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https://github.com/Tramac/awesome-semantic-segmentation-pytorch

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)
https://github.com/Tramac/awesome-semantic-segmentation-pytorch

List: awesome-semantic-segmentation-pytorch

pytorch semantic-segmentation

Last synced: 3 months ago
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Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

Host: GitHub
URL: https://github.com/Tramac/awesome-semantic-segmentation-pytorch
Owner: Tramac
License: apache-2.0
Created: 2019-03-11T01:33:25.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2023-01-04T08:24:27.000Z (over 1 year ago)
Last Synced: 2024-04-14T12:08:28.303Z (3 months ago)
Topics: pytorch, semantic-segmentation
Language: Python
Homepage:
Size: 1.71 MB
Stars: 2,725
Watchers: 43
Forks: 579
Open Issues: 118
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-stars - Tramac/awesome-semantic-segmentation-pytorch - Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet) (Python)
awesome-of-awesome-ml - awesome-semantic-segmentation-pytorch (by Tramac)
awesome-semantic-segmentation - Tramac/awesome-semantic-segmentation-pytorch
ultimate-awesome - awesome-semantic-segmentation-pytorch - Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet). (Other Lists / Julia Lists)

README

        # Semantic Segmentation on PyTorch

English | [简体中文](/README_zh-CN.md)

[![python-image]][python-url]

[![pytorch-image]][pytorch-url]

[![lic-image]][lic-url]

This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.



## Installation

```

# semantic-segmentation-pytorch dependencies

pip install ninja tqdm

# follow PyTorch installation in https://pytorch.org/get-started/locally/

conda install pytorch torchvision -c pytorch

# install PyTorch Segmentation

git clone https://github.com/Tramac/awesome-semantic-segmentation-pytorch.git

```

## Usage

### Train

-----------------

- **Single GPU training**

```

# for example, train fcn32_vgg16_pascal_voc:

python train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50

```

- **Multi-GPU training**

```

# for example, train fcn32_vgg16_pascal_voc with 4 GPUs:

export NGPUS=4

python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50

```

### Evaluation

-----------------

- **Single GPU evaluating**

```

# for example, evaluate fcn32_vgg16_pascal_voc

python eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc

```

- **Multi-GPU evaluating**

```

# for example, evaluate fcn32_vgg16_pascal_voc with 4 GPUs:

export NGPUS=4

python -m torch.distributed.launch --nproc_per_node=$NGPUS eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc

```

### Demo

```

cd ./scripts

#for new users:

python demo.py --model fcn32s_vgg16_voc --input-pic ../tests/test_img.jpg

#you should add 'test.jpg' by yourself

python demo.py --model fcn32s_vgg16_voc --input-pic ../datasets/test.jpg

```

```

.{SEG_ROOT}

├── scripts

│   ├── demo.py

│   ├── eval.py

│   └── train.py

```

## Support

#### Model

- [FCN](https://arxiv.org/abs/1411.4038)

- [ENet](https://arxiv.org/pdf/1606.02147)

- [PSPNet](https://arxiv.org/pdf/1612.01105)

- [ICNet](https://arxiv.org/pdf/1704.08545)

- [DeepLabv3](https://arxiv.org/abs/1706.05587)

- [DeepLabv3+](https://arxiv.org/pdf/1802.02611)

- [DenseASPP](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf)

- [EncNet](https://arxiv.org/abs/1803.08904v1)

- [BiSeNet](https://arxiv.org/abs/1808.00897)

- [PSANet](https://hszhao.github.io/papers/eccv18_psanet.pdf)

- [DANet](https://arxiv.org/pdf/1809.02983)

- [OCNet](https://arxiv.org/pdf/1809.00916)

- [CGNet](https://arxiv.org/pdf/1811.08201)

- [ESPNetv2](https://arxiv.org/abs/1811.11431)

- [DUNet(DUpsampling)](https://arxiv.org/abs/1903.02120)

- [FastFCN(JPU)](https://arxiv.org/abs/1903.11816)

- [LEDNet](https://arxiv.org/abs/1905.02423)

- [Fast-SCNN](https://github.com/Tramac/Fast-SCNN-pytorch)

- [LightSeg](https://github.com/Tramac/Lightweight-Segmentation)

- [DFANet](https://arxiv.org/abs/1904.02216)

[DETAILS](https://github.com/Tramac/awesome-semantic-segmentation-pytorch/blob/master/docs/DETAILS.md) for model & backbone.

```

.{SEG_ROOT}

├── core

│   ├── models

│   │   ├── bisenet.py

│   │   ├── danet.py

│   │   ├── deeplabv3.py

│   │   ├── deeplabv3+.py

│   │   ├── denseaspp.py

│   │   ├── dunet.py

│   │   ├── encnet.py

│   │   ├── fcn.py

│   │   ├── pspnet.py

│   │   ├── icnet.py

│   │   ├── enet.py

│   │   ├── ocnet.py

│   │   ├── psanet.py

│   │   ├── cgnet.py

│   │   ├── espnet.py

│   │   ├── lednet.py

│   │   ├── dfanet.py

│   │   ├── ......

```

#### Dataset

You can run script to download dataset, such as:

```

cd ./core/data/downloader

python ade20k.py --download-dir ../datasets/ade

```

|                           Dataset                            | training set | validation set | testing set |

| :----------------------------------------------------------: | :----------: | :------------: | :---------: |

| [VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) |     1464     |      1449      |      ✘      |

| [VOCAug](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz) |    11355     |      2857      |      ✘      |

| [ADK20K](http://groups.csail.mit.edu/vision/datasets/ADE20K/) |    20210     |      2000      |      ✘      |

| [Cityscapes](https://www.cityscapes-dataset.com/downloads/)  |     2975     |      500       |      ✘      |

| [COCO](http://cocodataset.org/#download)           |              |                |             |

| [SBU-shadow](http://www3.cs.stonybrook.edu/~cvl/content/datasets/shadow_db/SBU-shadow.zip) |     4085     |      638       |      ✘      |

| [LIP(Look into Person)](http://sysu-hcp.net/lip/)       |    30462     |     10000      |    10000    |

```

.{SEG_ROOT}

├── core

│   ├── data

│   │   ├── dataloader

│   │   │   ├── ade.py

│   │   │   ├── cityscapes.py

│   │   │   ├── mscoco.py

│   │   │   ├── pascal_aug.py

│   │   │   ├── pascal_voc.py

│   │   │   ├── sbu_shadow.py

│   │   └── downloader

│   │       ├── ade20k.py

│   │       ├── cityscapes.py

│   │       ├── mscoco.py

│   │       ├── pascal_voc.py

│   │       └── sbu_shadow.py

```

## Result

- **PASCAL VOC 2012**

|Methods|Backbone|TrainSet|EvalSet|crops_size|epochs|JPU|Mean IoU|pixAcc|

|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|

|FCN32s|vgg16|train|val|480|60|✘|47.50|85.39|

|FCN16s|vgg16|train|val|480|60|✘|49.16|85.98|

|FCN8s|vgg16|train|val|480|60|✘|48.87|85.02|

|FCN32s|resnet50|train|val|480|50|✘|54.60|88.57|

|PSPNet|resnet50|train|val|480|60|✘|63.44|89.78|

|DeepLabv3|resnet50|train|val|480|60|✘|60.15|88.36|

Note: `lr=1e-4, batch_size=4, epochs=80`.

## Overfitting Test

See [TEST](https://github.com/Tramac/Awesome-semantic-segmentation-pytorch/tree/master/tests) for details.

```

.{SEG_ROOT}

├── tests

│   └── test_model.py

```

## To Do

- [x] add train script

- [ ] remove syncbn

- [ ] train & evaluate

- [x] test distributed training

- [x] fix syncbn ([Why SyncBN?](https://tramac.github.io/2019/02/25/%E8%B7%A8%E5%8D%A1%E5%90%8C%E6%AD%A5%20Batch%20Normalization[%E8%BD%AC]/))

- [x] add distributed ([How DIST?]("https://tramac.github.io/2019/03/06/%E5%88%86%E5%B8%83%E5%BC%8F%E8%AE%AD%E7%BB%83-PyTorch/"))

## References

- [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)

- [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark)

- [gloun-cv](https://github.com/dmlc/gluon-cv)

- [imagenet](https://github.com/pytorch/examples/tree/master/imagenet)

[python-image]: https://img.shields.io/badge/Python-2.x|3.x-ff69b4.svg

[python-url]: https://www.python.org/

[pytorch-image]: https://img.shields.io/badge/PyTorch-1.1-2BAF2B.svg

[pytorch-url]: https://pytorch.org/

[lic-image]: https://img.shields.io/badge/Apache-2.0-blue.svg

[lic-url]: https://github.com/Tramac/Awesome-semantic-segmentation-pytorch/blob/master/LICENSE