Welcome to the Blindness Detection GitHub repository! This repository contains the code and resources for the Blindness Detection project, which is a task assigned by CodeClause Internship. The goal of this project is to develop a system that can detect signs of blindness in medical images.
Blindness Detection is a machine learning-based project that focuses on developing an algorithm capable of detecting blindness in medical images. The system is trained using a dataset of medical images labeled with blindness annotations. By analyzing these images, the model can classify whether a given image exhibits signs of blindness or not.
To run the Blindness Detection project locally, please follow the instructions below:
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Clone this repository to your local machine using the following command:
git clone https://github.com/gandharvk422/Blindness_Detection.git -
Navigate to the project directory:
cd Blindness_Detection -
Install the required dependencies:
pip install -r requirements.txt
To use the Blindness Detection system, follow these steps:
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Ensure that you have installed all the dependencies as mentioned in the installation section.
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Prepare your medical image dataset for testing. The dataset should be organized in a specific format, which is explained in the Data section.
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Run the detection script:
python Blindness Detection.py --image happy.jpg -
The system will process the image and display the results, indicating whether the image exhibits signs of blindness or not.
The Blindness Detection system is built using a deep learning model architecture, specifically tailored for image classification. The details of the model architecture, hyperparameters, and training process are explained in the code files and documentation within the repository.
The performance and results of the Blindness Detection system can be found in the repository's documentation. I have included detailed analysis and evaluation metrics to measure the accuracy and reliability of the model.