Model comparison in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained model for image classification.

Computer Vision

from torchvision import models
model = models.[1](pretrained=True)

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

Atrain

Bfit

Cresnet18

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using training or evaluation functions instead of model names.

The resnet18 model is a common pre-trained model used for image classification in computer vision.

2fill in blank

medium

Complete the code to set the model to evaluation mode before making predictions.

Computer Vision

model.[1]()

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

Aeval

Bfit

Ctrain

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using train() instead of eval().

Calling eval() sets the model to evaluation mode, which disables dropout and batch normalization updates.

3fill in blank

hard

Fix the error in the code to compute accuracy from model outputs and labels.

Computer Vision

correct = (outputs.argmax(dim=1) [1] labels).sum().item()
accuracy = correct / labels.size(0)

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

A!=

B=

C<

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using assignment = instead of comparison ==.

The equality operator == compares predicted labels to true labels to count correct predictions.

4fill in blank

hard

Fill both blanks to create a dictionary of model names and their validation accuracies.

Computer Vision

results = { '[1]': val_acc1, '[2]': val_acc2 }

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

AResNet18

BVGG16

CAlexNet

DDenseNet

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect or misspelled model names.

Common model names like ResNet18 and VGG16 are used as keys to store their accuracies.

5fill in blank

hard

Fill all three blanks to complete the code that compares two models' accuracies and prints the better one.

Computer Vision

if results['[1]'] [2] results['[3]']:
    print('Better model: ResNet18')
else:
    print('Better model: VGG16')

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

AResNet18

B>

CVGG16

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping model names or using wrong comparison operators.

The code compares the accuracy of ResNet18 and VGG16 using the greater than operator to find the better model.

Practice

1. What is the main reason to compare different computer vision models on the same dataset?

easy

A. To find which model performs best for the task

B. To make the code run faster

C. To use more memory

D. To increase the dataset size

Solution

1. Step 1: Understand the purpose of model comparison

   Model comparison is done to evaluate which model gives better results on the same data.

2. Step 2: Identify the goal of comparing models

   The goal is to pick the best model for the task, not to affect code speed or data size.

3. Final Answer:

   To find which model performs best for the task -> Option A

4. Quick Check:

   Model comparison = find best model [OK]

Hint: Compare models by their results on the same data [OK]

Common Mistakes:

• Thinking comparison changes dataset size
• Confusing speed with model quality
• Assuming more memory means better model

2. Which of the following code snippets correctly compares two models' accuracy on the same test data in Python?

easy

A. acc1 = model1.fit(X_test, y_test)
acc2 = model2.fit(X_test, y_test)

B. acc1 = model1.evaluate(X_test, y_test)[1]
acc2 = model2.evaluate(X_test, y_test)[1]

C. acc1 = model1.predict(X_test)
acc2 = model2.predict(X_test)

D. acc1 = model1.score(X_train)
acc2 = model2.score(X_train)

Solution

1. Step 1: Identify correct method to get accuracy

   Using evaluate on test data returns loss and accuracy; index 1 is accuracy.

2. Step 2: Check other options for correctness

   fit trains, not evaluates; predict gives predictions, not accuracy; score needs both data and labels.

3. Final Answer:

   acc1 = model1.evaluate(X_test, y_test)[1] acc2 = model2.evaluate(X_test, y_test)[1] -> Option B

4. Quick Check:

   Use evaluate() for accuracy [OK]

Hint: Use evaluate() on test data to get accuracy [OK]

Common Mistakes:

• Using fit() instead of evaluate() for accuracy
• Using predict() output as accuracy
• Evaluating on training data instead of test data

3. Given the code below, what will be printed?

acc1 = 0.85
acc2 = 0.90
if acc1 > acc2:
    print('Model 1 is better')
else:
    print('Model 2 is better')

medium

A. Model 1 is better

B. Error: comparison not possible

C. Model 2 is better

D. No output

Solution

1. Step 1: Compare accuracy values

   acc1 is 0.85 and acc2 is 0.90, so acc1 < acc2.

2. Step 2: Follow the if-else logic

   Since acc1 > acc2 is false, the else block runs printing 'Model 2 is better'.

3. Final Answer:

   Model 2 is better -> Option C

4. Quick Check:

   0.85 < 0.90 so Model 2 wins [OK]

Hint: Compare accuracy numbers directly [OK]

Common Mistakes:

• Confusing greater than with less than
• Expecting error from simple comparison
• Ignoring else block output

4. You have two models but the code below gives an error. What is the problem?

acc1 = model1.evaluate(X_test, y_test)
acc2 = model2.evaluate(X_test, y_test)
if acc1 > acc2:
    print('Model 1 better')
else:
    print('Model 2 better')

medium

A. evaluate() returns a tuple, so direct comparison fails

B. X_test and y_test are swapped

C. Missing parentheses in print statements

D. Models are not trained yet

Solution

1. Step 1: Understand evaluate() output

   evaluate() returns a tuple (loss, accuracy), not a single number.

2. Step 2: Identify why comparison fails

   Comparing tuples directly with > causes error or unexpected behavior.

3. Final Answer:

   evaluate() returns a tuple, so direct comparison fails -> Option A

4. Quick Check:

   Compare accuracy values, not tuples [OK]

Hint: Extract accuracy from evaluate() tuple before comparing [OK]

Common Mistakes:

• Comparing full evaluate() output tuples
• Swapping test data inputs
• Assuming print syntax error

5. You want to compare three models on accuracy and speed. Which approach best helps you pick the best model?

hard

A. Use the model with smallest file size regardless of accuracy

B. Pick the model with highest accuracy only, ignoring speed

C. Choose the model with fastest training time only

D. Train all models, record accuracy and inference time, then choose the best trade-off

Solution

1. Step 1: Understand multiple criteria comparison

   Comparing models on both accuracy and speed requires measuring both metrics.

2. Step 2: Choose approach balancing accuracy and speed

   Recording accuracy and inference time helps find the best trade-off for your needs.

3. Final Answer:

   Train all models, record accuracy and inference time, then choose the best trade-off -> Option D

4. Quick Check:

   Balance accuracy and speed for best model [OK]

Hint: Measure both accuracy and speed, then compare [OK]

Common Mistakes:

• Ignoring speed when accuracy matters
• Choosing fastest training but poor accuracy
• Selecting model by size alone