PyTorch Semantic Segmentation

Introduction

This repository is a PyTorch implementation for semantic segmentation / scene parsing. The code is easy to use for training and testing on various datasets. The codebase mainly uses ResNet50/101/152 as backbone and can be easily adapted to other basic classification structures. Implemented networks including PSPNet and PSANet, which ranked 1st places in ImageNet Scene Parsing Challenge 2016 @ECCV16, LSUN Semantic Segmentation Challenge 2017 @CVPR17 and WAD Drivable Area Segmentation Challenge 2018 @CVPR18. Sample experimented datasets are ADE20K, PASCAL VOC 2012 and Cityscapes.

Usage

Highlight:
- Both multithreading training (nn.DataParallel) and multiprocessing training (nn.parallel.DistributedDataParallel) (recommended) are supported. And the later one is much faster.
- Better reimplementation results with well designed code structures.
- All initialization models, trained models and predictions are available.
Requirement:
- Hardware: 4-8 GPUs (better with >=11G GPU memory)
- Software: PyTorch>=1.0.0, Python3, tensorboardX, apex

Clone the repository:

git clone https://github.com/hszhao/semseg.git

Train:
- Download related datasets and modify the relevant paths specified in folder config, and download ImageNet pre-trained models and put them under folder initmodel for weight initialization. Remember to use the right dataset format detailed in FAQ.md.
- Specify the gpu used in config then do training:
```
sh tool/train.sh ade20k pspnet50
```
Test:
- Download trained segmentation models and put them under folder specified in config or modify the specified paths.
- For full testing (get listed performance):
```
sh tool/test.sh ade20k pspnet50
```
- Quick demo on one image:
```
PYTHONPATH=./ python tool/demo.py --config=config/ade20k/ade20k_pspnet50.yaml --image=figure/demo/ADE_val_00001515.jpg TEST.scales '[1.0]'
```
Visualization: tensorboardX incorporated for better visualization.
```
tensorboard --logdir=run1:$EXP1,run2:$EXP2 --port=6789
```
Other:
- Resources: GoogleDrive LINK contains shared models, visual predictions and data lists.
- Models: ImageNet pre-trained models and trained segmentation models can be accessed. Note that our ImageNet pretrained models are slightly different from original ResNet implementation in the beginning part.
- Predictions: Visual predictions of several models can be accessed.
- Datasets: attributes (names and colors) are in folder dataset and some sample lists can be accessed.
- Some FAQs: FAQ.md.
- Former video predictions: high accuracy -- PSPNet, PSANet; high efficiency -- ICNet.

Performance

Description: mIoU/mAcc/aAcc stands for mean IoU, mean accuracy of each class and all pixel accuracy respectively. ss denotes single scale testing and ms indicates multi-scale testing. Training time is measured on a sever with 8 GeForce RTX 2080 Ti. General parameters cross different datasets are listed below:

Train Parameters: sync_bn(True), scale_min(0.5), scale_max(2.0), rotate_min(-10), rotate_max(10), zoom_factor(8), ignore_label(255), aux_weight(0.4), batch_size(16), base_lr(1e-2), power(0.9), momentum(0.9), weight_decay(1e-4).
Test Parameters: ignore_label(255), scales(single: [1.0], multiple: [0.5 0.75 1.0 1.25 1.5 1.75]).

ADE20K: Train Parameters: classes(150), train_h(473/465-PSP/A), train_w(473/465-PSP/A), epochs(100). Test Parameters: classes(150), test_h(473/465-PSP/A), test_w(473/465-PSP/A), base_size(512).

Setting: train on train (20210 images) set and test on val (2000 images) set.

Network	mIoU/mAcc/aAcc(ss)	mIoU/mAcc/pAcc(ms)	Training Time
PSPNet50	0.4189/0.5227/0.8039.	0.4284/0.5266/0.8106.	14h
PSANet50	0.4229/0.5307/0.8032.	0.4305/0.5312/0.8101.	14h
PSPNet101	0.4310/0.5375/0.8107.	0.4415/0.5426/0.8172.	20h
PSANet101	0.4337/0.5385/0.8102.	0.4414/0.5392/0.8170.	20h

PSACAL VOC 2012: Train Parameters: classes(21), train_h(473/465-PSP/A), train_w(473/465-PSP/A), epochs(50). Test Parameters: classes(21), test_h(473/465-PSP/A), test_w(473/465-PSP/A), base_size(512).

Setting: train on train_aug (10582 images) set and test on val (1449 images) set.

Network	mIoU/mAcc/aAcc(ss)	mIoU/mAcc/pAcc(ms)	Training Time
PSPNet50	0.7705/0.8513/0.9489.	0.7802/0.8580/0.9513.	3.3h
PSANet50	0.7725/0.8569/0.9491.	0.7787/0.8606/0.9508.	3.3h
PSPNet101	0.7907/0.8636/0.9534.	0.7963/0.8677/0.9550.	5h
PSANet101	0.7870/0.8642/0.9528.	0.7966/0.8696/0.9549.	5h

Cityscapes: Train Parameters: classes(19), train_h(713/709-PSP/A), train_w(713/709-PSP/A), epochs(200). Test Parameters: classes(19), test_h(713/709-PSP/A), test_w(713/709-PSP/A), base_size(2048).

Setting: train on fine_train (2975 images) set and test on fine_val (500 images) set.

Network	mIoU/mAcc/aAcc(ss)	mIoU/mAcc/pAcc(ms)	Training Time
PSPNet50	0.7730/0.8431/0.9597.	0.7838/0.8486/0.9617.	7h
PSANet50	0.7745/0.8461/0.9600.	0.7818/0.8487/0.9622.	7.5h
PSPNet101	0.7863/0.8577/0.9614.	0.7929/0.8591/0.9638.	10h
PSANet101	0.7842/0.8599/0.9621.	0.7940/0.8631/0.9644.	10.5h

Citation

If you find the code or trained models useful, please consider citing:

@misc{semseg2019,
  author={Zhao, Hengshuang},
  title={semseg},
  howpublished={\url{https://github.com/hszhao/semseg}},
  year={2019}
}
@inproceedings{zhao2017pspnet,
  title={Pyramid Scene Parsing Network},
  author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
  booktitle={CVPR},
  year={2017}
}
@inproceedings{zhao2018psanet,
  title={{PSANet}: Point-wise Spatial Attention Network for Scene Parsing},
  author={Zhao, Hengshuang and Zhang, Yi and Liu, Shu and Shi, Jianping and Loy, Chen Change and Lin, Dahua and Jia, Jiaya},
  booktitle={ECCV},
  year={2018}
}

Question