SSD concept in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does SSD stand for in object detection?

SSD stands for Single Shot MultiBox Detector, a method to detect objects in images in one pass.

intermediate

How does SSD detect objects differently from two-stage detectors like Faster R-CNN?

SSD detects objects in a single step without a separate region proposal stage, making it faster but still accurate.

intermediate

What role do default boxes (anchor boxes) play in SSD?

Default boxes are pre-set boxes of different sizes and aspect ratios used to predict object locations and classes at multiple scales.

intermediate

Why does SSD use multiple feature maps at different scales?

Using multiple feature maps helps SSD detect objects of various sizes by looking at different levels of detail.

beginner

What are the main outputs of the SSD model for each default box?

For each default box, SSD outputs a class score (what object it might be) and box offsets (how to adjust the box to fit the object).

What is the main advantage of SSD over two-stage detectors?

A It detects objects in a single pass, making it faster.

B It uses more complex region proposals.

C It requires multiple passes over the image.

D It only detects one object per image.

SSD detects objects in one step without separate region proposals, which speeds up detection.

What are default boxes in SSD used for?

A To generate multiple guesses for object locations and sizes.

B To increase image resolution.

C To reduce the number of classes.

D To crop the image into smaller parts.

Default boxes are pre-defined boxes that help SSD predict where objects are and their sizes.

Why does SSD use multiple feature maps at different scales?

A To avoid using default boxes.

B To reduce the number of parameters.

C To increase training time.

D To detect objects of different sizes effectively.

Multiple feature maps let SSD detect small and large objects by looking at different detail levels.

What does SSD output for each default box?

A Only the bounding box coordinates.

B Only the class label.

C Class scores and box location adjustments.

D Image segmentation masks.

SSD predicts both what object is inside and how to adjust the box to fit it better.

Which of these is NOT a characteristic of SSD?

A Single pass detection.

B Uses region proposal network.

C Multi-scale feature maps.

D Default boxes for predictions.

SSD does not use a separate region proposal network; that is typical of two-stage detectors.

Explain how SSD detects objects in an image and why it is faster than two-stage detectors.

Think about how SSD predicts boxes and classes all at once.

Describe the purpose of default boxes and multi-scale feature maps in SSD.

Consider how SSD handles small and large objects.