PyTorch-RPNet
PyTorch implementation of Residual Pyramid Learning for Single-Shot Semantic Segmentation
Network
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
- Python 3 and pip.
- Set up a virtual environment (optional, but recommended).
- Install dependencies using pip:
pip install -r requirements.txt.
Examples: Training
sh train.sh
Examples: Testing
python main.py -m test --step 1