Tramac · Jeralyn123 · Mar 21, 2022 · Mar 21, 2022 · Mar 21, 2022 · Mar 21, 2022
diff --git a/README.md b/README.md
@@ -1,213 +1,49 @@
-# 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]
+# slightweight Segmentation
 
 This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.
 
-<p align="center"><img width="100%" src="docs/weimar_000091_000019_gtFine_color.png" /></p>
+stage 1: # Installation
 
-## Installation
-
-```
-# semantic-segmentation-pytorch dependencies
+stage 2: # dependencies
 pip install ninja tqdm
 
-# follow PyTorch installation in https://pytorch.org/get-started/locally/
+stage 3: # 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
-```
+stage 4: # for example, train swnet_resnet_citys:
+python train.py --model swnet --backbone resnet --dataset citys --lr 0.0001 --epochs 50
+
+
+stage 5: # for example, evaluate swnet_resnet_citys
+python eval.py --model swnet --backbone resnet --dataset citys
+
 ### Demo
-```
+
 cd ./scripts
 #for new users:
-python demo.py --model fcn32s_vgg16_voc --input-pic ../tests/test_img.jpg
+python demo.py --model swnet_resnet_citys --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
-│   │   ├── ......
-```
+python demo.py --model swnet_resnet_citys --input-pic ../datasets/test.jpg
+
+### performance evaluation
+
+![image](https://user-images.githubusercontent.com/43395674/159203398-86f4874e-7b0f-48a3-8414-cdf662d56f99.png)
+![image](https://user-images.githubusercontent.com/43395674/159203405-7b656176-2e93-4d67-98e6-6d650204b0d6.png)
+
+![image](https://user-images.githubusercontent.com/43395674/159203470-99a509cc-68cc-4fa4-be65-43e0c9204cb1.png)
+![image](https://user-images.githubusercontent.com/43395674/159203480-10ff8f81-965f-419c-ab98-83fade7b3b65.png)
+
+### a experiment on a simulative scene based on Jetson
+![image](https://user-images.githubusercontent.com/43395674/159203486-19980424-c6c4-4644-a44b-9f52085b2067.png)
+
 
 #### 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/"))
-<!--
-- [x] fix syncbn ([Why SyncBN?](https://tramac.github.io/2019/04/08/SyncBN/))
-- [x] add distributed ([How DIST?](https://tramac.github.io/2019/04/22/%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]][python-url]
-[![pytorch-image]][pytorch-url]
-[![lic-image]][lic-url]
--->
-
-[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
+
+Acknowledgement: we thanks the code support from "awesome-semantic-segmentation-pytorch (https://github.com/Tramac/Awesome-semantic-segmentation-pytorch)". The swnet is a improvement from enet.
+