Data augmentation in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of image rotation augmentation

Given the following PyTorch code applying a rotation augmentation to a 3x3 grayscale image tensor, what is the output tensor after the transformation?

PyTorch

import torch
import torchvision.transforms as T

image = torch.tensor([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]], dtype=torch.float32).unsqueeze(0).unsqueeze(0)

transform = T.RandomRotation(degrees=(90, 90))

rotated_image = transform(image)

print(rotated_image.squeeze().int())

A

[[1 4 7]
 [2 5 8]
 [3 6 9]]

B

[[7 4 1]
 [8 5 2]
 [9 6 3]]

C

[[9 8 7]
 [6 5 4]
 [3 2 1]]

D

[[3 6 9]
 [2 5 8]
 [1 4 7]]

💡 Hint

Think about how a 90 degree rotation affects the position of pixels in a matrix.

A 90 degree counter-clockwise rotation moves the third column to the first row, the second column to the second row, and the first column to the third row. The tensor is rotated accordingly.

❓ Model Choice

intermediate

Best augmentation for small dataset with overfitting

You have a small image dataset and your model is overfitting. Which data augmentation technique is most effective to reduce overfitting?

A Reducing learning rate without augmentation

B Adding Gaussian noise to labels

C Increasing batch size without augmentation

D Random horizontal flip and random crop

💡 Hint

Think about augmentations that increase data diversity realistically.

Random horizontal flip and random crop create new variations of images, helping the model generalize better and reduce overfitting.

❓ Hyperparameter

advanced

Choosing rotation degree range for augmentation

You want to augment images by rotating them randomly. Which rotation degree range is most appropriate to preserve the original image semantics for handwritten digit recognition?

A Rotate between -10 and 10 degrees

B Rotate between -180 and 180 degrees

C Rotate between 90 and 270 degrees

D Rotate between 45 and 135 degrees

💡 Hint

Consider how much rotation changes the digit's appearance.

Small rotations like -10 to 10 degrees keep digits recognizable, while large rotations can make digits look like other numbers or meaningless.

❓ Metrics

advanced

Effect of augmentation on validation accuracy

You train two identical models on the same dataset. Model A uses data augmentation; Model B does not. After training, Model A has 85% validation accuracy, Model B has 80%. What does this difference most likely indicate?

A Augmentation caused overfitting by adding noise

B Model B trained longer, so it performed worse

C Augmentation improved generalization by increasing data diversity

D Validation data was augmented, causing bias

💡 Hint

Think about how augmentation affects model learning on unseen data.

Augmentation increases data variety, helping the model generalize better and improving validation accuracy.

🔧 Debug

expert

Debugging incorrect augmentation pipeline

You apply this PyTorch augmentation pipeline but the output images are always identical to the input. What is the bug? Code: import torchvision.transforms as T transform = T.Compose([ T.RandomHorizontalFlip(p=0), T.RandomRotation(degrees=0), T.ColorJitter(brightness=0) ]) augmented_image = transform(input_image)

A All augmentation parameters are set to zero probability or zero effect, so no change occurs

B The Compose function is missing a call to .to_tensor(), so no augmentation happens

C RandomHorizontalFlip requires p=1 to flip images always, p=0 disables it

D ColorJitter requires at least one non-zero parameter to change brightness

💡 Hint

Check the parameters controlling augmentation strength.

All augmentations have zero effect or zero probability, so the pipeline does not modify images.