https://github.com/akshadk7/multi-label-genre-classification
A Tensorflow ConvNet Approach to the Multi Label Genre Classification on Movie Posters
https://github.com/akshadk7/multi-label-genre-classification
cnn-keras convolutional-neural-networks genre-classifier multi-label-image-classification tensorflow
Last synced: 8 months ago
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A Tensorflow ConvNet Approach to the Multi Label Genre Classification on Movie Posters
- Host: GitHub
- URL: https://github.com/akshadk7/multi-label-genre-classification
- Owner: AkshadK7
- License: mit
- Created: 2021-11-24T00:07:44.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2025-02-10T10:54:34.000Z (8 months ago)
- Last Synced: 2025-02-10T11:36:55.592Z (8 months ago)
- Topics: cnn-keras, convolutional-neural-networks, genre-classifier, multi-label-image-classification, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 440 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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**:
```bash
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.
```bash
pip install -r requirements.txt
```
3. **Run the Jupyter Notebook**:
```bash
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](https://github.com/AkshadK7/Multi-Label-Genre-Classification/blob/main/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.
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*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.*