Model comparison in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of model comparison in machine learning?

The main goal is to find which model performs best on a given task by comparing their accuracy, speed, and other metrics.

beginner

Name two common metrics used to compare computer vision models.

Accuracy and inference time are common metrics. Accuracy measures how often the model predicts correctly, and inference time measures how fast the model makes predictions.

intermediate

Why is it important to compare models on a validation set rather than the training set?

Because the validation set shows how well the model generalizes to new data, while the training set only shows how well it learned the examples it saw.

intermediate

What does it mean if one model has higher accuracy but slower inference time than another?

It means the first model is more accurate but takes longer to make predictions. Choosing between them depends on whether accuracy or speed is more important for the task.

beginner

How can visualizing model predictions help in model comparison?

Visualizing predictions helps spot where models make mistakes or succeed, giving insight beyond numbers and helping choose the best model for real-world use.

Which metric measures how fast a computer vision model makes predictions?

A Accuracy

B Inference time

C Loss

D Precision

Inference time measures the speed of the model's predictions.

Why should models be compared on a validation set instead of the training set?

A Training set is easier to use

B Training set is larger

C Validation set has fewer labels

D Validation set shows generalization to new data

Validation set tests how well the model works on unseen data.

If Model A is more accurate but slower than Model B, what should you consider?

A Always choose the faster model

B Ignore accuracy if speed is slow

C Whether accuracy or speed matters more for your task

D Use both models together

Choosing depends on the task's needs for accuracy versus speed.

Which of these is NOT a typical metric for model comparison?

A Model color

B Inference time

C Accuracy

D Precision

Model color is irrelevant for performance comparison.

How can visualizing model outputs help in comparison?

A Shows where models succeed or fail

B Increases model accuracy

C Reduces inference time

D Changes model architecture

Visualization helps understand model behavior beyond numbers.

Explain why comparing models using multiple metrics is important in computer vision.

Think about what matters more: speed or correctness.

Describe how you would decide between two models where one is faster but less accurate, and the other is slower but more accurate.

Consider the situation where the model will be used.