On which data evaluate an object detection model ? (similar or real life data ?)

I’m training an object detection model (SSD300) to detect and classify body poses in thermal images.

Even I have more than 2k different poses, the background does not change much (I have only 5 different points of view).

I trained my model on these images (70% for the training and 30% for validation).

Now, I want to evaluate the model on an unbiased dataset.

Should I keep images of my dataset for this purpose or should I use a real life dataset ?

(A good solution would be to have a real life training set, but I don’t have)

I tried both, but as expected, I have an mAP=0.9 when evaluated on similar pictures and mAP=0.5 when evaluated on completely different images.

Bonus question: is mAP a relevant metric when I want to show result to a client ? (e.g a client doesn’t understand if I tell him “my model has a mAP=0.7”)

Precision-Recall ? (but I have to choose a pose classification threshold…)