Crater Detection with RCN

This repository contains a notebook that demonstrates the implementation of an RCNN (Region-based Convolutional Neural Network) model with pre-trained RESNET50_V2 backbone for detecting craters in satellite images. The project is designed to provide a hands-on experience with applying advanced computer vision techniques.

Overview

The notebook guides you through the process of setting up the environment, preparing the dataset, defining the RCNN model, training the model, and evaluating its performance. The goal is to detect and localize craters in satellite imagery, which can be crucial for geological studies, planetary science research, and navigation.

Installation

To run the notebook, you need to have Python installed on your machine, along with Jupyter Notebooks or JupyterLab. You also need to install the following dependencies:

• PyTorch
• torchvision
• Albumentations
• OpenCV
• pycocotools

You can install the required libraries using pip:

pip install torch torchvision albumentations opencv-python-headless pycocotools

Usage

To use this project, follow these steps:

1. Clone the repository to your local machine.
2. Ensure you have all the required dependencies installed.
3. Open the Jupyter notebook and follow the instructions within.

Structure

The notebook is structured as follows:

1. Environment Setup: Cloning necessary repositories and installing dependencies.
2. Importing Packages: Importing required Python packages for the project.
3. Data Preparation: Instructions on how to prepare and load the dataset for training and evaluation.
4. Model Definition: Defining the RCNN model architecture and necessary utilities.
5. Training: Steps to train the RCNN model on the prepared dataset.
6. Evaluation: Evaluating the model's performance on a test set.

Contributing

Contributions to this project are welcome! Please fork the repository and submit a pull request with your proposed changes.

License

This project is open-sourced under the MIT License. See the LICENSE file for more details.

Acknowledgments

• PyTorch team for providing an excellent deep learning framework.
• The torchvision contributors for vision-related operations and model implementations.
• The creators of the Albumentations library for powerful image augmentation techniques.