Reproducing AlexNet Paper

This repository is the official implementation of Reproducing AlexNet Paper as the final project of ARI5004 Deep Learning course. In this study the ImageNet100 dataset has been used.

Environment

In this project Python virtual environment has been utilized. To create a virtual environment for the project run the following code:

python -m venv <path-to-virtualenv>

After virtual environment is created to activate the virtual environment run the following code:

call <path-to-virtualenv>/Scripts/activate.bat

source <path-to-virtualenv>/bin/activate

After activating the virtual environment your terminal should look like below:

(venv) C:\<path-to-project>

(venv) machine-name:path-to-project username$

(venv) username@machine-name:path-to-project$

Requirements

After activating the virtual environment to install the requirements run the following code:

pip install -r requirements.txt

Training

To train the AlexNet model in the paper, run the following command:

python train.py -tb <train-batch-size> -vb <validation-batch-size> -op <optimizer> -dp <dataset-path> -lr <learning-rate> -e <number-of-epochs> -vs <validation-dataset-size> -j <json-label-file> -cp <checkpoint-path> -nc <number-of-classes>

To get more help and see the options run the following command:

python train.py --help

Evaluating

To evaluate the model run the following command:

python eval.py -td <test-dataset-path> -bs <test-batch-size> -ch <checkpoint-path>

To get more help and see the options run the following command:

python eval.py --help

Results

Our model achieves the following performance:

AlexNet	Top-1 Accuracy	Top-5 Accuracy
AlexNet	3.36%	14.34%

Contributing Guidelines

We welcome contributions to this project! To ensure a smooth contribution process, please follow these guidelines:

Areas of Contribution:

• Bug fixes and improvements to the codebase
• New features related to the paper or model
• Documentation improvements and tutorials
• Unit tests and code coverage enhancements
• Sharing experimental results and analyses

Contribution Workflow:

1. Fork this repository and create a new branch for your changes.
2. Implement your changes and update the relevant documentation.
3. Run unit tests and ensure your code adheres to the project's style guide.
4. Create a pull request and clearly describe your changes.
5. Be prepared to address any feedback or suggestions from the maintainers.

Additional Notes:

• Please adhere to the PEP 8 coding style guide.
• Include unit tests for any new code you add.
• Use descriptive commit messages and pull request titles.
• We appreciate contributions in any form, even if they are small bug fixes or suggestions.

Thank you for your interest in contributing!

For citation you can use the following BibTeX

@misc{alexnet_reproduction,
  author = "Anwar Abuelrub, and Volkan Bakir",
  title = "Reproducing AlexNet Paper: Final Project for ARI5004 Deep Learning Course",
  year = "2024",
  howpublished = "\url{https://github.com/creaturerigger/reproducing_alexnet_paper}",
  note = "Implements the AlexNet model from the {ImageNet Classification with Deep Convolutional Neural Networks} paper on the ImageNet100 dataset. Includes training, evaluation, and performance analysis scripts."
}