This project utilizes parallel deep learning techniques to enhance image captioning capabilities using the PyTorch framework and the COCO dataset. By leveraging the power of parallel computing, we aim to improve processing speed and efficiency, enabling more sophisticated image understanding in real-time applications.
- Parallel data loading with PyTorch
DataLoader. - Advanced preprocessing techniques for handling large image datasets.
- Implementation of a deep learning model using multi-GPU training.
- Utilization of mixed precision training to optimize memory usage and computational speed.
Python 3.8+ PyTorch 1.7+ CUDA Toolkit 11.0+
Clone the repository and install the required packages:
git clone https://github.com/yourusername/yourprojectname.git
cd yourprojectname
pip install -r requirements.txt- Parallel data loading with PyTorch
DataLoader.
- Advanced preprocessing techniques for handling large image datasets.
- Implementation of a deep learning model using multi-GPU training.
- Utilization of mixed precision training to optimize memory usage and computational speed.
If you would like to contribute to this project, you can follow these steps:
- Fork the repository on GitHub.
- Create a new branch with a descriptive name for your contribution.
- Make your changes and commit them to your branch.
- Push your branch to your forked repository.
- Open a pull request on the original repository, describing your changes and why they should be merged.
We appreciate any contributions to this project and will review and merge them if they align with the project's goals and guidelines.
We would like to acknowledge the contributions of the following individuals and organizations to this project:
- Prof. Handan Liu for her guidance and insights.
- Abhishek Shankar for his contributions to the project.
This project is licensed under the MIT License.