FCN (Fully Convolutional Network) in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main advantage of using an FCN for image segmentation?

Fully Convolutional Networks (FCNs) are popular for image segmentation tasks. What is the key advantage of using an FCN compared to traditional CNNs with fully connected layers?

A FCNs can take input images of any size and produce correspondingly sized output segmentation maps.

B FCNs require less training data because they use fully connected layers.

C FCNs always run faster because they use fewer convolutional layers.

D FCNs use fully connected layers to improve classification accuracy.

💡 Hint

Think about how FCNs handle input size and output size compared to traditional CNNs.

FCNs replace fully connected layers with convolutional layers, allowing them to accept images of any size and output segmentation maps of corresponding spatial dimensions. This flexibility is a key advantage for dense prediction tasks like segmentation.

❓ Predict Output

intermediate

Output shape of an FCN layer

Consider an input tensor of shape (batch_size=1, channels=3, height=64, width=64) passed through a convolutional layer in an FCN with 16 filters, kernel size 3, stride 1, and padding 1. What will be the output shape?

Computer Vision

import torch
import torch.nn as nn

input_tensor = torch.randn(1, 3, 64, 64)
conv_layer = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
output = conv_layer(input_tensor)
output.shape

A torch.Size([1, 16, 66, 66])

B torch.Size([1, 16, 62, 62])

C torch.Size([1, 3, 64, 64])

D torch.Size([1, 16, 64, 64])

💡 Hint

Recall how padding affects output size in convolution layers.

With kernel size 3, stride 1, and padding 1, the output height and width remain the same as input (64x64). The number of output channels equals the number of filters (16).

❓ Model Choice

advanced

Choosing the correct FCN architecture for semantic segmentation

You want to build an FCN for semantic segmentation on a dataset with 10 classes. Which architecture choice is best to produce pixel-wise class predictions?

A Use convolutional layers followed by fully connected layers to output a single class label per image.

B Use convolutional layers only, ending with a 1x1 convolution layer with 10 filters to output class scores per pixel.

C Use fully connected layers only to output a 10-dimensional vector for each pixel.

D Use convolutional layers with max pooling but no upsampling layers.

💡 Hint

Think about how to get class scores for each pixel in the input image.

FCNs use convolutional layers to keep spatial information. A 1x1 convolution with 10 filters outputs class scores for each pixel, enabling pixel-wise classification for segmentation.

❓ Hyperparameter

advanced

Effect of upsampling method in FCN output quality

In an FCN, which upsampling method is most likely to produce smoother and more accurate segmentation maps?

A Bilinear interpolation upsampling

B Nearest neighbor upsampling

C Max pooling

D Strided convolution downsampling

💡 Hint

Consider which method interpolates pixel values smoothly.

Bilinear interpolation uses weighted averages of nearby pixels, producing smoother upsampled images than nearest neighbor, which simply copies pixels.

❓ Metrics

expert

Choosing the best metric for evaluating FCN segmentation output

You trained an FCN for semantic segmentation. Which metric best measures how well the predicted segmentation matches the ground truth at the pixel level?

A Accuracy (percentage of correctly classified pixels)

B Mean Squared Error (MSE) between predicted and true pixel values

C Intersection over Union (IoU) per class averaged over all classes

D Precision of the predicted class labels ignoring spatial location

💡 Hint

Think about a metric that balances false positives and false negatives per class.

IoU measures overlap between predicted and true segmentation masks per class, balancing false positives and false negatives, making it ideal for segmentation evaluation.