MASK-RCNN implementation for Lyft Perception Challenge
-
Updated
Jun 3, 2018 - Jupyter Notebook
MASK-RCNN implementation for Lyft Perception Challenge
Won 28th place in this competition to accurately detect cars and road in images from CARLA simulator at 10 frames per second.
Semantic segmentation models for self-driving cars. Models developed for "Lyft Udacity Challenge for Self-driving Cars".
Road Image Segmentation for Autonomous Vehicle using Fully Convolution Network (FCN).
Add a description, image, and links to the lyft-perception-challenge topic page so that developers can more easily learn about it.
To associate your repository with the lyft-perception-challenge topic, visit your repo's landing page and select "manage topics."