Road Segmentation Project: Pixel-per-pixel labeling through the use of a Fully Convolutional Neural Network
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Updated
May 28, 2018 - Jupyter Notebook
Road Segmentation Project: Pixel-per-pixel labeling through the use of a Fully Convolutional Neural Network
TensorFlow implementation of Fully Convolutional Networks
Udacity Self Driving Car Semantic Segmentation project
Some models built from scratch with PyTorch during my graduate program at UT Austin
[Caffe] A deep convnet developed for semantic segmentation task.
MATLAB implementation of popular image segmentation algorithms
Implementation of Segnet, FCN, UNet and other models in Keras.
Implement FCN and CNN network using C without Library (use opencv only when reading images)
Udacity Self-Driving Car Engineer Nanodegree Semantic Segmentation Project.
Implementing FCN8 and FCN32 semantic segmentation models to classify pixels in road scenes.
TensorFlow implementation of FCN and U-Net models
Semantic Segmentation on the Indian Driving Dataset for the NVCPRIPG 2019 Challenge
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