Conditional GAN (CGAN) - Jittor Implementation

This is a project that implements Conditional Generative Adversarial Networks (CGAN) using Jittor.

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Table of Contents

• Project Description
• How to Run
• Project Structure
• Tech Stack
• Author
• Acknowledgements

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Project Description

A Jittor implementation of Conditional GAN (CGAN).

How to Run

In this section, you can provide the instructions or steps to set up and run your project. Make sure to include all necessary dependencies and environment setup, and provide clear instructions for each step.

For example:

1. Clone the repository to your local machine:

git clone https://gitlink.org.cn/Qedsama/PA3-Graphics.git

2. Install project dependencies:

pip install -r requirements.txt

3. Run:

python ./src/CGAN.py

Ensure to provide enough context and explanations so that others can easily run your project.

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Project Structure

├── result/ # Directory for results ├── models/ # Directory for models │ ├── discriminator_last.pkl │ └── generator_last.pkl ├── src/ # Directory for code │ ├── CGAN.py ├── requirements.txt └── README.md

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Tech Stack

• Jittor
• Python
• Numpy
• Matplotlib

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Author

Qedsama

Github:https://github.com/Qedsama

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Acknowledgements

• Special thanks to the Jittor team for providing a powerful deep learning framework.

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