Facial-Expression-Recognition

The following repository describes a framework for testing different types of neural network under different types of training with different databases.

Installation

To standardize the process, we use conda environments. Run this command to install conda enviroment:

conda create --name Faces --file requirements.txt

Run a model

To run a model that we have defined, we must execute the following command.

python train.py

If we want to evaluate a model, we must run the following command:

python eval.py

Settings

In the config.ini file we can define which model will be used, as well as the database and the training to follow.

Model

• name: The name of your model, should be the same name as your model class.
• model_file: The file where your model implementation is located. It should always be inside the models folder and should start with the prefix models

Train

• enable: If we want the model to be trainable.
• epochs: Number of epochs in which the model will be trained.
• checkpoints_path: Path, where the checkpoints of the model are saved.
• train_file: The file where your train implementation is located. It should always be inside the trainings folder and should start with the prefix trainings
• save_step: Interval of epochs in which a checkpoint is generated.

Dataset

• name: The name of your dataset.
• dataset_file: The file where your dataset implementation is located. It should always be inside the datasets folder and should start with the prefix datasets. Remember that in the file where your database is implemented, there must be a function called get_dataset that returns three DataLoaders: Train, Valid and Test. In addition to classes to classify. In short it should be something like this:

def get_dataset(***):
    ***
    return (train_loader, valid_loader, test_loader, classes)

Eval

• enable: If you want the model to be evaluated.
• model_to_eval: Name of the model to evaluate.

Metrics

• confussion_matrix: If you want to generate the confusion matrix when evaluating the model.
• graphics: If you want to generate graphs of loss and accuracy at the end of the training.

Useful information just for the owner of the repository.

Enlace a la carpeta de papers leídos: [https://drive.google.com/drive/folders/1A5JLgfl8_e3SZfaZDI5EaPcAu6lSb1Vm?usp=sharing]

Enlace a la carpeta con las bases de datos: [https://drive.google.com/drive/folders/1JHqI0sIV6AYSanFXWZ1oIwKIlzzRFLNp?usp=sharing]