Lego Bricks Computer Vision Project

AutoDash

Updated 10 months ago

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Classes (5)
2x1
2x2
2x4
base
pooltable

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Description

Project Overview:

The Lego Brick Detection Model is a machine learning model that utilizes computer vision techniques to detect and track objects within a video stream from a camera mounted on top of a Table with Legos pieces. The goal of the project is to create an interactive dashboard that provides real-time information about the Legos positions.

Class Types:

  1. Small (2x1)
  2. Medium (2x2)
  3. Large (2x4)
  4. 16x16 plate (base)
  5. Table (pooltable)

Status and Timeline:

The Lego Brick Autodashboard is currently in development and is expected to be completed by March 31, 2024. The team is working in ensuring functionality and seamless integration with the other main project components.

Contribution and Labeling Guidelines:

To help improve the accuracy of the Lego Brick, community members are encouraged to contribute labeled data to the project's dataset. Please refer to the "Annotate" section for instructions on how to get started. All contributions will be acknowledged and credited appropriately.

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            lego-bricks-uwgtj_dataset,
                            title = { Lego Bricks Dataset },
                            type = { Open Source Dataset },
                            author = { AutoDash },
                            howpublished = { \url{ https://universe.roboflow.com/autodash/lego-bricks-uwgtj } },
                            url = { https://universe.roboflow.com/autodash/lego-bricks-uwgtj },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { feb },
                            note = { visited on 2024-12-12 },
                            }
                        
                    

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