An approach for pixel-based classification of drivable parts of the road (known as Semantic Segmentation). Project 12 of Udacity's Self-Driving Car Engineer Nanodegree Program.
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Updated
Mar 19, 2018 - Python
An approach for pixel-based classification of drivable parts of the road (known as Semantic Segmentation). Project 12 of Udacity's Self-Driving Car Engineer Nanodegree Program.
Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)
FCN model and utils for automating the segmentation of knee structures from MR images
model, training code, and thoughts from training an UNet
Udacity Self-Driving Car Engineer Nanodegree Semantic Segmentation Project.
Implementing FCN8 and FCN32 semantic segmentation models to classify pixels in road scenes.
[Caffe] A deep convnet developed for semantic segmentation task.
Transfer Learning using PyTorch
A project on Topic Modelling and Text Summarization of NIPS research papers
Semantic Segmentation on the Indian Driving Dataset for the NVCPRIPG 2019 Challenge
Fully Convolutional Image Segmentation based on ResNet.
3D obstacle avoidance using perception
Fully convolutional neural network for semantic segmentation of pet images.
A fully convolutional approach to identifying road pixels in images
Experimental playground repository for training FCNs for Semantic Segmentation
fully convolutional networks
road detection using semantic segmentation and fully connected neural network
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