Question:I was training a custom model in pytorch and the dataset was very uneven. As in there are 10 classes for which some class have only 800 images while some have 4000 images. I found that image augmentation was a solution for my problem to avoid overfitting. But i got confused in between while implementing, the below codes were used to alter the features of the images
Answer:It looks like you are using
online augmentations, If you like to use
offlineplease do a pre-processing step that saves the images and then use them in the training step
Please make sure you understand the difference between
Offline or pre-processing Augmentation
To increase the size of the data set, enhancement is applied as a pre-processing step. Usually, we do this when we want to expand a small training data set. When applying to larger data sets, we have to consider disk space
Online or real-time Augmentation
The augmentation is being applied in real-time through random augmentations. Since the augmented images do not need to be saved on the disk, this method is usually applied to large data sets. At each epoch, the online augmentation model will see a different image.
If you have better answer, please add a comment about this, thank you!