Welcome to the Shershaah repository! This project is developed for the Bhartiya Antariksh Hackathon 2024. Our goal is to build an AI/ML model to detect craters on the lunar surface using images from the Orbiter High Resolution Camera (OHRC).
The project aims to automatically detect craters on the Moon's surface utilizing Mask R-CNN, a state-of-the-art deep learning model for object detection and segmentation. The primary data source is the OHRC images, which provide high-resolution imagery essential for accurate crater detection.
To get started with this project, follow the instructions below:
-
Clone the repository:
git clone https://github.com/isatyamks/shershaah.git cd shershaah -
Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate`
-
Install the required dependencies:
pip install -r requirements.txt
To use the model for detecting craters on lunar images, follow these steps:
- Prepare your OHRC images and place them in the
datasets/traindirectory. - Run the detection script:
samples\crater\inspect_crater_data.ipynb
- The results will be saved in the
outputdirectory with the detected craters highlighted.
- Python 3.8+
- TensorFlow
- Keras
- OpenCV
- NumPy
- Matplotlib
For a complete list of dependencies, refer to the requirements.txt file.
We welcome contributions from the community. If you'd like to contribute, please fork the repository, create a feature branch, and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License. See the LICENSE file for more details.
- The Bhartiya Antariksh Hackathon 2024 organizers for providing this platform.
- Our mentors and peers for their guidance and support.
Thank you for visiting our repository. We hope our project contributes to advancements in lunar exploration and AI technology.