Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

This is a PyTorch implementation of research paper, Deep-Emotion

[Note] This is not the official implementation of the paper

Architecture

• An end-to-end deep learning framework, based on attentional convolutional network
• Attention mechanism is added through spatial transformer network

Datasets

• FER2013
• CK+
• JAFFE
• FERG

Prerequisites

To run this code, you need to have the following libraries:

• pytorch >= 1.1.0
• torchvision ==0.5.0
• opencv
• tqdm
• PIL

Structure of this repository

This repository is organized as :

• main This file contains setup of the dataset and training loop.
• visualize This file contains the source code for evaluating the model on test data and real-time testing on webcam.
• deep_emotion This file contains the model class
• data_loaders This file contains the dataset class
• generate_data This file contains the setup of the dataset

Usage

Cool video created by DeepLearning_by_PhDScholar for how to use this implementation.

Data preparation

Download the dataset from Kaggle, and decompress train.csv and test.csv into ./data folder.

How to run

Setup the dataset

python main.py [-s [True]] [-d [data_path]]

--setup                 Setup the dataset for the first time
--data                  Data folder that contains data files

To train the model

python main.py  [-t] [--data [data_path]] [--hparams [hyperparams]]
                                [--epochs] [--learning_rate] [--batch_size]

--data                  Data folder that contains training and validation files
--train                 True when training
--hparams               True when changing the hyperparameters
--epochs                Number of epochs
--learning_rate         Learning rate value
--batch_size            Training/validation batch size

To validate the model

python visualize.py [-t] [-c] [--data [data_path]] [--model [model_path]]

--data                  Data folder that contains test images and test CSV file
--model                 Path to pretrained model
--test_cc               Calculate the test accuracy
--cam                   Test the model in real-time with webcam connect via USB

Prediction Samples