This repository contains the code and data for a blood cell counter based on YoLo v7, a state-of-the-art object detection algorithm. The goal of this project is to provide a fast and accurate way to count and classify different types of blood cells from microscopic images.
To get started with the blood cell counter, follow the steps below:
- Python 3.7 or higher
- PyTorch 1.13.0 or higher
- Matplotlib
- Numpy
The blood cell counter is based on YoLo v7, a state-of-the-art object detection algorithm that can accurately detect and classify objects in images. The model is trained on a large dataset of microscopic blood cell images, which includes different types of blood cells such as red blood cells, white blood cells, and platelets. The model is optimized for speed and accuracy, making it suitable for real-time blood cell counting in clinical settings.
If you would like to contribute to the blood cell counter, please follow the guidelines below:
- Fork the repository and create a new branch for your contribution.
- Make your changes and test them thoroughly.
- Create a pull request with a detailed description of your changes.
- Your contribution will be reviewed by the project maintainers, and any feedback will be provided.
This project is licensed under the MIT License. See the LICENSE file for more details.
The blood cell counter is built upon the YoLo v7 object detection algorithm, developed by the original authors. We would like to acknowledge their contribution to the field of computer vision and object detection.
I am grateful to the team at the Protection Radiation Laboratory for inspiring me with their research challenge of counting human blood cells for their investigation on Blood Quality using Bioimpedance and Cell Counter. In response, I have developed this code to offer them assistance in solving this problem.
For any questions or inquiries, please contact [email protected] or visit my personal website at mahasin.tech.
We hope you find this blood cell counter useful for your research. Thank you!