I already trained a deep neural network called YOLO (You Only Look Once) with high-quality images (1920 by 1080 pixels) for a detection task. The result for mAP and IOU were 93% and 89% respectively.
I wanted to decrease the quality of my training data set using some available filters, then I use those low-quality images along with high-quality images to train the network again.
Does this method increase the accuracy (or, in general, performance) of the deep neural network (for a detection task)? Like mAP and IOU?
My data set is vehicle images.
mAP: mean average precision
IOU: intersection over union ( or overlap)