I got stuck with a problem, Thanks in advance for the solution.
The custom model is trained to detect person and faces with data set of the respective images in a White Background.
The model got an accuracy about 98% for both classes.
While checking the model accuracy in deep stream with coloured and textured background, the inferred bounding boxes seem to populate randomly all-over the screen.
My Questions :
Does the quality of the data set plays a vital role ?
[Such as sharpness/blurred edges, textures, colours]
If the model is trained in a data set of simple background to detect object, will the performance of the get worse on inferring over complex background ?