convolution neural network image size distortion
In case you want to preserve your data, you can crop it randomly and use it to transform to square. That way your model will look on the cropped part of the image. Doing so can increase your data but this is one good data if you save the transformed image. However using random crop function from from the dataloader will stream line the process. Cropping is a good augmentation technique for preprocessing the data.
I have 2 data sets of images, one is perfect square, so resizing to 224x224 for CNN will not result in any distortion, the other dataset is not square so resizing to 224x224 will result in image distortion.
I will split the sets to train and validation, is this a good way to train the model? will there be any bias in the model?
I am afraid the model will identify distortion rather than the real differences between the 2 sets..