Extremely low precision but high mAP

[eric80739]

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!