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hey guys through this video id like to warn you about the use of jpeg images for scientific image processing tasks now in the last tutorial i warned you about the data augmentation part of keras and i said for categorical labels please be careful because its changing your actual labels now jpeg does even worse okay and lets actually let me show you exactly what i mean again taking the example from last time so we have images okay and corresponding masks this mask here is a hand painted lets say label representing different regions in our original image so this is a semantic segmentation example okay so this gray dark grayish region is representing these bright pixels okay so now if you go back to my image and look at the pixel values lets bring up the histogram you can see the histogram has four peaks that means all the pixels in my image are represented by four values thats it okay if you look at the list these values are 33 okay so i have 957 individual data points showing nin