# SegmenTron

**Repository Path**: wangdong_cn_admin/SegmenTron

## Basic Information

- **Project Name**: SegmenTron
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-14
- **Last Updated**: 2021-03-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PyTorch for Semantic Segmentation
## Introduce
This repository contains some models for semantic segmentation and the pipeline of training and testing models, 
implemented in PyTorch.

![](docs/images/demo.png)

## Model zoo

|Model|Backbone|Datasets|eval size|Mean IoU(paper)|Mean IoU(this repo)|
|:-:|:-:|:-:|:-:|:-:|:-:|
|DeepLabv3_plus|xception65|cityscape(val)|(1025,2049)|78.8|[78.93](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/deeplabv3_plus_xception_segmentron.pth)|
|DeepLabv3_plus|xception65|coco(val)|480/520|-|[70.50](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/deeplabv3_plus_xception_coco_segmentron.pth)|
|DeepLabv3_plus|xception65|pascal_aug(val)|480/520|-|[89.56](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/deeplabv3_plus_xception_pascal_aug_segmentron.pth)|
|DeepLabv3_plus|xception65|pascal_voc(val)|480/520|-|[88.39](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/deeplabv3_plus_xception_pascal_voc_segmentron.pth)|
|DeepLabv3_plus|resnet101|cityscape(val)|(1025,2049)|-|[78.27](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/deeplabv3_plus_resnet101_segmentron.pth)|
|Danet|resnet101|cityscape(val)|(1024,2048)|79.9|[79.34](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/danet101_segmentron.pth)|
|Pspnet|resnet101|cityscape(val)|(1025,2049)|78.63|[77.00](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/pspnet_resnet101_segmentron.pth)|

### real-time models
Model|Backbone|Datasets|eval size|Mean IoU(paper)|Mean IoU(this repo)|FPS|
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
|ICnet|resnet50(0.5)|cityscape(val)|(1024,2048)|67.8|-|41.39|
|DeepLabv3_plus|mobilenetV2|cityscape(val)|(1024,2048)|70.7|[70.3](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/deeplabv3_plus_mobilenetv2_segmentron.pth)|46.64|
|BiSeNet|resnet18|cityscape(val)|(1024,2048)|-|-|39.90|
|LEDNet|-|cityscape(val)|(1024,2048)|-|-|31.78|
|CGNet|-|cityscape(val)|(1024,2048)|-|-|46.11|
|HardNet|-|cityscape(val)|(1024,2048)|75.9|-|69.06|
|DFANet|xceptionA|cityscape(val)|(1024,2048)|70.3|-|21.46|
|HRNet|w18_small_v1|cityscape(val)|(1024,2048)|70.3|[70.5](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/hrnet_w18_small_v1_segmentron.pth)|66.01|
|Fast_SCNN|-|cityscape(val)|(1024,2048)|68.3|[68.9](https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/fast_scnn_segmentron.pth)|145.77|

FPS was tested on V100.

## Environments

- python 3
- torch >= 1.1.0
- torchvision
- pyyaml
- Pillow
- numpy

## INSTALL

```
python setup.py develop
```

if you do not want to run CCNet, you do not need to install, just comment following line in ```segmentron/models/__init__.py```
```
from .ccnet import CCNet
```
## Dataset prepare
Support cityscape, coco, voc, ade20k now.

Please refer to [DATA_PREPARE.md](docs/DATA_PREPARE.md) for dataset preparation.

## Pretrained backbone models 

pretrained backbone models will be download automatically in pytorch default directory(```~/.cache/torch/checkpoints/```).

## Code structure
```
├── configs    # yaml config file
├── segmentron # core code
├── tools      # train eval code
└── datasets   # put datasets here 
```

## Train
### Train with a single GPU
```
CUDA_VISIBLE_DEVICES=0 python -u tools/train.py --config-file configs/cityscapes_deeplabv3_plus.yaml
```
### Train with multiple GPUs
```
CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
```

## Eval
### Eval with a single GPU
You can download trained model from model zoo table above, or train by yourself.
```
CUDA_VISIBLE_DEVICES=0 python -u ./tools/eval.py --config-file configs/cityscapes_deeplabv3_plus.yaml \
TEST.TEST_MODEL_PATH your_test_model_path

```
### Eval with a multiple GPUs
```
CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_test.sh ${CONFIG_FILE} ${GPU_NUM} \
TEST.TEST_MODEL_PATH your_test_model_path
```

## References
- [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)
- [detectron2](https://github.com/facebookresearch/detectron2)
- [gloun-cv](https://github.com/dmlc/gluon-cv)