Image Classifier for Mood Detection

This TensorFlow-based image classifier is designed to detect a person's mood from an image, classifying them as either happy or sad. The model has been trained on a dataset containing labeled images of individuals expressing happiness and sadness.

Accuracy Graph

Data Loss Graph

Installation

1. Clone the repository:

   git clone https://github.com/Akash-nath29/image-classifier.git

2. Navigate to the project directory:

   cd image-classifier

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Ensure you have the necessary dependencies installed (see Installation).

2. Place the image(s) you want to classify in the images directory.

3. Run the classifier script:

   python classify.py

   This script will analyze the images in the images directory and print the predicted mood for each image.

Model Details

• The model architecture is based on deep neural networks (DNNs), specifically designed for image classification tasks.
• It utilizes the TensorFlow framework for building and training the model.
• The model achieves accuracy comparable to state-of-the-art methods for mood detection in images.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License

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