CNN Object Detection Models 5

Question 1

What distinguishes instance segmentation from semantic segmentation in the context of object detection?

Accepted Answer

Instance segmentation distinguishes between separate instances of the same class

Answer

Instance segmentation assigns a unique mask to each individual object instance, while semantic segmentation labels each pixel with a class without distinguishing instances.

Question 2

In the context of YOLO training, what is the purpose of using k-means clustering on the training dataset?

Accepted Answer

To determine good anchor box dimensions for the dataset

Answer

K-means clustering on ground-truth bounding box dimensions helps choose anchor sizes that best fit the objects in the specific training dataset.

Question 3

What is the main trade-off when increasing the number of anchor boxes per location in a detector?

Accepted Answer

Higher recall but increased computational cost and memory

Answer

More anchors improve coverage of object shapes and sizes (higher recall) but increase the number of predictions to process, raising computation and memory cost.

Question 4

Which of the following best describes the 'neck' component in modern object detection architectures like YOLOv4?

Accepted Answer

Feature aggregation module between backbone and detection head

Answer

The neck (e.g., PANet, FPN) aggregates and mixes features from different backbone stages before passing them to the detection head.

Question 5

Why does soft-NMS outperform standard NMS in crowded scene detection?

Accepted Answer

It decays scores of overlapping boxes rather than eliminating them

Answer

Soft-NMS reduces suppressed boxes' scores by a continuous function of overlap instead of hard removal, preserving detections of nearby objects.

CNN - Convolutional Neural Networks Practice Test

CNN - Convolutional Neural Networks Practice Test

CNN Object Detection Models 5