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: almost 7 years ago
Linked from 1 awesome list


Backlinks from these awesome lists:

Related projects:

RepositoryDescriptionStars
eromera/erfnet_pytorchProvides a PyTorch implementation of the ERFNet architecture for semantic segmentation432
e-lab/enet-trainingProvides tools and models for training deep neural networks for real-time semantic segmentation and scene parsing351
erogol/seg-torchCustom image segmentation implementation using deep learning with Lua and Torch37
e-lab/linknetAn implementation of a deep learning network for image segmentation tasks using Lua and the Torch7 framework.168
torch/nnAn open-source neural network package providing a modular framework for building and training neural networks.1,346
xiaoyufenfei/lednetA lightweight deep learning framework for real-time semantic segmentation514
eryixie/planerecnetAn implementation of a deep learning model for instance segmentation and monocular depth estimation.79
media-smart/vedasegA PyTorch-based toolbox for building and training semantic segmentation models408
yu-changqian/torchsegA toolkit for building and training semantic segmentation models using PyTorch.1,408
aurora95/keras-fcnKeras implementation of Fully Convolutional Networks for Semantic Segmentation650
timosaemann/enetA deep neural network architecture for real-time semantic segmentation in images585
oandrienko/fast-semantic-segmentationReal-time semantic segmentation using optimized network architectures220
fedor-chervinskii/segnet-torchAn implementation of Segmentation Network architecture with deconvolutional network in PyTorch for image segmentation tasks.6
tramac/lightweight-segmentationProvides implementations of lightweight neural network models for real-time semantic segmentation.357
zudi-lin/pytorch_connectomicsA deep learning framework for automatic and semi-automatic segmentation of 3D image stacks in connectomics172