- Introduction
- Dataset
- Installation
- Project Structure
- Model Architecture
- Training
- Evaluation
- Usage
- Results
- Contributing
- License
Emotions play a crucial role in human interactions, influencing our decisions, perceptions, and behavior. Recognizing and interpreting emotions accurately is not only essential for personal interactions but also has a wide range of applications in technology, including social robotics, human-computer interaction, mental health assessments, and more.
This project aims to create a robust and efficient image classifier that distinguishes between happy and sad facial expressions. By leveraging the power of deep learning, specifically Convolutional Neural Networks (CNNs), we can automate the process of emotion detection with high accuracy.
The classifier is built using TensorFlow and Keras, popular frameworks for deep learning due to their ease of use and flexibility. Additionally, OpenCV, a powerful library for computer vision, is used for image preprocessing to ensure the input data is in the best possible form for training the model.
- End-to-End Pipeline: From data preprocessing to model training and evaluation, this project provides a comprehensive workflow for image classification tasks.
- Scalability: The modular design of the codebase allows for easy expansion and adaptation to other classification tasks or datasets.
- Real-World Application: With potential uses in emotion detection in videos, interactive applications, and mental health monitoring, this project lays the groundwork for impactful real-world solutions.
By following this project, you will gain insights into:
- How to preprocess image data for deep learning.
- Designing and training a CNN for binary classification.
- Evaluating model performance and making predictions on new data.
- Integrating deep learning models into practical applications.
The dataset used in this project consists of images labeled as "happy" or "sad". The images are sourced from publicly available datasets and manually curated to ensure quality and relevance. The dataset is split into training and validation sets.
- Training Set: 80% of the data
- Validation Set: 20% of the data
Each image is resized to 256x256 pixels and converted to RGB format if not already in that format. This ensures consistency and allows the model to learn effectively from the data.
To get started, clone this repository and install the necessary dependencies.
git clone https://github.com/snmath-pi/ImageClassfier
cd ImageClassifier
pip install -r requirements.txt