auto2 Object Detection Dataset and Pre-Trained Model by autolabeltest

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Explore Dataset

Here are a few use cases for this project:

Car Racing Training Simulation Auto2 can be used in virtual training simulations for professional or amateur car racers. Distinguishing different cone colors helps racers to understand the layout quickly and make strategic decisions during the race.
Driverless Vehicle Testing Auto2 can be applied to autonomous vehicle technology where it is critical for the vehicle to perceive and understand surroundings. The ability to distinguish between cone colors could be used for mapping and navigation.
Traffic Management Systems Auto2 can be integrated with smart city applications or traffic management systems. Recognition of colored cones can be useful for the automated tracking of roadwork areas, generate alternative route suggestions.
Incident Response Training Emergency response units could use the model for training purposes. By simulating various emergency situations with different cone colors representing different levels of urgency or hazard levels, the tool could assist in enhancing decision-making skills.
Construction Site Monitoring The model can be used to monitor construction sites. The identification of specific colored cones can indicate different areas on the site, informing automated systems or site managers about the current state of the construction area.

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{
                            auto2-s0kck_dataset,
                            title = { auto2 Dataset },
                            type = { Open Source Dataset },
                            author = { autolabeltest },
                            howpublished = { \url{ https://universe.roboflow.com/autolabeltest/auto2-s0kck } },
                            url = { https://universe.roboflow.com/autolabeltest/auto2-s0kck },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { jan },
                            note = { visited on 2024-05-15 },
                            }