model and fine-tune it on your small dataset, which can help the model learn better with limited data.
• Adjust Confidence Thresholds: Play around with the confidence thresholds during inference to reduce false positives.

Remember, with such a small dataset, overfitting is almost given, but since you're okay with overlooking it for now, these suggestions should help improve performance without needing more images.

Feel free to dive into our docs https://docs.ultralytics.com/yolov5/ for more detailed guidance on training adjustments and techniques. Keep experimenting, and good luck! 🚀

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