D2R Object Detection Dataset and Pre-Trained Model by Markus Mikkonen

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Explore Dataset

Here are a few use cases for this project:

Video Game Content Creation: The D2R computer vision model can be used by video game content creators and streamers to automatically identify and tag game objects and characters in their videos. This can help the audience understand the content better and can increase engagement levels.
Game Strategy Formation: Players of the video game could use the model to analyze screen shots or videos of gameplay to develop more effective strategies. By identifying characters and objects, players can anticipate game events and make informed in-game decisions.
Video Game Quality Assurance Testing: Game developers can use this technology to automate parts of their quality assurance testing procedures. By identifying characters and objects in different scenarios and environments, they can ensure that every element is appearing and functioning correctly.
Gaming Community Moderation: In gaming communities where users upload game screen captures, the model can be used to sort and categorize images based on the presence of specific objects or characters.
Interactive Gaming Guides: The model can be employed to create interactive gaming guides or tutorials. By identifying game elements, the guides can provide customized tips to players based on their current scenario or gameplay.

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{ d2r-fxcz1_dataset,
    title = { D2R Dataset },
    type = { Open Source Dataset },
    author = { Markus Mikkonen },
    howpublished = { \url{ https://universe.roboflow.com/markus-mikkonen-gcmd9/d2r-fxcz1 } },
    url = { https://universe.roboflow.com/markus-mikkonen-gcmd9/d2r-fxcz1 },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { aug },
    note = { visited on 2023-12-11 },
}