Model optimization (pruning, quantization) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is model pruning in machine learning?

Model pruning is a technique that removes less important parts of a neural network, like some connections or neurons, to make the model smaller and faster without losing much accuracy.

beginner

Explain quantization in the context of neural networks.

Quantization reduces the precision of the numbers used in a model, for example changing 32-bit floats to 8-bit integers, which makes the model smaller and faster to run, especially on devices with limited resources.

intermediate

How does pruning help improve model performance?

Pruning removes unnecessary parts of the model, which reduces its size and speeds up predictions, making it easier to run on devices with less memory or slower processors.

intermediate

What is a common trade-off when applying quantization to a model?

The trade-off is between model size and speed versus accuracy. Quantization makes the model smaller and faster but can slightly reduce its accuracy due to lower number precision.

advanced

Name two common types of pruning used in model optimization.

Two common types are: 1) Weight pruning, which removes individual connections with small weights, and 2) Structured pruning, which removes entire neurons or filters to simplify the model structure.

What does pruning mainly remove from a neural network?

A Training data samples

B Output layers

C Input features

D Less important connections or neurons

Pruning removes less important connections or neurons to reduce model size and speed up inference.

Quantization typically changes model numbers from:

A Integers to floats

B 32-bit floats to 8-bit integers

C 8-bit integers to 32-bit floats

D Strings to numbers

Quantization reduces precision, often converting 32-bit floating-point numbers to 8-bit integers.

Which is a benefit of model pruning?

A Speeds up model inference

B Requires more memory

C Adds more layers

D Increases model size

Pruning reduces model size and speeds up inference by removing unnecessary parts.

What is a possible downside of quantization?

A Model becomes slower

B Model uses more memory

C Model accuracy may slightly decrease

D Model requires more training data

Quantization can slightly reduce accuracy due to lower number precision.

Structured pruning removes:

A Entire neurons or filters

B Individual weights only

C Training samples

D Input features

Structured pruning removes whole neurons or filters to simplify the model.

Describe how pruning and quantization help optimize a computer vision model for deployment on mobile devices.

Think about how smaller and faster models help on phones.

Explain the trade-offs involved when applying pruning and quantization to a neural network.

Consider what you lose and gain with these techniques.