Is there any problem if I try to train the U-Net Image Segmentation model introduced in Weeks 3 assignment on a different dataset?
The dataset I’m trying to train my model with is “cityscapes_data”. This dataset has got 3475 images (RGB images and Mask images). There are 30 different classes defined. The major difference between this dataset and the one introduced in the assignment (CARLA dataset) is that the Mask images do not have 4 number of channels, they just have 3 (RGB).
The only part of the code I have changed is the unet_model function (now the number of classes defined is n_classes = 30 instead of n_classes = 23), but I guess I should change lots of parts of the code, such as the preprocessing image part.
Does someone know what things I should change in order to be able to run the same U-Net funtion defined in our notebook? Moreover, is there any way I could transform the cityscapes_data Mask images to the same format (4 number of channels) as the CARLA Mask images dataset?
I would appreciate any help.
Thanks a lot!