This repository contains code for a Facial Emotion Recognition project. The goal of this project is to build and train a deep learning model that can recognize emotions from facial expressions in images.

• Introduction
• Dataset
• Installation
• Usage
• Results

In this project, we utilize a convolutional neural network (CNN) architecture to perform facial emotion recognition. The code is implemented using PyTorch and includes the following components:

• Loading a file containing image data and labels
• Data preprocessing and transformation
• Creating custom datasets and data loaders
• Model architecture definition
• Model training and evaluation

The dataset used for this project is FER2013, which contains facial expression images labeled with various emotions. The dataset is split into training and validation sets.

1. Clone this repository:

   git clone https://github.com/Aayush518/Facial_Emotion_Recognition.git
   cd Facial_Emotion_Recognition

2. Install the required dependencies:

   pip install torch pandas scikit-learn torchvision numpy matplotlib Pillow torchsummary tqdm   

1. Place the FER2013 dataset CSV file (e.g., fer2013_mini_XCEPTION.csv) in the project directory.

2. Run the data preprocessing and model training code:

   python Train_Model.py

3. Evaluate the trained model:

   python Trained.py

Include information about the model's performance on the validation set and any additional insights or visualizations.