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DLP · Object detection & its metrics

14 questions · 70 marks · 3 papers

Answer key

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Deep Learning Practice

Object detection & its metrics

Official Paper

Examination Instructions & Guidelines

Solve on notebook/paper first before checking solutions.
MCQ: Exactly 1 option is correct. Negative marking applies.
MSQ: One or more options correct. Select all to score.
NAT: Numerical response. No negative marking.
Scientific calculator allowed for all calculations.
Official answer key is sealed at the end of this sheet.

End Term · 31 Aug 2025 (FN)End Term

Q1MSQ5 Marks·Choose all correct options
Key ↓
In the Faster R-CNN architecture, the Region Proposal Network (RPN) plays a critical role in object detection. Which of the following statements correctly describe the role of the RPN?
1. AThe RPN generates a set of class-agnostic region proposals likely to contain objects.
2. BThe RPN performs classification and bounding box regression to output object classes and their exact locations.
3. CThe RPN uses anchor boxes of different scales and aspect ratios to slide over convolutional feature maps.
4. DThe RPN replaces traditional region proposal methods like selective search, making the pipeline faster and end-to-end trainable.
5. EThe RPN outputs fixed-size feature maps that are directly used for object classification.
6. FThe RPN filters out background regions by assigning objectness scores to anchors.
Q2MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are valid advantages of YOLO compared to Fast R-CNN or Faster R-CNN?
1. AYOLO performs object classification and localization in a single forward pass, making it faster than two-stage detectors like Fast R-CNN and Faster R-CNN.
2. BYOLO does not require region proposal generation, making the entire pipeline fully end-to-end trainable.
3. CYOLO achieves higher localization accuracy than Faster R-CNN on small objects due to its coarse grid-based prediction.
4. DYOLO treats bounding box coordinates and objectness score as regression outputs, while computing class probabilities through classification.
5. EYOLO achieves higher precision by using a large number of anchor boxes per region.
6. FYOLO treats object detection as a regression problem, which simplifies the detection pipeline and reduces computational overhead.
Q3NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:
Q4NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:

End Term · 31 Aug 2025 (AN)End Term

Q5MCQ5 Marks·Choose one option
Key ↓
1. AA–(d), B–(e), C–(a), D–(b), E–(c)
2. BA–(d), B–(a), C–(e), D–(b), E–(c)
3. CA–(e), B–(d), C–(a), D–(c), E–(b)
4. DA–(d), B–(e), C–(b), D–(a), E–(c)
Q6MSQ5 Marks·Choose all correct options
Key ↓
In the Faster R-CNN architecture, the Region of Interest (RoI) Pooling layer plays a key role in the object detection pipeline. Which of the following statements correctly describe the function and properties of RoI Pooling?
1. ARoI Pooling converts variable-sized region proposals into fixed-size feature maps for the classifier head.
2. BRoI Pooling crops the region proposals from the input image and resizes them to a fixed dimension.
3. CRoI Pooling enables batching and fully connected layers by standardizing the input dimensions.
4. DRoI Pooling uses bilinear interpolation to produce smoother feature maps for each proposal.
5. ERoI Pooling operates on convolutional feature maps, not directly on the raw image.
6. FRoI Pooling discards proposals with low objectness scores before passing them to the classifier.
Q7MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are valid advantages of YOLO compared to Fast R-CNN or Faster R-CNN?
1. AYOLO is faster at inference time because it predicts bounding boxes and class probabilities in a single pass.
2. BYOLO is fully end-to-end trainable and does not rely on external region proposal mechanisms.
3. CYOLO achieves higher recall than Faster R-CNN on all types of objects, especially small and overlapping ones.
4. DYOLO formulates detection as a combination of regression (for bounding box coordinates and objectness score) and classification (for discrete object categories).
5. EYOLO uses multi-scale feature fusion to improve detection of small objects.
6. FYOLO’s architecture allows it to be deployed more easily on real-time and edge devices due to its compactness.
Q8NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:
Q9NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:

End Term · 13 Apr 2025 (AN)End Term

Q10MCQ5 Marks·Choose one option
Asked 3 times Key ↓
1. A0.25
2. B0.29
3. C0.45
4. D0.21
Q11MCQ5 Marks·Choose one option
Key ↓
In the Fast R-CNN object detection pipeline, which of the following steps is NOT part of the preprocessing process?
1. AResize the input image to a fixed size.
2. BExtract region proposals using Selective Search.
3. CApply a Region Proposal Network (RPN) to generate region proposals.
4. DExtract features from the entire image using a CNN backbone before classifying region proposals.
Q12MCQ5 Marks·Choose one option
Key ↓
1. A2.00
2. B2.25
3. C2.40
4. D2.75
Q13MCQ5 Marks·Choose one option
Key ↓
Consider the following code snippet, which generates predictions and ground truth for an object detection task: Given the predictions and ground truths, what are the precision and recall values?
1. APrecision: 0.75, Recall: 1.00
2. BPrecision: 0.75, Recall: 0.75
3. CPrecision: 1.00, Recall: 0.75
4. DPrecision: 0.60, Recall: 1.00
Q14MCQ5 Marks·Choose one option
Asked 3 times Key ↓
Consider the following code snippet, which calculates the F1-score for an object detection task: Given the predictions and ground truth, what is the calculated F1-score?
1. A0.67
2. B0.71
3. C0.73
4. D0.75

Official Key

Master Answer Key & Explanations

Official answer keys from IIT Madras BS past papers. Click any question to review.

Reveal Key

Q1A, C, D, F

Q2A, B, D, F

Q30.61 to 0.65

Q40.49 to 0.53

Q5A

Q6A, C, E

Q7A, B, D, F

Q80.61 to 0.65

Q90.3 to 0.34

Q10B

Q11C

Q12C

Q13A

Q14C

Questions and answer keys are reproduced from the official IIT Madras BS papers. Topics and repeated question types are BTechi’s own grouping. BTechi is not affiliated with IIT Madras.