EfficientNet scaling in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What does compound scaling in EfficientNet do?

EfficientNet uses a compound scaling method to scale up the model. What does this method do?

A It randomly changes the network architecture during training to find the best model.

B It only increases the depth of the network while keeping width and resolution constant.

C It scales the number of output classes to improve accuracy.

D It scales depth, width, and resolution of the network uniformly using fixed coefficients.

💡 Hint

Think about how EfficientNet balances different model dimensions together.

Compound scaling uniformly scales depth, width, and input resolution with fixed coefficients to balance model size and accuracy.

❓ Model Choice

intermediate

Choosing EfficientNet variant for limited GPU memory

You want to train an EfficientNet model on a GPU with limited memory. Which variant should you choose to balance accuracy and memory use?

A EfficientNet-B0

B EfficientNet-B7

C EfficientNet-B5

D EfficientNet-B3

💡 Hint

Smaller variants use less memory but have lower accuracy.

EfficientNet-B0 is the smallest variant, requiring less memory and suitable for limited GPU resources.

❓ Predict Output

advanced

Output shape after EfficientNet scaling

Given the following code snippet using PyTorch, what is the output shape of the tensor after scaling?

Computer Vision

import torch
from torchvision.models import efficientnet_b0

model = efficientnet_b0()
input_tensor = torch.randn(1, 3, 224, 224)
output = model.features(input_tensor)
print(output.shape)

A torch.Size([1, 1280, 14, 14])

B torch.Size([1, 1280, 7, 7])

C torch.Size([1, 320, 7, 7])

D torch.Size([1, 1000])

💡 Hint

Look at the output channels (1280) and spatial size after the feature extractor in EfficientNet-B0.

The features part of EfficientNet-B0 outputs a tensor with shape [batch, 1280, 7, 7] before the avgpool and classifier.

❓ Hyperparameter

advanced

Effect of increasing input resolution in EfficientNet scaling

What is the main effect of increasing the input image resolution in EfficientNet's compound scaling?

A It increases the spatial size of feature maps, improving fine detail capture but increasing computation.

B It reduces the number of layers in the network to speed up training.

C It decreases the number of channels in each layer to reduce overfitting.

D It changes the activation functions to improve non-linearity.

💡 Hint

Think about what happens when you feed larger images into a convolutional network.

Increasing input resolution increases feature map sizes, allowing the model to capture more detail but requiring more computation.

❓ Metrics

expert

Comparing accuracy and FLOPS of EfficientNet variants

Which EfficientNet variant has approximately 19 billion FLOPS and achieves around 84.0% top-1 accuracy on ImageNet?

A EfficientNet-B7

B EfficientNet-B5

C EfficientNet-B6

D EfficientNet-B4

💡 Hint

Recall the FLOPS and accuracy increase with variant number.

EfficientNet-B6 has about 19B FLOPS and achieves ~84.0% top-1 accuracy on ImageNet.

Practice

1. What is the main idea behind EfficientNet scaling in computer vision models?

easy

A. It uses only higher image resolution without changing the model.

B. It only increases the number of layers to improve accuracy.

C. It reduces model size by removing layers randomly.

D. It scales depth, width, and resolution together using fixed constants.

Solution

1. Step 1: Understand EfficientNet scaling components

   EfficientNet scales three model dimensions: depth (layers), width (channels), and input resolution together.

2. Step 2: Recognize the use of constants

   It uses constants alpha, beta, gamma with a scaling factor phi to balance these dimensions.

3. Final Answer:

   It scales depth, width, and resolution together using fixed constants. -> Option D

4. Quick Check:

   EfficientNet scales depth, width, resolution together [OK]

Hint: Remember: EfficientNet scales depth, width, and resolution together [OK]

Common Mistakes:

• Thinking it only increases layers
• Assuming it changes only resolution
• Believing it randomly removes layers

2. Which formula correctly represents the compound scaling method used in EfficientNet for depth (d), width (w), and resolution (r)?

easy

A. d = phi * alpha, w = phi * beta, r = phi * gamma

B. d = alpha + phi, w = beta + phi, r = gamma + phi

C. d = alpha^phi, w = beta^phi, r = gamma^phi

D. d = alpha / phi, w = beta / phi, r = gamma / phi

Solution

1. Step 1: Recall EfficientNet scaling formula

   EfficientNet uses exponential scaling: depth = alpha^phi, width = beta^phi, resolution = gamma^phi.

2. Step 2: Compare options with formula

   Only d = alpha^phi, w = beta^phi, r = gamma^phi matches the exponential form with constants raised to the power phi.

3. Final Answer:

   d = alpha^phi, w = beta^phi, r = gamma^phi -> Option C

4. Quick Check:

   Uses exponentiation alpha^phi [OK]

Hint: Look for exponential scaling with phi as power [OK]

Common Mistakes:

• Using multiplication instead of exponentiation
• Adding phi instead of exponentiating
• Dividing constants by phi

3. Given alpha=1.2, beta=1.1, gamma=1.15, and phi=2, what is the scaled depth (d) using EfficientNet scaling?

medium

A. 1.2^2 = 1.44

B. 1.2 * 2 = 2.4

C. 1.2 + 2 = 3.2

D. 2 / 1.2 = 1.67

Solution

1. Step 1: Apply the formula for depth scaling

   Depth d = alpha^phi = 1.2^2 = 1.44.

2. Step 2: Calculate the value

   1.2 squared equals 1.44, matching 1.2^2 = 1.44.

3. Final Answer:

   1.44 -> Option A

4. Quick Check:

   1.2^2 = 1.44 [OK]

Hint: Raise alpha to the power phi for depth [OK]

Common Mistakes:

• Multiplying alpha by phi instead of exponentiating
• Adding phi to alpha
• Dividing phi by alpha

4. Identify the error in this Python code snippet for EfficientNet scaling:

alpha, beta, gamma, phi = 1.2, 1.1, 1.15, 2
depth = alpha * phi
width = beta ** phi
resolution = gamma ** phi

medium

A. Depth should be alpha ** phi, not alpha * phi

B. Width should be beta * phi, not beta ** phi

C. Resolution should be gamma * phi, not gamma ** phi

D. No error, the code is correct

Solution

1. Step 1: Review EfficientNet scaling formula

   Depth should be scaled as alpha raised to phi (alpha ** phi), not multiplied.

2. Step 2: Check code for depth calculation

   Code uses alpha * phi which is incorrect; width and resolution use exponentiation correctly.

3. Final Answer:

   Depth should be alpha ** phi, not alpha * phi -> Option A

4. Quick Check:

   Depth uses exponentiation (**), not multiplication (*) [OK]

Hint: Depth uses exponentiation, not multiplication [OK]

Common Mistakes:

• Confusing multiplication with exponentiation
• Assuming width or resolution calculations are wrong
• Thinking code has no errors

5. You want to scale an EfficientNet model with phi=3, alpha=1.2, beta=1.1, gamma=1.15. Which of these sets of scaled values (depth, width, resolution) is closest to the correct scaling?

hard

A. (1.2+3, 1.1+3, 1.15+3) = (4.2, 4.1, 4.15)

B. (1.2^3, 1.1^3, 1.15^3) ≈ (1.73, 1.33, 1.52)

C. (3*1.2, 3*1.1, 3*1.15) = (3.6, 3.3, 3.45)

D. (3/1.2, 3/1.1, 3/1.15) ≈ (2.5, 2.73, 2.61)

Solution

1. Step 1: Apply compound scaling formula

   Scale each dimension by raising constants to the power phi: depth = 1.2^3, width = 1.1^3, resolution = 1.15^3.

2. Step 2: Calculate approximate values

   1.2^3 ≈ 1.73, 1.1^3 ≈ 1.33, 1.15^3 ≈ 1.52, matching (1.2^3, 1.1^3, 1.15^3) ≈ (1.73, 1.33, 1.52).

3. Final Answer:

   (1.73, 1.33, 1.52) -> Option B

4. Quick Check:

   1.2^3 ≈ 1.73, 1.1^3 ≈ 1.33, 1.15^3 ≈ 1.52 [OK]

Hint: Use powers, not multiplication or addition for scaling [OK]

Common Mistakes:

• Multiplying constants by phi instead of exponentiating
• Adding phi to constants
• Dividing phi by constants