R-CNN family overview in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the main difference between R-CNN and Fast R-CNN

What is the key difference between the original R-CNN and Fast R-CNN in how they process images for object detection?

A R-CNN uses a single CNN for the whole image, while Fast R-CNN uses multiple CNNs for each region proposal.

B Fast R-CNN does not use region proposals, unlike R-CNN which relies on them.

C Fast R-CNN processes the entire image with a single CNN forward pass, while R-CNN processes each region proposal separately.

D R-CNN uses a fully convolutional network, while Fast R-CNN uses a fully connected network.

💡 Hint

Think about how many times the CNN runs on the image in each method.

R-CNN runs the CNN separately on each region proposal, which is slow. Fast R-CNN runs the CNN once on the whole image and then extracts features for each region, making it faster.

❓ Model Choice

intermediate

Choosing the right R-CNN variant for real-time detection

You want to build a real-time object detection system on a mobile device with limited computing power. Which R-CNN family model is the best choice?

A Faster R-CNN

B Fast R-CNN

C R-CNN

D Mask R-CNN

💡 Hint

Consider which model introduced a faster region proposal method.

Faster R-CNN introduced a Region Proposal Network (RPN) that speeds up detection, making it more suitable for real-time applications compared to R-CNN and Fast R-CNN.

❓ Predict Output

advanced

Output shape of feature map in Faster R-CNN backbone

Given an input image of size 224x224x3 passed through a backbone CNN that reduces spatial dimensions by a factor of 16, what is the shape of the output feature map?

Computer Vision

import torch
import torch.nn as nn

input_tensor = torch.randn(1, 3, 224, 224)

class SimpleBackbone(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(3, 256, kernel_size=3, stride=2, padding=1)
        self.pool = nn.MaxPool2d(2)
    def forward(self, x):
        x = self.conv(x)  # halves size
        x = self.pool(x)  # halves size again
        x = self.pool(x)  # halves size again
        return x

model = SimpleBackbone()
output = model(input_tensor)
output.shape

A torch.Size([1, 256, 56, 56])

B torch.Size([1, 256, 7, 7])

C torch.Size([1, 256, 14, 14])

D torch.Size([1, 256, 28, 28])

💡 Hint

Each stride 2 or pooling halves the spatial size.

Starting from 224x224, conv with stride 2 halves to 112x112, then first pool halves to 56x56, second pool halves to 28x28. So output shape is (1, 256, 28, 28).

But the code applies pooling twice after conv, so final size is 28x28.

❓ Metrics

advanced

Evaluating Mask R-CNN segmentation output

Mask R-CNN outputs a mask for each detected object. Which metric best measures how well the predicted mask matches the true mask?

A Intersection over Union (IoU)

B Accuracy

C Mean Squared Error (MSE)

D Precision

💡 Hint

Think about overlap between predicted and true masks.

IoU measures the overlap between predicted and true masks, making it the best metric for segmentation quality.

🔧 Debug

expert

Debugging slow training in Faster R-CNN

You notice that training Faster R-CNN on your dataset is very slow. You suspect the bottleneck is in the region proposal network (RPN). Which of the following changes will most likely speed up training without hurting accuracy?

A Increase the number of anchors per location in the RPN.

B Reduce the input image size to a smaller resolution.

C Remove the RPN and use external region proposals.

D Increase the number of training epochs.

💡 Hint

Think about how input size affects computation.

Reducing input image size reduces computation in the backbone and RPN, speeding up training without necessarily hurting accuracy much.

Increasing anchors or epochs increases computation. Removing RPN loses the speed advantage of Faster R-CNN.

Practice

1. What is the main purpose of the R-CNN family of models in computer vision?

easy

A. To enhance image resolution

B. To generate captions for images

C. To detect edges in images

D. To find and classify objects in images by proposing regions

Solution

1. Step 1: Understand R-CNN model goal

   The R-CNN family is designed to locate objects by proposing regions and then classify them.

2. Step 2: Compare options with model purpose

   Only To find and classify objects in images by proposing regions matches the goal of finding and classifying objects using region proposals.

3. Final Answer:

   To find and classify objects in images by proposing regions -> Option D

4. Quick Check:

   R-CNN purpose = find and classify objects [OK]

Hint: R-CNN = Region proposals + Classification [OK]

Common Mistakes:

• Confusing R-CNN with image captioning models
• Thinking R-CNN enhances image quality
• Mixing edge detection with object detection

2. Which of the following is the correct sequence of steps in the original R-CNN pipeline?

easy

A. Classify entire image, extract regions, refine bounding boxes

B. Refine bounding boxes, extract regions, classify regions

C. Extract regions, classify regions, refine bounding boxes

D. Extract regions, refine bounding boxes, classify entire image

Solution

1. Step 1: Recall R-CNN pipeline steps

   R-CNN first extracts region proposals, then classifies each region, and finally refines bounding boxes.

2. Step 2: Match steps with options

   Extract regions, classify regions, refine bounding boxes correctly lists the steps in order: extract regions, classify, then refine boxes.

3. Final Answer:

   Extract regions, classify regions, refine bounding boxes -> Option C

4. Quick Check:

   R-CNN steps = extract, classify, refine [OK]

Hint: Remember: Propose regions first, then classify [OK]

Common Mistakes:

• Classifying before extracting regions
• Refining boxes before classification
• Mixing order of steps

3. Given the following code snippet for Faster R-CNN, what is the output shape of the feature map if the input image is 224x224 and the backbone reduces size by a factor of 16?

input_image = torch.randn(1, 3, 224, 224)
features = backbone(input_image)
print(features.shape)

medium

A. [1, C, 14, 14]

B. [1, C, 7, 7]

C. [1, C, 28, 28]

D. [1, C, 224, 224]

Solution

1. Step 1: Calculate spatial size after backbone

   The backbone reduces the input size by factor 16, so 224 / 16 = 14.

2. Step 2: Determine output shape format

   Output shape is batch size 1, channels C (depends on backbone), height 14, width 14.

3. Final Answer:

   [1, C, 14, 14] -> Option A

4. Quick Check:

   224/16 = 14, so output height and width = 14 [OK]

Hint: Divide input size by backbone stride for output size [OK]

Common Mistakes:

• Using 7 instead of 14 for output size
• Confusing channels with spatial dimensions
• Assuming no size reduction

4. You have a Mask R-CNN model but it is not producing masks for detected objects. Which of the following is the most likely cause?

medium

A. The backbone network is too shallow

B. The model was trained without the mask branch enabled

C. The input images are grayscale instead of RGB

D. The bounding box proposals are too large

Solution

1. Step 1: Understand Mask R-CNN mask output

   Mask R-CNN produces masks only if the mask branch is included and trained.

2. Step 2: Analyze options for mask absence

   If the mask branch is disabled during training, no masks will be produced, making The model was trained without the mask branch enabled correct.

3. Final Answer:

   The model was trained without the mask branch enabled -> Option B

4. Quick Check:

   Mask branch enabled = masks output [OK]

Hint: Masks need mask branch enabled and trained [OK]

Common Mistakes:

• Blaming backbone depth for missing masks
• Assuming grayscale input stops mask output
• Thinking bounding box size affects mask generation

5. You want to improve the speed of an R-CNN model without losing much accuracy. Which approach from the R-CNN family is best suited for this?

hard

A. Use Faster R-CNN with a Region Proposal Network (RPN)

B. Use Fast R-CNN with selective search region proposals

C. Use Mask R-CNN with added mask prediction

D. Use the original R-CNN with SVM classifiers

Solution

1. Step 1: Identify speed improvements in R-CNN family

   Faster R-CNN improves speed by integrating a Region Proposal Network to generate proposals efficiently.

2. Step 2: Compare options for speed and accuracy

   Fast R-CNN still uses slow selective search; Mask R-CNN adds mask overhead; original R-CNN is slow. So Faster R-CNN is best.

3. Final Answer:

   Use Faster R-CNN with a Region Proposal Network (RPN) -> Option A

4. Quick Check:

   Faster R-CNN = speed + accuracy via RPN [OK]

Hint: Faster R-CNN uses RPN for faster proposals [OK]

Common Mistakes:

• Choosing Fast R-CNN which still uses slow proposals
• Adding mask prediction which slows model
• Using original R-CNN which is slow