02 / Computer vision · Deep learning
ResNet18 & VGG13
ResNet18 and VGG13 take on the same handwritten digits. A comparison built in PyTorch.
ARCHITECTURE STUDY02
ResNet18 ↔ VGG13 / MNISTThe question
How do two different network architectures approach the same image classification task?
The approach
I trained ResNet18 and VGG13 on MNIST using PyTorch. Keeping the dataset the same focuses the comparison on the architectures, including ResNet's residual connections and VGG's sequential layers.
How it works
A shared dataset
Both networks use MNIST, a dataset of handwritten digits. The task is to assign an input image to a digit class.
Different paths through a network
ResNet18 uses residual connections. VGG13 uses a sequential stack of layers. The project puts both architectures to work on the same task.
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