occupy Object Detection Dataset and Pre-Trained Model by clioa

Images

840 images

Explore Dataset

Here are a few use cases for this project:

Chess Game Analysis: The "occupy" model could be utilized to track and analyze chess games in real time, identifying moves from either player in different turns. It can transform an actual physical game into a digital format instantly, ensuring easier evaluation and analysis.
Chess Tutoring Applications: This computer vision model can be incorporated into applications that support learning and playing chess. It could provide real-time feedback and recommendations by tracking the board's state.
Real-time AI Gaming: The model could be used in AI-driven chess gaming. By identifying different classes (x, 0, b, w), it could help the AI make calculated moves based on the current status of the game.
Automated Sports Broadcasting: During live or recorded broadcasts of chess games, the "occupy" model can provide automated updates of board occupation changes, aiding commentators and viewers in understanding the current state of the game.
Chess Game Archiving: The "occupy" model can be used to digitally archive historical or noteworthy games. The system could be used to take photos of important games and then record the positions and strategies employed in the game for posterity and study.

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.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            occupy_dataset,
                            title = { occupy Dataset },
                            type = { Open Source Dataset },
                            author = { clioa },
                            howpublished = { \url{ https://universe.roboflow.com/clioa/occupy } },
                            url = { https://universe.roboflow.com/clioa/occupy },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-04-27 },
                            }