All dookeydash Object Detection Dataset and Pre-Trained Model by sam

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Explore Dataset

Here are a few use cases for this project:

Gaming Environments: The "AllDookeyDash" model can be utilized in game development for identifying and categorizing different in-game objects like rockets, fragments, or wood. It will allow developers to create a smarter game environment responsive to various components for a more immersive gaming experience.
Space Debris Monitoring: The model could be employed in a space context to identify and categorize space debris (fragments and rockets). Classifying these items could aid in improving damage prevention and enhance safety procedures for astronauts and space equipment.
Safety and Security: It can be used within defense systems, utilizing its "danger" object recognition to identify potential threats or harmful objects in real-time, potentially aiding in early threat detection and proactive responses.
Natural Disaster Management: This model could be implemented for identifying and classifying debris - wood, fragments - after a natural disaster like a tornado or hurricane. It could help support the efficiency of clean-up operations and damage assessment.
Scoring Systems in Sports: With its ability to identify the 'score' object class, the model could be useful in sport events. It can be used to track, identify, and assign scores based on objects (like balls or markers) in sports like basketball or soccer, aiding in keeping accurate scores.

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{
                            all-dookeydash_dataset,
                            title = { All dookeydash Dataset },
                            type = { Open Source Dataset },
                            author = { sam },
                            howpublished = { \url{ https://universe.roboflow.com/sam-ipwxd/all-dookeydash } },
                            url = { https://universe.roboflow.com/sam-ipwxd/all-dookeydash },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-07-27 },
                            }