I work with data of similar size and I use random crops during training and a sliding window for prediction. For example you could train to segment 128x128-sized crops of the input images, then put the predictions together to segment the image at full resolution and keep your 200 classes probably. But tbh 200 sounds a bit excessive anyway
kweu t1_ja8ur2q wrote
Reply to [D] Training a UNet-like architecture for semantic segmentation with 200 outcome classes. by Scared_Employer6992
I work with data of similar size and I use random crops during training and a sliding window for prediction. For example you could train to segment 128x128-sized crops of the input images, then put the predictions together to segment the image at full resolution and keep your 200 classes probably. But tbh 200 sounds a bit excessive anyway