hacathon_space_il

Description: This project uses the KMeans clustering algorithm to cluster points of interest in images from the Bereshit spaceship. The model is trained on a dataset of images from the Bereshit mission, and can be used to predict points of interest in future images from the Bereshit 2 mission.

Libraries:

• scipy
• numpy
• matplotlib
• sklearn
• Usage:

Installation

Clone the repository:

git clone https://github.com/myk93/hacathon_space_il.git

Navigate to the project directory:

cd hacathon_space_il

Create a virtual environment (optional but recommended):

python3 -m venv venv
source venv/bin/activate

Install the required dependencies:

pip install -r requirements.txt

This will train the KMeans model and cluster the points of interest in the images.

TO-DO

• Improve the performance of the KMeans model.
• Add more features to the images, such as color and texture.
• Use the model to predict points of interest in future images from the Bereshit 2 mission.

Acknowledgments:

The Bereshit mission is a joint project of the Israeli Space Agency and SpaceIL. The KMeans clustering algorithm is a popular clustering algorithm that is used in a variety of applications. The scipy, numpy, matplotlib, and sklearn libraries are all popular libraries for scientific computing in Python.