Emotion Detection: Happy and Sad Classification

Project Overview

This project aims to develop a machine learning model capable of classifying images based on emotional content, specifically distinguishing between happy and sad expressions. The model's accuracy in recognizing these emotions will enhance applications in mental health monitoring, social media analysis, and human-computer interaction.

Table of Contents

• Project Overview
• Installation
• Usage
• Dataset
• Model Training
• Evaluation
• Results
• Contributing
• License

Installation

To run this project, you need to have Python installed on your system along with the necessary libraries. Follow the steps below to set up your environment:

1. Clone the repository:

   git clone https://github.com/HoomKh/Emotion-Detection-HappySad-Classification.git

2. Change to the project directory:

   cd Emotion-Detection-HappySad-Classification

3. Install the required packages:

   pip install -r requirements.txt

Usage

To classify images based on their emotional content, follow these steps:

1. Ensure your dataset is properly formatted and placed in the specified directory.
2. Run the model training script:

   python train_model.py

3. Use the trained model to make predictions:

   python predict.py --image path_to_image.jpg

Dataset

The dataset used for this project should contain labeled images categorized into 'happy' and 'sad' expressions. Ensure your dataset is organized into two folders: happy and sad, each containing relevant images.

Model Training

The model is trained using a convolutional neural network (CNN) architecture. Training involves the following steps:

1. Preprocessing the images (resizing, normalization, etc.).
2. Splitting the dataset into training and validation sets.
3. Training the CNN model on the training set.
4. Validating the model on the validation set.

Evaluation

The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. These metrics provide insights into how well the model distinguishes between happy and sad expressions.

Results

The results of the model, including accuracy and loss graphs, will be saved and can be viewed in the results directory. Example predictions and their corresponding confidence scores will also be provided.

Contributing

Contributions are welcome! If you would like to improve this project, please fork the repository and submit a pull request. Ensure your changes are well-documented and tested.

License

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

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