Aperture

Modified residual network (ResNet) architecture trained on CIFAR-10 image classification dataset, with total number of trainable parameters less than 5 million.

Introduction

A residual network (ResNet) architecture is any convolutional network with skipped connections. The key component in ResNet models is a residual block that implements:

$$ ReLU(S(x) + F(x)) $$

where $S(x)$ refers to the skipped connection and $F(x)$ is a block that implements conv -> BN -> relu -> conv -> BN; here, “BN” stands for batch normalization. Chaining such blocks serially gives a deep ResNet. Primary hyperparameters (design variables) in such architectures include:

• $N$, the number of residual layers
• $B$, the number of residual blocks in the $i^{th}$ residual layer
• $C_i$, the number of channels in the $i^{th}$ layer.
• $F_i$, the filter size in the $i^{th}$ layer.
• $K_i$, the kernel size in the $i^{th}$ skip connection.
• $P$, the pool size in the average pool layer.

The project also experiments with:

• Optimizers (SGD, SGD with Nesterov, Adam, Adadelta, Adagrad)
• Data Augmentation Strategies (Standard, Mixup)
• Learning Rates
• Batch Sizes
• Epochs

The best test accuracy of 94.12% is achieved using ResNet-22, with total number of tranable parameters equal to 4,922,826. The execution logs for the experiments are located under /out directory.

Usage

• Clone the repository

  git clone git@github.com:utsavoza/aperture.git

• Setup and activate the virtual environment

  python3 -m venv .
  source ./bin/activate

• Install the required dependencies from requirements.txt

  pip3 install -r requirements.txt

• Configure and execute main.py

  python3 main.py --model=resnet22_2 --num-workers=2 --optim=adadelta --lr=0.1

Acknowledgements

The project makes use of and builds up on the pytorch-cifar repository by kuangliu for training various ResNet models on CIFAR-10 from scratch. The project also utilizes procedures from this repository for employing mixup data augmentation strategy.

License

Copyright (c) 2023 Utsav Oza

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.