Emotion_Classifier

A deep learning-based image classification system designed to detect and classify images as either "happy" or "sad." This project leverages a Convolutional Neural Network (CNN) model to achieve high accuracy in emotion detection from images.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Dependencies

Introduction

This project implements an image classification system using a Convolutional Neural Network (CNN) to detect emotions in images, specifically classifying them as "happy" or "sad." The model was trained to achieve a 96% accuracy in emotion detection, making it a reliable tool for emotion recognition tasks.

Features

• Deep Learning Model: Utilizes a CNN model for emotion detection.
• High Accuracy: Achieves 96% accuracy in classifying images as happy or sad.
• Real-Time Emotion Detection: Can be integrated into applications for real-time emotion recognition.

Installation

To set up this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/ZinalPotphode/Emotion_Classifier
   cd Emotion_Classifier

2. Install the required dependencies:

   pip install tensorflow opencv-python matplotlib jupyter

Usage

To use the emotion classification system, follow these steps:

1. Train the Model: Use the provided Jupyter notebook to train the CNN model on your dataset.

2. Classify Images: After training, you can use the model to classify new images as "happy" or "sad."

Dependencies

This project requires the following libraries and frameworks:

• Python 3.x
• TensorFlow
• OpenCV
• Matplotlib
• Jupyter Notebook

You can install these dependencies using the following command:

    pip install tensorflow opencv-python matplotlib jupyter