yyfyan / Fast_Seg

This repo provides :zap: fast:zap: semantic segmentation models on CityScapes/Camvid DataSet by Pytorch

⚡Fast_Seg⚡

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

Usage

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

Datasets Perparation

• You can download [cityscapes] dataset (https://www.cityscapes-dataset.com/) from here. Note: please download leftImg8bit_trainvaltest.zip(11GB) and gtFine_trainvaltest(241MB).
• You can download camvid dataset from here.
• You can download pretrained XceptionA here, and ResNet18 with bgr input here and ResNet50 with bgr input here.

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

License

This project is released under the Apache 2.0 license.

Acknowledgement

Thanks to previous open-sourced repo:
Encoding
CCNet
TorchSeg
pytorchseg