Go Positions Computer Vision Project
Synthetic dataset of black and white stones on go boards. Generated using Unity Perception
Use Case
To be able to take a picture of a go game and figure out the position of each stone in order to score the game or analyze with AI. Project inspiration stems from this blog post along with past ideas we've had for this: https://blog.roboflow.com/chess-boards/
Classes
blackStone
: Black go stones, 90,501
labels
whiteStone
: White go stones, 89,963
labels
grid
: Cross section grid of a go board, 1,000
labels
Trained Model API
This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.
YOLOv8
This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.
YOLOv5
This project has a YOLOv5 model checkpoint available for inference with Roboflow Deploy. YOLOv5 is a proven and tested, production ready, state-of-the-art real-time object detection model.
Cite this Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ go-positions_dataset,
title = { Go Positions Dataset },
type = { Open Source Dataset },
author = { Synthetic Data },
howpublished = { \url{ https://universe.roboflow.com/synthetic-data-3ol2y/go-positions } },
url = { https://universe.roboflow.com/synthetic-data-3ol2y/go-positions },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { mar },
note = { visited on 2023-03-20 },
}