Why augmentation multiplies training data in Computer Vision - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply a horizontal flip to an image using torchvision transforms.

Computer Vision

transform = torchvision.transforms.Compose([torchvision.transforms.[1]()])

Drag options to blanks, or click blank then click option'

ANormalize

BRandomHorizontalFlip

CToTensor

DResize

Attempts:

3 left

💡 Hint

Common Mistakes

Using Resize or Normalize instead of a flip transform.

The RandomHorizontalFlip transform flips images horizontally, creating new variations and thus multiplying training data.

2fill in blank

medium

Complete the code to create augmented data by rotating images randomly within 30 degrees.

Computer Vision

transform = torchvision.transforms.Compose([torchvision.transforms.RandomRotation([1])])

Drag options to blanks, or click blank then click option'

A30

B15

C45

D60

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 15 or 45 degrees which do not match the instruction.

RandomRotation with 30 degrees rotates images randomly between -30 and +30 degrees, increasing data variety.

3fill in blank

hard

Fix the error in the code to correctly apply color jitter augmentation.

Computer Vision

transform = torchvision.transforms.ColorJitter(brightness=[1])

Drag options to blanks, or click blank then click option'

A0.5

B'0.5'

CTrue

D[0.5]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string instead of a float for brightness.

The brightness parameter expects a float number, not a string or list. So 0.5 is correct.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each image filename to its augmented version using a horizontal flip.

Computer Vision

augmented_images = {filename: transform([1]) for filename, [2] in images.items()}

Drag options to blanks, or click blank then click option'

Aimage

Bimg

Cdata

Dpicture

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names for the blanks or incorrect variable names.

The dictionary comprehension uses img as the input to transform and img as the variable for image data in the loop.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that filters images with width greater than 100 and applies a rotation augmentation.

Computer Vision

augmented = {name: transform([1]) for name, [2] in dataset.items() if [3].width > 100}

Drag options to blanks, or click blank then click option'

Aimg

Bimage

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using inconsistent variable names between the loop and condition.

The comprehension uses img as input to transform, img as loop variable, and checks img.width for filtering.

Practice

1. Why does image augmentation multiply the training data in computer vision?

easy

A. Because it creates new images by changing existing ones

B. Because it deletes some images to reduce overfitting

C. Because it compresses images to save space

D. Because it labels images automatically

Solution

1. Step 1: Understand augmentation purpose

   Augmentation creates new images by applying changes like rotation or flipping to existing images.

2. Step 2: Connect augmentation to data size

   These new images increase the total number of training samples, effectively multiplying the dataset size.

3. Final Answer:

   Because it creates new images by changing existing ones -> Option A

4. Quick Check:

   Augmentation = new image versions [OK]

Hint: Augmentation makes new images from old ones [OK]

Common Mistakes:

• Thinking augmentation deletes images
• Confusing augmentation with compression
• Believing augmentation automatically labels data

2. Which of the following Python code snippets correctly applies horizontal flip augmentation using torchvision transforms?

easy

A. transform = transforms.FlipHorizontal()

B. transform = transforms.HorizontalFlip()

C. transform = transforms.RandomHorizontalFlip(p=1.0)

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

Solution

1. Step 1: Recall torchvision syntax

   In torchvision, horizontal flip is done with RandomHorizontalFlip and a probability parameter.

2. Step 2: Check options for correct class and parameters

   Only transform = transforms.RandomHorizontalFlip(p=1.0) uses the correct class name and parameter format.

3. Final Answer:

   transform = transforms.RandomHorizontalFlip(p=1.0) -> Option C

4. Quick Check:

   Correct torchvision flip syntax = transform = transforms.RandomHorizontalFlip(p=1.0) [OK]

Hint: Look for RandomHorizontalFlip with p parameter [OK]

Common Mistakes:

• Using incorrect class names like HorizontalFlip
• Missing the probability parameter p
• Assuming FlipHorizontal exists in torchvision

3. Given this code snippet applying augmentation, what is the total number of images after augmentation if the original dataset has 100 images and each image is augmented with 3 different transformations?

medium

A. 300

B. 400

C. 100

D. 600

Solution

1. Step 1: Calculate augmented images per original

   Each original image produces 3 new augmented images plus the original itself, totaling 4 images per original.

2. Step 2: Multiply by original dataset size

   100 original images * 4 images each = 400 images total after augmentation.

3. Final Answer:

   400 -> Option B

4. Quick Check:

   (3 augmentations + 1 original) * 100 = 400 [OK]

Hint: Add original + augmentations, then multiply by dataset size [OK]

Common Mistakes:

• Counting only augmented images, ignoring originals
• Multiplying by 3 instead of 4
• Adding instead of multiplying

4. Identify the error in this augmentation code snippet and select the fix:

from torchvision import transforms
transform = transforms.Compose([
    transforms.RandomRotation(30),
    transforms.RandomFlip(horizontal=true)
])

medium

A. Replace RandomFlip with RandomHorizontalFlip

B. Change RandomRotation(30) to RandomRotation(degrees=30)

C. Add p=0.5 to RandomFlip

D. No error, code is correct

Solution

1. Step 1: Check torchvision augmentation classes

   There is no RandomFlip class in torchvision; horizontal flip uses RandomHorizontalFlip.

2. Step 2: Identify correct fix

   Replacing RandomFlip with RandomHorizontalFlip fixes the error; other options are incorrect or unnecessary.

3. Final Answer:

   Replace RandomFlip with RandomHorizontalFlip -> Option A

4. Quick Check:

   Correct flip class = RandomHorizontalFlip [OK]

Hint: Use exact torchvision class names for flips [OK]

Common Mistakes:

• Assuming RandomFlip exists
• Changing rotation syntax unnecessarily
• Adding parameters to non-existent classes

5. You have a small dataset of 50 images. You want to multiply your training data by 5 times using augmentation. Which combination of augmentations is best to achieve this without losing original images?

hard

A. Apply 5 augmentations but discard original images

B. Only keep original images and train longer

C. Apply 2 augmentations and duplicate images twice

D. Apply 4 different augmentations per image plus keep original images

Solution

1. Step 1: Understand multiplication goal

   To multiply data by 5 times, total images after augmentation should be 50 * 5 = 250.

2. Step 2: Calculate images per original

   Keeping original plus 4 augmentations per image gives 5 images per original, totaling 250 images.

3. Step 3: Evaluate options

   Apply 4 different augmentations per image plus keep original images achieves the goal without losing original images; others either discard originals or don't reach 5x.

4. Final Answer:

   Apply 4 different augmentations per image plus keep original images -> Option D

5. Quick Check:

   Original + 4 augmentations = 5x data [OK]

Hint: Keep originals + 4 augmentations for 5x data [OK]

Common Mistakes:

• Discarding original images reduces data size
• Duplicating images without augmentation doesn't add variety
• Training longer without augmentation doesn't multiply data