I want to develop a CNN model to identify 24 hand signs in American Sign Language. I created a custom dataset that contains 3000 images for each hand sign i.e. 72000 images in the entire dataset.
For training the model, I would be using 80-20 dataset split (2400 images/hand sign in the training set and 600 images/hand sign in the validation set). My question is:
Should I randomly shuffle the images when creating the dataset? And Why?
PS: Based on my previous experience, it led to validation loss being lower than training loss and validation accuracy more than training accuracy.