erfnet

Segmentation network toolkit

A toolbox for training and evaluating real-time semantic segmentation networks using Torch library.

GitHub

120 stars
2 watching
22 forks
Language: Lua
last commit: about 5 years ago
Linked from 1 awesome list


Backlinks from these awesome lists:

Related projects:

Repository Description Stars
eromera/erfnet_pytorch Provides a PyTorch implementation of the ERFNet architecture for semantic segmentation 432
e-lab/enet-training Provides tools and models for training deep neural networks for real-time semantic segmentation and scene parsing 351
erogol/seg-torch Custom image segmentation implementation using deep learning with Lua and Torch 37
e-lab/linknet An implementation of a deep learning network for image segmentation tasks using Lua and the Torch7 framework. 168
torch/nn An open-source neural network package providing a modular framework for building and training neural networks. 1,346
xiaoyufenfei/lednet A lightweight deep learning framework for real-time semantic segmentation 514
eryixie/planerecnet An implementation of a deep learning model for instance segmentation and monocular depth estimation. 79
media-smart/vedaseg A PyTorch-based toolbox for building and training semantic segmentation models 408
yu-changqian/torchseg A toolkit for building and training semantic segmentation models using PyTorch. 1,408
aurora95/keras-fcn Keras implementation of Fully Convolutional Networks for Semantic Segmentation 650
timosaemann/enet A deep neural network architecture for real-time semantic segmentation in images 585
oandrienko/fast-semantic-segmentation Real-time semantic segmentation using optimized network architectures 220
fedor-chervinskii/segnet-torch An implementation of Segmentation Network architecture with deconvolutional network in PyTorch for image segmentation tasks. 6
tramac/lightweight-segmentation Provides implementations of lightweight neural network models for real-time semantic segmentation. 356
zudi-lin/pytorch_connectomics A deep learning framework for automatic and semi-automatic segmentation of 3D image stacks in connectomics 172