vichetkao/graph_detection_model_v2

YOLOv26 Finetuned Figure/Graph Detection Model

This repository provides a YOLOv26 model finetuned for detecting figures and graphs in document images, such as those found in scientific papers. The model and code are open for public use and research.

Model Overview

• Base Model: YOLOv26
• Task: Figure/Graph detection in document images
• Finetuned on: Custom dataset of figures and graphs

Quick Start

1. Clone the repository and install dependencies

# Clone this repository
cd https://huggingface.co/vichetkao/graph_detection_model
python -m venv .venv
.\.venv\Scripts\activate
cd ../..
pip install -r requirements.txt

2. Download the Model Weights

Download the best.pt weights from this Hugging Face model page and place it in run/train/weights/best.pt or specify the path with --weights.

3. Run Detection

You can run detection on your images using the provided script:

python run_detect_testing.py --source "testing" --name testing_graph_bbox --device 0

• --source: Path to your image folder or file (default: testing)
• --weights: (Optional) Path to your model weights (best.pt)
• --device: Set to 0 for GPU or cpu for CPU inference

4. Output

• Results will be saved in run/detect/testing_graph_bbox
• YOLO label files will be in run/detect/testing_graph_bbox/labels

Example Detection Results

Below are some example detection results from the model:

import subprocess
subprocess.call([
    "python", "run_detect_testing.py",
    "--source", "testing",
    "--weights", "run/train/weights/best.pt",
    "--device", "0"
])

Citation

If you use this model, please cite the original YOLOv26 paper and this repository.

License & Credits

This project is released under an open-source license for research and educational use.

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Author & Credits

Author: Kao Vichet
Bachelor Student, Cambodia Academy of Digital Technology
AI Full Stack Developer Internship, Techo Startup Center
LinkedIn: https://www.linkedin.com/in/vichet-kao/

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