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)

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Semantic Segmentation on PyTorch

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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

DETAILS 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 1464 1449
VOCAug 11355 2857
ADK20K 20210 2000
Cityscapes 2975 500
COCO
SBU-shadow 4085 638
LIP(Look into Person) 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 for details.

.{SEG_ROOT}
├── tests
│   └── test_model.py

To Do

  • add train script
  • remove syncbn
  • train & evaluate
  • test distributed training
  • fix syncbn (Why SyncBN?)
  • add distributed (How DIST?)

References

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About

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

License:Apache License 2.0


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