Augmentation policy search (AutoAugment) in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main goal of AutoAugment in image classification?

AutoAugment is a technique used in computer vision. What is its primary purpose?

A To reduce the size of the training dataset by removing similar images

B To convert images into grayscale to reduce color complexity

C To speed up the training process by simplifying the model architecture

D To automatically find the best set of image transformations to improve model accuracy

💡 Hint

Think about how AutoAugment changes the input images before training.

AutoAugment searches for the best augmentation policies that transform images to help the model generalize better, improving accuracy.

❓ Model Choice

intermediate

Which model training step benefits most from AutoAugment policies?

During training an image classifier, which step is directly improved by applying AutoAugment policies?

A Data preprocessing by applying learned image transformations

B Model parameter initialization

C Loss function selection

D Final model evaluation on test data

💡 Hint

AutoAugment changes the input images before feeding them to the model.

AutoAugment policies are applied during data preprocessing to augment images, which helps the model learn better features.

❓ Predict Output

advanced

What is the output shape after applying AutoAugment to a batch of images?

Given a batch of 32 RGB images of size 224x224, what will be the shape of the batch after applying AutoAugment policies?

Computer Vision

import torch
from torchvision import transforms

to_pil = transforms.ToPILImage()
to_tens = transforms.ToTensor()
batch = torch.randn(32, 3, 224, 224)  # batch of images
policy = transforms.AutoAugment()
augmented_batch = torch.stack([to_tens(policy(to_pil(img))) for img in batch])
print(augmented_batch.shape)

A torch.Size([32, 3, 224, 224])

B torch.Size([3, 32, 224, 224])

C torch.Size([32, 1, 224, 224])

D torch.Size([32, 3, 112, 112])

💡 Hint

AutoAugment applies transformations but does not change image size or batch size.

AutoAugment applies image transformations like rotation or color changes but keeps the original image size and batch dimensions.

❓ Hyperparameter

advanced

Which hyperparameter in AutoAugment controls the strength of image transformations?

AutoAugment applies various transformations with different intensities. Which hyperparameter controls this intensity?

A Learning rate

B Magnitude

C Batch size

D Epoch count

💡 Hint

This parameter adjusts how strong each augmentation operation is.

Magnitude controls the strength or degree of each augmentation operation applied to images in AutoAugment.

❓ Metrics

expert

How does AutoAugment typically affect validation accuracy and training loss?

When using AutoAugment policies during training, what is the typical effect on validation accuracy and training loss?

A Validation accuracy decreases, training loss stays the same

B Validation accuracy decreases, training loss decreases significantly

C Validation accuracy increases, training loss may increase or stay similar

D Validation accuracy stays the same, training loss increases drastically

💡 Hint

Think about how augmentation affects model generalization and training difficulty.

AutoAugment improves generalization, so validation accuracy usually increases. Training loss may increase or stay similar because augmented data is harder to fit.