Skip to content
This repo provides ⚡️ fast⚡️ semantic segmentation models on CityScapes/Camvid DataSet by Pytorch
Python Shell
Branch: master
Clone or download
Latest commit 9b36739 Dec 12, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
data add more txt files Nov 27, 2019
exp add more scripts Dec 10, 2019
libs merge loss file Dec 10, 2019
.gitignore add more txt files Nov 27, 2019
LICENSE add more models Nov 28, 2019 add more models Nov 30, 2019 Update Dec 12, 2019
requirement.txt add more txt files Nov 27, 2019 add more scripts Dec 10, 2019 fix bugs and add ICNet training scripts Dec 10, 2019


This repo try to implement state-of-art fast semantic segmentation model s on road scene dataset(CityScape, Mapillary, Camvid).

What is purpose of this repo?

This repo aims to do experiments and verify the idea of fast semantic segmentation and this repo also provide some fast models.

Our ICnet implementation achieves 74.5% mIoU which is 5% point higher than orginal paper. !!!!! Here: model

Another Link For Accurate Seg:

GALD-net provides some state-of-art accurate methods implementation.

Model Zoo (Updating)

  1. ICNet:ICnet for real-time semantic segmentation on high-resolution images. ECCV-2018, paper
  2. DF-Net: Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search.CVPR-2019, paper
  3. Bi-Seg: Bilateral segmentation network for real-time semantic segmentation.ECCV-2018, paper
  4. DFA-Net: Deep feature aggregation for real-time semantic segmentation.CVPR-2019,paper
  5. ESP-Net: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation. ECCV-2018,paper
  6. SwiftNet: In defense of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images. CVPR2019, paper
  7. Real-Time Semantic Segmentation via Multiply Spatial Fusion Network.(face++) arxiv,paper
  8. Fast-SCNN: Fast Semantic Segmentation Network.BMVC-2019 paper


  1. use for training For example, use scripts in exp floder for training and evaluation.
  2. use for prediction with different size input.

Datasets Perparation

Some Advice on Training

  1. use syn-bn(apex).
  2. use batch-size >=8.
  3. use deep supervised loss for easier optimation.
  4. use large crop size during training.
  5. longer training time for small models(60,000 interaction or more).
  6. use Mapillary data for pretraining for boosting performance.
  7. Deeply based resnet runs slowly than torch pretrained resnet but with higher accuracy.
  8. The small network doesn't need ImageNet pretraining if training longer time on Cityscape.(Fast-SCNN paper)

      (a) test image            (b) ground truth           (c) predicted result


This project is released under the Apache 2.0 license.


Thanks to previous open-sourced repo:

You can’t perform that action at this time.