The Story of SqueezeNet: Why Smaller CNNs Can Be Smarter? | Computer Vision Series
Colab Notebook: https://colab.research.google.com/drive/11GERunnzlzqgN_5Fi2YpIr39gLNcul23?usp=sharingMiro Notes: https://miro.com/app/board/uXjVIo0AJQY=/?share_link_id=855263573673
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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.
Colab Notebook: https://colab.research.google.com/drive/11GERunnzlzqgN_5Fi2YpIr39gLNcul23?usp=sharingMiro Notes: https://miro.com/app/board/uXjVIo0AJQY=/?share_link_id=855263573673
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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 1×1 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.
