https://github.com/biswadeep-roy/human-emotion-detection-using-deep-learning

This project implements a deep learning model for the detection of human emotions in images. It uses Convolutional Neural Networks (CNNs) to classify images into three emotional categories: angry, happy, and sad.
https://github.com/biswadeep-roy/human-emotion-detection-using-deep-learning

python python3 tensorflow

Last synced: 3 days ago
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This project implements a deep learning model for the detection of human emotions in images. It uses Convolutional Neural Networks (CNNs) to classify images into three emotional categories: angry, happy, and sad.

• Host: GitHub
• URL: https://github.com/biswadeep-roy/human-emotion-detection-using-deep-learning
• Owner: biswadeep-roy
• License: mit
• Created: 2023-10-05T12:10:22.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-01T06:12:01.000Z (about 1 year ago)
• Last Synced: 2024-11-08T10:26:44.254Z (about 2 months ago)
• Topics: python, python3, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.16 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Human-Emotion-Detection-using-Deep-Learning

This project implements a deep learning model for the detection of human emotions in images. It uses Convolutional Neural Networks (CNNs) to classify images into three emotional categories: angry, happy, and sad.

![image](https://github.com/biswadeep-roy/Human-Emotion-Detection-using-Deep-Learning/assets/74821633/fbb5eabe-0dc9-46eb-92fb-b049ac031958)

## Dataset

The dataset used in this project is the "Human Emotions Dataset" available on Kaggle. It consists of labeled images representing different emotions.

### Dataset Link

[Human Emotions Dataset](https://www.kaggle.com/muhammadhananasghar/human-emotions-datasethes)

## Model Architecture

The model architecture is based on a modified LeNet-5 CNN architecture. It includes layers for data preprocessing, convolution, batch normalization, max-pooling, dropout, and fully connected layers. Regularization techniques are used to prevent overfitting.

## Configuration

The project is configured with various hyperparameters, including batch size, image size, learning rate, and dropout rate, which can be adjusted as needed.

## Training

The model is trained using TensorFlow and Keras. The training dataset is shuffled and preprocessed, and the model's performance is evaluated using a validation dataset.

## Usage

To use this project, follow these steps:

1. Clone the repository:




`git clone https://github.com/biswadeep-roy/Human-Emotion-Detection-using-Deep-Learning/`




`cd human-emotion-detection`




2. Install the required libraries:

`pip install -r requirements.txt`

3. Download and unzip the dataset (or use your own dataset) into the appropriate directories:

- Training data: `/content/dataset/Emotions Dataset/Emotions Dataset/train`

- Validation data: `/content/dataset/Emotions Dataset/Emotions Dataset/test`

4. Adjust the configuration in the code as needed.

5. Train the model.

6. Evaluate the model.

7. Make predictions on new data:

## Results and Outcomes

# Model Performance

The model achieved a significant level of accuracy in classifying human emotions in images.

Training and validation metrics, such as accuracy and loss, can be found in the training logs.

# Key Outcomes

Successful implementation of a modified LeNet-5 architecture for emotion detection.

Efficient preprocessing of image data, including resizing and rescaling.

Effective use of regularization techniques to prevent overfitting.

Deployment of a TensorFlow-based deep learning model.

# Challenges Faced

Mention any challenges you encountered during the project, such as data quality issues, overfitting, or performance optimization.

# Future Improvements

Provide insights into potential future improvements or enhancements for the project.

Ideas may include fine-tuning hyperparameters, exploring different architectures, or incorporating real-time emotion detection from a video stream.

# Acknowledgments

Express gratitude to any contributors, libraries, or datasets that played a significant role in achieving the project's outcomes.

# Visualizations

Include any relevant visualizations, such as confusion matrices or ROC curves, to showcase the model's performance.

## Author

- (https://github.com/biswadeep_roy)

## License

This project is licensed under the MIT License - see the [MIT](LICENSE) file for details.

Feel free to reach out if you have any questions or suggestions!