object detection for autonomous vehicles
In the domain of autonomous cars, object detection for self-driving vehicles is a major challenge. It must be able to precisely detect and identify objects in its surroundings, such as pedestrians, other cars, traffic signs, and barriers, in order to function safely and effectively.
By utilizing Intel OneAPI, the main objective of this project is to develop a reliable, accurate, and effective object detection system that will support the development of the next generation of autonomous vehicles.
to achieve this goal we are using state of the art U-net. which is quite good model for instance segmentation. we are trying to train this model on a little part of city scape dataset. once we train the model we will try to optimize it using oneAPI. we will record the data for both of the scenarios and onece we are done we will make this data available on this repository.
architecture of U-net
real time detection can also be perfrmed like shown in this diagram
INTEL oneapi AI analytics toolkit, OneDNN, python3, opencv, pillow, keras, intel-tensorflow, intel devcloud,
step-1 : clone this repository step-2 : run train.py
for more information please visit our medium page ...
I learnt how to work with Intel oneapi and devcloud. Intel oneapi has also given me a chance to explore various different libraries and toolkits that had helped us to optimize the performance of our model.