The Story of SqueezeNet: Why Smaller CNNs Can Be Smarter? | Computer Vision Series

Colab Notebook: colab.research.google.com/drive/11GERunnzlzqgN_5Fi… Notes: miro.com/app/board/uXjVIo0AJQY=/?share_link_id=855…

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SqueezeNet Explained - The CNN That Proved Size Does Not Matter | Computer Vision from Scratch

In this lecture from the Computer Vision from Scratch series, we dive deep into SqueezeNet – the CNN architecture that shocked the deep learning world by delivering AlexNet-level accuracy with 50x fewer parameters and a model size under 0.5 MB.

We walk through:

The historical context: from AlexNet to Inception V1

Why SqueezeNet was introduced in 2016 despite the success of larger models like VGG and GoogleNet

The Fire Module – SqueezeNet’s core architectural innovation

Parameter efficiency using 1x1 convolutions and squeeze-expand blocks

Full implementation using transfer learning on a five-flowers image classification dataset

Side-by-side comparison with other architectures (AlexNet, VGG, Inception V1) in terms of accuracy, training time, and deployment potential

What surprised us?
SqueezeNet outperformed every other model we tried so far – including VGG and Inception – in both accuracy and training speed.

This is not just another architecture lecture. This is a case study in why smaller, smarter models are the future of edge AI.