MsPacman-Train Object Detection Dataset and Pre-Trained Model by Kevin Karagitz

Images

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Explore Dataset

Here are a few use cases for this project:

Gameplay Analysis: Analyze recorded gameplay sessions of Ms. Pac-Man to evaluate player strategies and identify patterns. This can help players improve their strategizing and game developers understand player behavior.
Automated Gameplay: Develop AI bots that use MsPacman-Train to accurately recognize in-game objects and make gameplay decisions. These bots could be integrated into the game as alternative opponents or serve as training partners for players looking to improve their skills.
Gaming Tutorials: Enhance educational tutorials focused on Ms. Pac-Man strategies and tactics by automatically highlighting in-game objects, potential moves, and risks using the MsPacman-Train algorithms for better visualization.
Retro Gaming Content: Enable content creators, such as streamers and YouTubers, to automatically add metadata/tags to their Ms. Pac-Man content for improved searchability and discoverability. The recognition of specific in-game objects may also help creators cater to specific viewer interests or preferences.
Ms. Pac-Man: The Game Remastered: Utilize the MsPacman-Train model in the development of a modern remastered version of Ms. Pac-Man, ensuring that the AI within the game recognizes the original objects faithfully while potentially introducing new objects or features.

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.

Cite This Project

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

@misc{
                            mspacman-train_dataset,
                            title = { MsPacman-Train Dataset },
                            type = { Open Source Dataset },
                            author = { Kevin Karagitz },
                            howpublished = { \url{ https://universe.roboflow.com/kevin-karagitz-bh8at/mspacman-train } },
                            url = { https://universe.roboflow.com/kevin-karagitz-bh8at/mspacman-train },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-04-29 },
                            }