Real Face Detector - COMP6321

Project Structure

Here is our project folder structure and files with their descriptions.

root
│
└───.github: GitHub workflow.
│
└───data: contains sample data for tests.
|
└───facenet: facenet package.
│
└───recipes: contains training codes for different models.
|
└───tests: contains unit tests and test scripts.
|
└───trained: contains different trained models such as FaceCNN, MLP, etc.

Installation

In the root folder, run the following command:

pip install .

or

 pip install . --use-feature=in-tree-build

Dataset

The dataset is not publicly available and can be requested from here.

Training

Our training scripts reside under the recipes folder. For training, we recommend using the same conda environment that has been defined for COMP6321. This means using python 3.8 and having the basic required packages like torch, numpy, scikit-learn, pandas, seaborn for the heatmap, and Pillow for image loading. Also, for faster training, you should use a cuda GPU with at least 8 GB VRAM and related torch packages installed. Should no GPU is not available, the CPU will be picked for training automatically.

Example of How to Run train.py :

In this example, we are launching the training process for FaceCNN. Run the following command in the root of the project:

python.exe "recipes\FaceCNN\train.py"

The trained model along with training and validation loss histories would be saved under the trained folder. At the end of each epoch, if the new weights achieve a lower validation loss, we would save that model. Each epoch should take about 15 minutes with RTX3070.

Testing and Evaluation

The project notebook can be accessed from here. It contains all of the testing and evaluation codes along with various figures such as confusion matrices. Running this notebook should give the expected result and is pretty much self-explanatory.

References

We follow the SpeachBrain guidelines for project structure, unit tests, documentation, formatting, and GitHub CI. SpeachBrain deployment code has also been directly used in this project.

Note: All other parts of the project are completely original and written by Mohammadamin Aliari. Also, all the academic references and other inspirations have been acknowledged in the project report.