This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.
- add FastFCN, ESPNet
- add distributed training (Note: I have no enough device to test distributed, If you are interested in it, welcome to complete testing and fix bugs.)
# 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
# the following will install the lib with symbolic links, so that you can modify
# the files if you want and won't need to re-build it
cd awesome-semantic-segmentation-pytorch/core/nn
python setup.py build develop
- 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=$NGPUS train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50
- Single GPU training
# for example, evaluate fcn32_vgg16_pascal_voc
python eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc
- Multi-GPU training
# for example, evaluate fcn32_vgg16_pascal_voc with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS --model fcn32s --backbone vgg16 --dataset pascal_voc
cd ./scripts
python demo.py --model fcn32s_vgg16_voc --input-pic ./datasets/test.jpg
.{SEG_ROOT}
├── scripts
│ ├── demo.py
│ ├── eval.py
│ └── train.py
- FCN
- ENet
- PSPNet
- ICNet
- DeepLabv3
- DenseASPP
- EncNet
- BiSeNet
- PSANet
- DANet
- OCNet
- CGNet
- ESPNetv2
- CCNet
- DUNet(DUpsampling)
- FastFCN(JPU)
- LEDNet
- Fast-SCNN
- LightSeg
DETAILS for model & backbone.
.{SEG_ROOT}
├── core
│ ├── models
│ │ ├── bisenet.py
│ │ ├── danet.py
│ │ ├── deeplabv3.py
│ │ ├── denseaspp.py
│ │ ├── dunet.py
│ │ ├── encnet.py
│ │ ├── fcn.py
│ │ ├── pspnet.py
│ │ ├── icnet.py
│ │ ├── enet.py
│ │ ├── ocnet.py
│ │ ├── ccnet.py
│ │ ├── psanet.py
│ │ ├── cgnet.py
│ │ ├── espnet.py
│ │ ├── lednet.py
│ │ ├── ......
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
- 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.
See TEST for details.
.{SEG_ROOT}
├── tests
│ └── test_model.py
- add train script
- add lightnet
- fix moved syncbn
- train & evaluate
- test distributed training
- fix syncbn (Why SyncBN?)
- add distributed (How DIST?)