Multi-Label Genre Classification

A TensorFlow Convolutional Neural Network (ConvNet) Approach to Multi-Label Genre Classification on Movie Posters.

Overview

This project implements a Convolutional Neural Network (CNN) using TensorFlow to perform multi-label genre classification of movie posters. The model analyzes visual features of movie posters to predict multiple genres associated with each movie.

Repository Contents

• Multi_Label_Genre_Classification.ipynb: Jupyter Notebook detailing data preprocessing, model architecture, training procedures, and evaluation metrics.
• LICENSE: MIT License file.

Requirements

• Python 3.x
• Jupyter Notebook
• TensorFlow
• Keras
• Pandas
• NumPy
• Scikit-learn
• Matplotlib

Setup Instructions

1. Clone the Repository:

   git clone https://github.com/AkshadK7/Multi-Label-Genre-Classification.git
   cd Multi-Label-Genre-Classification

2. Install Dependencies: It's recommended to use a virtual environment to manage dependencies.

   pip install -r requirements.txt

3. Run the Jupyter Notebook:

   jupyter notebook Multi_Label_Genre_Classification.ipynb

Usage

• Open the Multi_Label_Genre_Classification.ipynb notebook.
• Follow the steps to preprocess the data, build and train the CNN model, and evaluate its performance.
• Modify the notebook as needed to experiment with different model architectures or parameters.

Results

The notebook provides performance metrics and visualizations comparing the model's predictions to the actual genres. These insights help assess the model's accuracy and identify areas for improvement.

License

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

Acknowledgements

Special thanks to the contributors of the datasets and the open-source community for providing tools and libraries that made this project possible.

*Note: Ensure that the `requirements.txt` file includes all necessary dependencies for the project. If it doesn't exist, you may need to create it by listing the required packages.*