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Image Recognition with Machine Learning

Fire Module

Explore the fire module concept in SqueezeNet to reduce convolutional layer parameters effectively. Understand how squeeze layers decrease input channels and mix kernel sizes, enabling efficient and high-performance image recognition models.

We'll cover the following...

Chapter Goals:

  • Learn strategies for decreasing the number of parameters in a model
  • Understand how the fire module works and why it's effective
  • Write your own fire module function

A. Decreasing parameters

In order to make a smaller model, we need to decrease the number of weights per convolution layer. There are three ways to decrease the number of weights in a convolution layer:

  • Decrease the kernel size
  • Decrease the number of filters used
  • Decrease the number of input channels
We don't necessarily want to reduce the number of filters we use, since using a variety of filters allows us to extract different hidden features from the input. However, there are ways to decrease the kernel size and number of input channels while still maintaining good model performance. ...