Tensorflow Object Detection: Resizing Images and Padding

Question

I am trying to create a tensorflow object detection with Single Shot Multibox Detector (SSD) with MobileNet. My dataset consists of images larger than 300x300 pixels (e.g. 1280x1080). I know that tensorflow object detection reduces the images to 300x300 in the training process, what I am interested in is the following:

Does it have a positive or negative influence on the later object detection if I reduce the pictures to 300x300 pixels before the training with padding? Without padding I don't think it has any negative effects, but with padding I'm not sure if it has any effects that I'm overlooking.

Thanks a lot in advance!

Answer 1

I dont know SSD, but CNNs generally use convolutional layers as feature extractors, stacked upon another with different kernel sizes representing different feature sizes, i.e. using spatial correlation to their advantage. If you use padding, the padding will thus be incorporated into the extracted features, possible corrupting your results.