global_shuter_cam+negatives Computer Vision Project
Updated a year ago
Here are a few use cases for this project:
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"Robotic Vision Training": This model could be used to train robots in distinguishing between different shapes and colors while navigating in defined environments, such as warehouses, factories, or game spaces.
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"Game Development": Developers could use the model during game testing to ensure the game's AI accurately recognizes and interacts with different objects present in the virtual world, like Yellow-Cone and Purple-Cube.
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"Educational Tools": It could be implemented in educational apps/games that aim to teach children about colors, shapes, and object recognition, adding an interactive element to learning.
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"Quality Control in Toy Manufacturing": The model could help in inspecting and sorting toy parts on an assembly line in factories, aiding in recognizing any deformities or inconsistencies in expected shapes and colors.
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"Assisted Reality Applications": This model could power vision-based systems like AR glasses, helping identify certain defined objects in the room like a 'Purple-Cube' and interact with them virtually.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
global_shuter_cam-negatives_dataset,
title = { global_shuter_cam+negatives Dataset },
type = { Open Source Dataset },
author = { orbit 1690 labeling team },
howpublished = { \url{ https://universe.roboflow.com/orbit-1690-labeling-team/global_shuter_cam-negatives } },
url = { https://universe.roboflow.com/orbit-1690-labeling-team/global_shuter_cam-negatives },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { oct },
note = { visited on 2024-11-17 },
}