Image augmentation transforms in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of horizontal flip augmentation

What will be the shape and pixel value of the center pixel after applying a horizontal flip to this 3x3 grayscale image?

Computer Vision

import numpy as np
image = np.array([[10, 20, 30],
                  [40, 50, 60],
                  [70, 80, 90]])
flipped = np.fliplr(image)
center_pixel = flipped[1,1]
print(flipped.shape, center_pixel)

A (3, 3) 40

B (3, 3) 80

C (3, 3) 60

D (3, 3) 50

💡 Hint

Horizontal flip reverses columns but keeps rows the same.

The horizontal flip reverses each row. The center pixel stays at position (1,1) and keeps its original value 50.

❓ Model Choice

intermediate

Best augmentation for rotation invariance

Which image augmentation transform helps a model learn rotation invariance best?

A Random rotation by angles between -30 and 30 degrees

B Random brightness adjustment

C Random horizontal flip

D Random cropping

💡 Hint

Rotation invariance means the model should recognize objects even if rotated.

Random rotation teaches the model to recognize objects at different angles, improving rotation invariance.

❓ Hyperparameter

advanced

Choosing brightness adjustment range

You want to augment images by adjusting brightness. Which brightness factor range is most reasonable to avoid unnatural images?

A [0.0, 3.0]

B [0.1, 2.0]

C [0.5, 1.5]

D [1.0, 5.0]

💡 Hint

Brightness factor 1 means no change; too low or too high values can distort images.

A range from 0.5 to 1.5 adjusts brightness moderately, keeping images natural while augmenting.

❓ Metrics

advanced

Effect of augmentation on validation accuracy

After adding random cropping augmentation during training, which effect on validation accuracy is most likely?

A Validation accuracy increases due to better generalization

B Validation accuracy fluctuates randomly without pattern

C Validation accuracy stays the same

D Validation accuracy decreases due to overfitting

💡 Hint

Augmentation usually helps models generalize better to unseen data.

Random cropping creates varied training samples, helping the model generalize and improving validation accuracy.

🔧 Debug

expert

Bug in augmentation pipeline code

What error will this code raise when applying a vertical flip using torchvision transforms on a PIL image?

Computer Vision

from torchvision import transforms
from PIL import Image

image = Image.new('RGB', (100, 100), color='red')
transform = transforms.RandomVerticalFlip(p=1.0)
augmented = transform(image)
print(type(augmented))

A AttributeError: 'RandomVerticalFlip' object has no attribute 'forward'

B No error, prints <class 'PIL.Image.Image'>

C TypeError: 'RandomVerticalFlip' object is not callable

D RuntimeError: transform requires tensor input

💡 Hint

Check if RandomVerticalFlip works directly on PIL images.

RandomVerticalFlip from torchvision.transforms works on PIL images and returns a PIL image, so no error occurs.

Practice

1. What is the main purpose of image augmentation in training machine learning models?

easy

A. To reduce the size of the training dataset

B. To remove noise from images

C. To create more varied training images by modifying originals

D. To convert images to grayscale only

Solution

1. Step 1: Understand image augmentation

   Image augmentation means making small changes to original images to create new ones.

2. Step 2: Purpose in training

   This helps models see more variety and learn better, avoiding overfitting.

3. Final Answer:

   To create more varied training images by modifying originals -> Option C

4. Quick Check:

   Image augmentation = create varied images [OK]

Hint: Augmentation means changing images to get more training data [OK]

Common Mistakes:

• Thinking augmentation reduces dataset size
• Confusing augmentation with noise removal
• Assuming augmentation only changes color

2. Which of the following is the correct way to apply a horizontal flip using PyTorch's torchvision transforms?

easy

A. transforms.RandomHorizontalFlip(p=1.0)

B. transforms.HorizontalFlip()

C. transforms.FlipHorizontal()

D. transforms.RandomFlip(direction='horizontal')

Solution

1. Step 1: Recall torchvision syntax

   PyTorch uses transforms.RandomHorizontalFlip(p=probability) to flip images horizontally.

2. Step 2: Check options

   Only transforms.RandomHorizontalFlip(p=1.0) matches the correct function and parameter style.

3. Final Answer:

   transforms.RandomHorizontalFlip(p=1.0) -> Option A

4. Quick Check:

   Correct PyTorch flip = RandomHorizontalFlip [OK]

Hint: Look for 'RandomHorizontalFlip' with probability parameter [OK]

Common Mistakes:

• Using non-existent transform names
• Missing the probability parameter
• Confusing horizontal with vertical flip

3. Given the following code snippet using torchvision transforms, what is the output image size after applying the transforms?

transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.RandomCrop(100),
    transforms.ToTensor()
])

image = Image.open('sample.jpg')
output = transform(image)
print(output.shape)

medium

A. [3, 128, 128]

B. [3, 100, 100]

C. [1, 100, 100]

D. [3, 228, 228]

Solution

1. Step 1: Analyze each transform step

   First, image is resized to 128x128 pixels with 3 color channels (RGB). Then a random crop of size 100x100 is taken.

2. Step 2: Determine output tensor shape

   After cropping, the image size is 100x100 with 3 channels. ToTensor() converts it to a tensor with shape [channels, height, width] = [3, 100, 100].

3. Final Answer:

   [3, 100, 100] -> Option B

4. Quick Check:

   Resize then crop = final size 100x100 [OK]

Hint: Resize then crop means output size = crop size [OK]

Common Mistakes:

• Ignoring the crop step size
• Confusing channel dimension with batch size
• Assuming crop keeps original size

4. The following code is intended to rotate an image by 45 degrees using torchvision transforms, but it raises an error. What is the mistake?

transform = transforms.Compose([
    transforms.Rotate(45),
    transforms.ToTensor()
])

image = Image.open('sample.jpg')
output = transform(image)

medium

A. transforms.Rotate doesn't exist; should use transforms.functional.rotate or transforms.RandomRotation

B. The angle 45 must be in radians, not degrees

C. ToTensor must come before Rotate

D. Image.open returns a tensor, so transform fails

Solution

1. Step 1: Check torchvision transform names

   There is no transforms.Rotate class. Rotation is done with transforms.RandomRotation or using functional API.

2. Step 2: Identify correct usage

   To rotate by a fixed angle, use transforms.RandomRotation([45, 45]) or transforms.functional.rotate. The code as is will cause an AttributeError.

3. Final Answer:

   transforms.Rotate doesn't exist; should use transforms.functional.rotate or transforms.RandomRotation -> Option A

4. Quick Check:

   No transforms.Rotate in torchvision [OK]

Hint: Check transform names carefully; Rotate is not a direct class [OK]

Common Mistakes:

• Using non-existent transform classes
• Confusing degrees and radians
• Wrong order of transforms

5. You want to augment a dataset of images to improve model robustness. Which combination of transforms would best simulate real-world variations while keeping image size constant?

hard

A. transforms.RandomCrop(224), transforms.RandomRotation(180), transforms.Resize(128)

B. transforms.Resize(256), transforms.CenterCrop(224), transforms.RandomVerticalFlip() only

C. transforms.RandomRotation(90), transforms.RandomCrop(200), transforms.ToTensor()

D. transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.2, contrast=0.2)

Solution

1. Step 1: Understand augmentation goals

   We want to simulate real-world changes like size, flip, and color while keeping output size fixed.

2. Step 2: Evaluate options

   transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.2, contrast=0.2) resizes and crops randomly to 224x224, flips horizontally, and changes brightness/contrast, all common augmentations that keep size constant.

3. Step 3: Check other options

   transforms.Resize(256), transforms.CenterCrop(224), transforms.RandomVerticalFlip() only flips vertically and crops but lacks color changes. transforms.RandomRotation(90), transforms.RandomCrop(200), transforms.ToTensor() changes size unpredictably and transforms.RandomCrop(224), transforms.RandomRotation(180), transforms.Resize(128) resizes after cropping, changing size.

4. Final Answer:

   transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.2, contrast=0.2) -> Option D

5. Quick Check:

   Best augmentations keep size fixed and add variety [OK]

Hint: Pick transforms that keep size fixed and add flip + color changes [OK]

Common Mistakes:

• Choosing transforms that change image size unpredictably
• Ignoring color augmentations
• Using only vertical flips which are less common