Hello everyone,
I had a small question with respect to data augmentation and dataloader. In order to use use data augmentation in addition to the unaltered set of original images for training, I am using ConcatDataset in Data Loader, which consists of two data_transform operations on same dataset. (data_transforms_A contains many augmentation techniques while data_transforms contains only scaling to leave original image intact.) Is this the right approach? Also in each batch, lets say of size 64,what is the distribution of amount of images fetched from each dataset?
Please find below the code i am currently using,
train_loader = torch.utils.data.DataLoader(
torch.utils.data.ConcatDataset([datasets.ImageFolder(args.data + ‘/train_images’,
transform=data_transforms_A),
datasets.ImageFolder(args.data + ‘/train_images’,
transform=data_transforms)]), batch_size=args.batch_size, shuffle=True, num_workers=1)
Thank you.