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PyTorch-RPNet

PyTorch implementation of Residual Pyramid Learning for Single-Shot Semantic Segmentation

Network

image_1

Segmentation Result


Citing RPNet

Please cite RPNet in your publications if it helps your research:

@ARTICLE{8744483,
author={X. {Chen} and X. {Lou} and L. {Bai} and J. {Han}},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={Residual Pyramid Learning for Single-Shot Semantic Segmentation},
year={2019},
volume={},
number={},
pages={1-11},
keywords={Intelligent vehicles;real-time vision;scene understanding;residual learning.},
doi={10.1109/TITS.2019.2922252},
ISSN={1524-9050},
month={},}

This implementation has been tested on the Cityscapes and CamVid (TBD) datasets. Currently, a pre-trained version of the model trained in CamVid and Cityscapes is available here.

Dataset Classes 1 Input resolution Batch size Epochs Mean IoU (%)
CamVid 11 480x360 3 300 x 4 64.82
Cityscapes 19 1024x512 3 300 x 4 70.4 (val)
  • When referring to the number of classes, the void/unlabeled class is always excluded.
  • Just for reference since changes in implementation, datasets, and hardware can lead to very different results. Reference hardware: Nvidia GTX 1080ti and an Intel Core i7-7920x 2.9GHz.

The results on Cityscapes dataset

Method Input Size Mean IoU Mean iIoU fps FLOPs
ENet 1024*512 58.3 34.4 77 4.03B
ERFNet 1024*512 68.0 40.4 59 25.6B
ESPNet 1024*512 60.3 31.8 139 3.19B
BiSeNet 1036*768 68.4 - 69 26.4B
ICNet 2048*1024 69.5 - 30 -
DeepLab(Mobilenet) 2048*1024 70.71(val) - 16 21.3B
LRR 2048*1024 69.7 48.0 2 -
RefinNet 2048*1024 73.6 47.2 - 263B
RPNet(ENet) 1024*512 63.37 39.0 88 4.28B
RPNet(ERFNet) 1024*512 67.9 44.9 123 20.7B

Installation

  1. Python 3 and pip.
  2. Set up a virtual environment (optional, but recommended).
  3. Install dependencies using pip: pip install -r requirements.txt.

Examples: Training

sh train.sh

Examples: Testing

python main.py -m test --step 1

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pytorch implement for RPNet

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