Why CNNs dominate image classification in Computer Vision - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why do CNNs perform better than fully connected networks on images?

Which reason best explains why Convolutional Neural Networks (CNNs) usually outperform fully connected networks on image classification tasks?

A CNNs use local connections and shared weights, capturing spatial patterns efficiently.

B Fully connected networks have fewer parameters, so they underfit images.

C CNNs ignore pixel relationships, making them faster but less accurate.

D Fully connected networks use convolution layers that reduce image size too much.

💡 Hint

Think about how images have nearby pixels related to each other.

CNNs use filters that slide over small regions of the image, sharing weights to detect features like edges and shapes. This local connectivity and weight sharing reduce parameters and capture spatial patterns better than fully connected layers.

❓ Predict Output

intermediate

Output shape after convolution layer

Given a grayscale image of size 28x28 and a convolution layer with 6 filters of size 5x5, stride 1, and no padding, what is the output shape?

Computer Vision

import torch
import torch.nn as nn

input_tensor = torch.randn(1, 1, 28, 28)  # batch=1, channels=1, height=28, width=28
conv = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=0)
output = conv(input_tensor)
print(output.shape)

A torch.Size([6, 1, 28, 28])

B torch.Size([1, 6, 28, 28])

C torch.Size([1, 6, 24, 24])

D torch.Size([1, 1, 24, 24])

💡 Hint

Output size = (Input size - Kernel size) / Stride + 1

Output height and width = (28 - 5)/1 + 1 = 24. Number of output channels = 6 filters. Batch size stays 1.

❓ Model Choice

advanced

Choosing CNN architecture for complex image classification

You want to classify high-resolution images with many classes. Which CNN architecture choice is best to improve accuracy while managing training time?

A Use a CNN with residual connections (ResNet) to allow deeper networks without vanishing gradients.

B Use a very deep CNN with many layers and no pooling to keep all details.

C Use a shallow CNN with large fully connected layers at the end to capture complexity.

D Use only convolution layers with large kernel sizes (e.g., 11x11) to cover more area.

💡 Hint

Think about how very deep networks can be trained effectively.

Residual connections help training very deep CNNs by allowing gradients to flow better, improving accuracy without excessive training problems. Very deep networks without residuals often suffer from vanishing gradients.

❓ Hyperparameter

advanced

Effect of kernel size on CNN feature detection

How does increasing the convolution kernel size from 3x3 to 7x7 generally affect a CNN's ability to detect features in images?

A Larger kernels always improve accuracy without drawbacks.

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

C Larger kernels decrease the number of parameters and speed up training.

D Larger kernels reduce the receptive field and miss important details.

💡 Hint

Consider the trade-off between detail and context in feature detection.

Larger kernels cover more area, capturing broader features but increasing parameters and computation, which can lead to overfitting if not managed well.

❓ Metrics

expert

Interpreting CNN training metrics for image classification

During CNN training on image classification, you observe training accuracy steadily increasing but validation accuracy plateaus and then decreases. What does this indicate?

A The dataset is too small to train any model.

B The model is underfitting and needs more layers.

C The learning rate is too low, causing slow training.

D The model is overfitting the training data and not generalizing well.

💡 Hint

Think about what it means when training improves but validation worsens.

When training accuracy improves but validation accuracy drops, the model memorizes training data patterns but fails to generalize to new data, a sign of overfitting.