Streetlights Detection Computer Vision Project

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