Hello everyone,
I am currently doing my project on segmentation.
The problem with my dataset is that my training and validation dataset is actually slightly different to the actual raw dataset or test dataset.
So, I came up with a solution which is that since data augmentation tends to help regularization and prevents overfitting, I have given a random noise and rotation to my training dataset.
Hence, my training dataset is comprised with 1000 original training dataset + 1000 noisy training dataset while keeping the validation dataset unchanged.
However, my current model is around 94% with 0.87IoU in epoch 50 whereas my original model performance was 95% with 0.89IoU in epoch 50.
Can someone explain to me that whether my data augmentation technique has actually worsen my model’s performance and if not, should I wait for longer epochs to see the results?