PK YOLO

[WACV'25] Official implementation of "PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplane MRI Slices".

AI & Machine LearningPythonGPL-3.0

Abstract

PK YOLO is an open-source AI & Machine Learning project. [WACV'25] Official implementation of "PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplane MRI Slices". This is the source code for the paper titled "PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplane MRI Slices" accepted by the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025), of which I am the first author. The paper is available to download from the WACV 2025 open access provided by the Computer Vision Foundation, IEEE Xplore Proceedings, or arXiv (the latest version). It is built using Python. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

This is the source code for the paper titled "PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplane MRI Slices" accepted by the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025), of which I am the first author. The paper is available to download from the WACV 2025 open access provided by the Computer Vision Foundation, IEEE Xplore Proceedings, or arXiv (the latest version).

2. Objective

[WACV'25] Official implementation of "PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplane MRI Slices".

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Python

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/mkang315/PK-YOLO.git
cd PK-YOLO
pip install -r requirements.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by mkang315 and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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