Faster R-CNN usage in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output shape of Faster R-CNN model predictions

Given the following PyTorch code snippet using a pretrained Faster R-CNN model, what is the type and structure of the output after running the model on a batch of images?

PyTorch

import torch
from torchvision.models.detection import fasterrcnn_resnet50_fpn

model = fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()

images = [torch.rand(3, 300, 400), torch.rand(3, 500, 600)]
outputs = model(images)

print(type(outputs))
print(len(outputs))
print(outputs[0].keys())

A outputs is a list of length 2 (one per image), each element is a dict with keys 'boxes', 'labels', and 'scores'.

B outputs is a tensor of shape (2, 100, 6) containing bounding boxes and labels for all images.

C outputs is a dict with keys 'boxes', 'labels', and 'scores' containing tensors for all images combined.

D outputs is a list of length 1 containing a dict with keys 'boxes', 'labels', and 'scores'.

💡 Hint

Think about how torchvision detection models return predictions for each image separately.

The Faster R-CNN model returns a list of dictionaries, one per input image. Each dictionary contains keys 'boxes', 'labels', and 'scores' with tensors describing detected objects.

❓ Model Choice

intermediate

Choosing the right backbone for Faster R-CNN

You want to detect small objects in high-resolution images using Faster R-CNN. Which backbone architecture is generally better for capturing fine details?

A ResNet-18 without FPN

B ResNet-50 with Feature Pyramid Network (FPN)

C MobileNetV2 without FPN

D VGG16 without FPN

💡 Hint

Feature Pyramid Network helps detect objects at multiple scales.

ResNet-50 with FPN provides strong multi-scale feature maps, which help detect small objects better than shallower or backbone without FPN.

❓ Hyperparameter

advanced

Adjusting Faster R-CNN training for fewer false positives

During training Faster R-CNN, you notice many false positive detections. Which hyperparameter adjustment is most likely to reduce false positives?

A Increase the batch size to 64

B Decrease the number of proposals generated by RPN

C Increase the score threshold for predicted boxes during inference

D Lower the learning rate drastically

💡 Hint

False positives can be filtered by confidence score thresholds.

Increasing the score threshold during inference filters out low-confidence boxes, reducing false positives.

🔧 Debug

advanced

Identifying error in Faster R-CNN input format

You run this code and get a runtime error: import torch from torchvision.models.detection import fasterrcnn_resnet50_fpn model = fasterrcnn_resnet50_fpn(pretrained=True) model.eval() image = torch.rand(3, 224, 224) output = model(image) What is the cause of the error?

A The pretrained model cannot run in eval mode.

B The image tensor must have shape (224, 224, 3) instead of (3, 224, 224).

C The model requires images to be normalized before input.

D The model expects a list of images, not a single tensor.

💡 Hint

Check the expected input type for torchvision detection models.

Faster R-CNN expects a list of tensors, each representing an image. Passing a single tensor causes a runtime error.

❓ Metrics

expert

Evaluating Faster R-CNN with mAP metric

You trained a Faster R-CNN model and want to evaluate its performance using mean Average Precision (mAP) at IoU=0.5. Which statement about mAP is correct?

A mAP@0.5 measures the average precision of detected boxes that have Intersection over Union (IoU) greater than 0.5 with ground truth boxes.

B mAP@0.5 is the average recall of all predicted boxes with confidence scores above 0.5.

C mAP@0.5 counts all predicted boxes regardless of IoU and averages their confidence scores.

D mAP@0.5 is the accuracy of classifying images as containing objects or not.

💡 Hint

mAP involves precision and IoU thresholds for matching predictions to ground truth.

mAP@0.5 calculates average precision considering only predicted boxes with IoU > 0.5 to ground truth, reflecting detection quality.

Practice

1. What is the primary purpose of the Faster R-CNN model in PyTorch?

easy

A. To enhance image resolution

B. To generate captions for images

C. To detect objects and their locations in images

D. To classify images into categories without localization

Solution

1. Step 1: Understand Faster R-CNN's role

   Faster R-CNN is designed to find objects and their bounding boxes in images.

2. Step 2: Compare with other tasks

   Unlike captioning or classification without localization, Faster R-CNN outputs object locations and labels.

3. Final Answer:

   To detect objects and their locations in images -> Option C

4. Quick Check:

   Object detection = Faster R-CNN purpose [OK]

Hint: Faster R-CNN = object detection with bounding boxes [OK]

Common Mistakes:

• Confusing detection with classification only
• Thinking it generates image captions
• Assuming it enhances image quality

2. Which of the following is the correct way to load a pretrained Faster R-CNN model in PyTorch?

easy

A. model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights='DEFAULT')

B. model = torchvision.models.fasterrcnn_resnet50_fpn(pretrained=False)

C. model = torchvision.models.fasterrcnn_resnet50_fpn(pretrained=True)

D. model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=None)

Solution

1. Step 1: Check PyTorch's current API for pretrained models

   PyTorch now uses the 'weights' argument with 'DEFAULT' to load pretrained weights.

2. Step 2: Identify correct syntax

   model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights='DEFAULT') uses weights='DEFAULT', which is the recommended way to load pretrained Faster R-CNN.

3. Final Answer:

   model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights='DEFAULT') -> Option A

4. Quick Check:

   Use weights='DEFAULT' for pretrained models [OK]

Hint: Use weights='DEFAULT' to load pretrained Faster R-CNN [OK]

Common Mistakes:

• Using deprecated pretrained=True argument
• Setting weights=None disables pretrained weights
• Confusing model namespaces in torchvision

3. Given the following code snippet, what is the output type of outputs after the model prediction?

import torchvision
import torch
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights='DEFAULT')
model.eval()
image = torch.rand(3, 300, 400)  # Random image tensor
outputs = model([image])

medium

A. A list of dictionaries with keys 'boxes', 'labels', and 'scores'

B. A single tensor with class probabilities

C. A dictionary with keys 'boxes' and 'labels' only

D. A tensor of shape (3, 300, 400) identical to input

Solution

1. Step 1: Understand Faster R-CNN output format

   Faster R-CNN returns a list of dictionaries, one per input image, each containing 'boxes', 'labels', and 'scores'.

2. Step 2: Analyze the code snippet

   Since input is a list with one image, output is a list with one dictionary containing detection results.

3. Final Answer:

   A list of dictionaries with keys 'boxes', 'labels', and 'scores' -> Option A

4. Quick Check:

   Output = list of dicts with boxes, labels, scores [OK]

Hint: Faster R-CNN outputs list of dicts with boxes, labels, scores [OK]

Common Mistakes:

• Expecting a single tensor output
• Missing 'scores' key in output
• Confusing input and output tensor shapes

4. Identify the error in the following code snippet for using Faster R-CNN for prediction:

import torchvision
import torch
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights='DEFAULT')
model.eval()
image = torch.rand(3, 224, 224)
outputs = model(image)

medium

A. Model.eval() should be called after prediction

B. Input image should be a list of tensors, not a single tensor

C. Weights argument 'DEFAULT' is invalid

D. Image tensor shape should be (224, 224, 3) instead of (3, 224, 224)

Solution

1. Step 1: Check input format for Faster R-CNN

   Faster R-CNN expects a list of image tensors, even if only one image is passed.

2. Step 2: Identify the error in the code

   The code passes a single tensor instead of a list, causing a runtime error.

3. Final Answer:

   Input image should be a list of tensors, not a single tensor -> Option B

4. Quick Check:

   Input = list of tensors for Faster R-CNN [OK]

Hint: Wrap input image in a list before passing to model [OK]

Common Mistakes:

• Passing single tensor instead of list
• Calling eval() after prediction
• Confusing channel order in tensor shape

5. You want to fine-tune a pretrained Faster R-CNN model on a custom dataset with 5 classes (including background). Which step is necessary before training?

hard

A. Change the input image size to (5, 224, 224)

B. Freeze all layers except the backbone

C. Set model.train() before loading pretrained weights

D. Replace the model's box predictor head with a new one having 5 output classes

Solution

1. Step 1: Understand model adaptation for new classes

   The box predictor head controls class outputs; it must match the number of classes in your dataset.

2. Step 2: Replace the box predictor head

   Replace the existing head with a new one configured for 5 classes to fine-tune properly.

3. Final Answer:

   Replace the model's box predictor head with a new one having 5 output classes -> Option D

4. Quick Check:

   Adjust box predictor for new class count [OK]

Hint: Change box predictor head to match your classes [OK]

Common Mistakes:

• Changing input image channels incorrectly
• Not replacing box predictor head
• Freezing all layers prevents fine-tuning