MKSC Object Detection Dataset and Pre-Trained Model by mmax

Images

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Explore Dataset

Here are a few use cases for this project:

Game Development and Enhancement: Developers can incorporate the MKSC model into their game development process for identifying different game elements like characters or objects (coins, trees, peaches, etc.). This can facilitate automatic level design, character recognition and movement logic.
Interactive Content Creation: Streamers, digital content creators, or video game reviewers can use this model to analyze gameplay, identifying key characters and events in real-time or during video editing. This can open doors to more interactive and engaging content for audiences, possibly even automated highlights or recaps based on character occurrences.
Gaming Tutorials and Guides: The MKSC model can be used to develop comprehensive gaming guides and step-by-step tutorials. By recognizing game elements, it can show players where to find specific items or characters, or provide an analysis of gameplay to help players improve.
Machine Learning Research: Researchers can use the MKSC model as a baseline or reference for their research in video game AI or broader computer vision/ML studies. It provides a good use-case for pixel class recognition in complex, dynamic environments like video games.
Video Game AI Training: AI bots can be trained using the MKSC model. It can help build a neural network that understands video game landscapes, enabling the bots to interact more diversely and intelligently in a video game setup, and enhancing player vs. AI experiences.

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{
                            mksc_dataset,
                            title = { MKSC Dataset },
                            type = { Open Source Dataset },
                            author = { mmax },
                            howpublished = { \url{ https://universe.roboflow.com/mmax/mksc } },
                            url = { https://universe.roboflow.com/mmax/mksc },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-05-05 },
                            }