Does each transformation is applied to each image suppose for image -> Adding brightness, contrast, flip, zoom or it will take randomly If it takes randomly then why padding is applied for all the images
@@tech_watt When using data augmentation in PyTorch, all transformations are not applied to a single image. Instead, each time an image is processed through the transformation pipeline during training, a subset of the defined transformations is randomly chosen and applied. This randomness ensures that each augmented image is slightly different from the original, introducing diversity into the training dataset and helping the model learn to generalize better to unseen data.
@@tech_watt Transformations like padding, resizing, and normalization are typically deterministic and applied uniformly to all images. These transformations ensure that all images in the dataset have the same dimensions and characteristics, which is often necessary for compatibility with neural network architectures.
when using data augmentation techniques, transformations are typically applied to each image randomly during training. This randomness is crucial for introducing diversity in the training data and enhancing the model's ability to generalize.
Good work man
Cool😁
Does each transformation is applied to each image suppose for image ->
Adding brightness, contrast, flip, zoom or it will take randomly
If it takes randomly then why padding is applied for all the images
It’s applied to each image sequentially
@@tech_watt When using data augmentation in PyTorch, all transformations are not applied to a single image. Instead, each time an image is processed through the transformation pipeline during training, a subset of the defined transformations is randomly chosen and applied. This randomness ensures that each augmented image is slightly different from the original, introducing diversity into the training dataset and helping the model learn to generalize better to unseen data.
@@tech_watt Transformations like padding, resizing, and normalization are typically deterministic and applied uniformly to all images. These transformations ensure that all images in the dataset have the same dimensions and characteristics, which is often necessary for compatibility with neural network architectures.
when using data augmentation techniques, transformations are typically applied to each image randomly during training. This randomness is crucial for introducing diversity in the training data and enhancing the model's ability to generalize.