Why architecture design impacts performance in Computer Vision - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does increasing model depth affect performance?

In convolutional neural networks, what is the most common effect of increasing the number of layers (depth) on model performance?

A It reduces performance because deeper models cannot learn complex features.

B It always improves performance by capturing more complex features without any drawbacks.

C It can improve performance by learning complex features but may cause vanishing gradients and overfitting.

D It has no effect on performance since only the number of neurons matters.

💡 Hint

Think about what happens when a model becomes very deep and how training might be affected.

Increasing depth allows the model to learn more complex features, but very deep networks can suffer from vanishing gradients and overfitting, which can hurt performance.

❓ Predict Output

intermediate

Output shape after convolution and pooling layers

Given the following PyTorch model snippet, what is the output shape after the last layer?

Computer Vision

import torch
import torch.nn as nn

class SimpleCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        return x

model = SimpleCNN()
input_tensor = torch.randn(1, 3, 64, 64)
output = model(input_tensor)
output.shape

A torch.Size([1, 32, 8, 8])

B torch.Size([1, 16, 8, 8])

C torch.Size([1, 16, 16, 16])

D torch.Size([1, 32, 16, 16])

💡 Hint

Calculate the size after each convolution and pooling step.

Input 64x64 → conv1 keeps size 64x64 → pool halves to 32x32 → conv2 keeps size 32x32 → pool halves to 16x16. Channels after conv2 are 32.

❓ Hyperparameter

advanced

Choosing kernel size impact on feature extraction

How does increasing the convolution kernel size from 3x3 to 7x7 typically affect a CNN's ability to extract features?

A Larger kernels capture more global features but increase parameters and risk overfitting.

B Larger kernels always improve performance without any drawbacks.

C Larger kernels reduce the receptive field and limit feature extraction.

D Kernel size does not affect feature extraction, only the number of filters matters.

💡 Hint

Think about how kernel size relates to the area of the image the filter sees.

Larger kernels see more of the image at once, capturing broader features but increasing model size and overfitting risk.

❓ Metrics

advanced

Interpreting validation accuracy drop with deeper architecture

A CNN model with 10 layers achieves 85% validation accuracy. Increasing to 50 layers drops validation accuracy to 70%. What is the most likely reason?

A The dataset is too large for the deeper model to learn.

B The deeper model suffers from vanishing gradients and overfitting.

C The deeper model is underfitting due to too few parameters.

D Validation accuracy always decreases with more layers.

💡 Hint

Consider training difficulties with very deep networks.

Very deep networks can have vanishing gradients making training hard and can overfit if not regularized, causing validation accuracy to drop.

🔧 Debug

expert

Identifying cause of exploding gradients in deep CNN

Given a deep CNN training with exploding gradients, which architectural choice is the most likely cause?

A Using ReLU activation without batch normalization in a very deep network.

B Using batch normalization and residual connections in the network.

C Using small kernel sizes like 3x3 in all convolution layers.

D Using dropout layers after every convolution.

💡 Hint

Think about what helps stabilize training in deep networks.

ReLU without batch normalization or residual connections can cause exploding gradients in deep networks due to unstable activations.