DOT_NN Object Detection Dataset by Opal

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Explore Dataset

readme with project details and resources.

Some helpful things you should add are:

A project overview The DOT and the Asset Management Department wants to collect assets, such as stops signs, curb cuts, street lights, etc and their exact coordinates to create a thorough database of these assets. The data scientists and engineers will create these databases and servers for a multitude of uses, whether that be adding more assets or knowing which assets need improvement.

Descriptions of each class type Classes : Streetlight, curbcut

Current status Current status: Task 1 Data collection & annotating (streetlights, curbcuts)

**Timeline**
	Task 2 : Create Dataset
	Task 3: Select a Model
	Task 4: Train
	Task 5: Visualize
	
	Using LiDAR -> point cloud
	
	Next Steps

Once your model is trained you can use your best checkpoint best.pt to:

Run CLI or Python inference on new images and videos
Validate accuracy on train, val and test splits
Export to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
Evolve hyperparameters to improve performance
Improve your model by sampling real-world images and adding them to your dataset

Cite This Project

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

@misc{
                            dot_nn_dataset,
                            title = { DOT_NN Dataset },
                            type = { Open Source Dataset },
                            author = { Opal_v },
                            howpublished = { \url{ https://universe.roboflow.com/opal_v/dot_nn } },
                            url = { https://universe.roboflow.com/opal_v/dot_nn },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { mar },
                            note = { visited on 2024-06-29 },
                            }