FairFace Classification

This project aims to classify faces into different categories such as age, gender, and race, using the FairFace dataset. The model used is a MobileNet V3 Large pretrained on ImageNet dataset, and the last layer is modified and adapted to the current FairFace dataset.

Table of Contents

• Installation
• Usage
• Training
• Results
• License
• Acknowledgements

Installation

To install the required packages, run the following command:

sh runthis.sh

Usage

To use the model for inference, run the following command:

python inference.py

This will load the checkpoint file and run inference on the test dataset. The results will be saved to a CSV file.

To use the model for training, run the following command:

python train.py

This will start the training process using the FairFace dataset. The training process will be logged to TensorBoard, and the best model will be saved to a checkpoint file.

Training

The training process consists of the following steps:

1. Load the FairFace dataset and create the dataloaders.
2. Initialize the model and move it to the GPU.
3. Define the loss function and optimizer.
4. Train the model for a given number of epochs.
5. Evaluate the model on the test dataset.
6. Save the best model checkpoint.

Results

The results of the training process can be viewed using TensorBoard. To start TensorBoard, run the following command:

tensorboard --logdir runs

Then open your web browser and go to http://localhost:6006.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

I would like to thank the FairFace team for making their dataset available, and the PyTorch team for making their library so easy to use. I would also like to thank my mentors and classmates for their feedback and support during the development of this project.