Custom Mask R-CNN for Leaf Disease Detection

This repository contains code for training and testing a custom Mask R-CNN model to detect various leaf diseases. The project uses the Mask R-CNN implementation from Matterport.

Table of Contents

• Forking the Repository
• Cloning the Repository
• Installing Dependencies
• Setting Up the Project
• Running the Script
• Acknowledgments

Forking the Repository

1. Go to the GitHub repository you want to fork.
2. Click on the Fork button on the top right corner of the repository page.
3. Select your GitHub account to fork the repository.

Cloning the Repository

Cloning the Repository

1. After forking the repository, clone it to your local machine using the following command:

   git clone https://github.com/your-username/your-repo-name.git

Installing Dependencies:

1. pip install -r requirements.txt
2. conda create -n maskrcnn python=3.6.8
3. conda activate maskrcnn
4. pip install tensorflow==1.15.0

Setting Up the Project:

1. git clone https://github.com/matterport/Mask_RCNN.git
   cd Mask_RCNN
   python setup.py install
   cd ..

2. Copy the mrcnn folder from the Mask R-CNN repository into your project directory.

3. Set the root directory in custom.py to your project directory:

(python code) ROOT_DIR = "path/to/your/project"

Ensure you have the COCO weights file in your project directory. If not, download it from COCO weights.

Running the script:

1. To train the model, run the custom.py script:

python custom.py

After training, the weights will be saved in the logs directory.

2. To test the model, use the test_model.ipynb Jupyter notebook:

jupyter notebook test_model.ipynb

3. Load the trained weights and run the inference on your test images.

Acknowledgments

This README file includes all the instructions and necessary code for setting up and running the Mask R-CNN model for leaf disease detection.