Part-1: Dataloaders for different scenarios of data augmentation in PyTorch

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This video and upcoming videos will show how we can create data loaders for different scenarios of data augmentation in PyTorch.
Scenario -1: one augmented image from one input image.
Scenario-2: Using augmentation for increasing dataset, multiple augmented images from one input image.
Scenario-3: Using augmentation for increasing dataset of selective classes.

Links:

#DeepLearning #PyTorch #Dataloaders in Pytorch #Data Augmentation in Pytorch #DataAugmentation #Augmentation for increasing dataset size
#Custom Collate Function in PyTorch

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Great stuff!!! Loving it. Is there any video in reducing the over fitting or improve the training performance on this topic?

Superrxeddy

Thanks for the video! I have a question, when doing the training loop, should I use a train data loader with no augmentation and another one with augmentation to double the data available? thnks

leo.y.comprendo

Part-1: Dataloaders for different scenarios of data augmentation in PyTorch

Part-1: Dataloaders for different scenarios of data augmentation in PyTorch

Part-2: Dataloaders for different scenarios of data augmentation in PyTorch

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