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https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks
Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)
https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks
camvid cityscapes computer-vision driving-scene-understanding efficient-segmentation-networks image-segmentation lightweight-semantic-segmentation neural-networks pytorch real-time-semantic-segmentation scene-understanding segmentation semantic-segmentation semantic-segmentation-models
Last synced: about 1 month ago
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Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)
- Host: GitHub
- URL: https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks
- Owner: xiaoyufenfei
- License: mit
- Created: 2019-10-07T03:16:07.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-07-25T10:16:24.000Z (5 months ago)
- Last Synced: 2024-08-01T20:36:39.732Z (4 months ago)
- Topics: camvid, cityscapes, computer-vision, driving-scene-understanding, efficient-segmentation-networks, image-segmentation, lightweight-semantic-segmentation, neural-networks, pytorch, real-time-semantic-segmentation, scene-understanding, segmentation, semantic-segmentation, semantic-segmentation-models
- Language: Python
- Homepage:
- Size: 1.12 MB
- Stars: 892
- Watchers: 20
- Forks: 163
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-computer-vision-papers - Efficient-Segmentation-Networks
README
## Efficient-Segmentation-Networks
[![python-image]][python-url]
[![pytorch-image]][pytorch-url]
This project aims at providing an easy-to-use, modifiable reference implementation for real-time semantic segmentation models using PyTorch.
---
### Table of Contents:
- Requirements
- Models
- Dataset Setting
- Usage
- Contact
### Requirements
[**PyTorch**](https://pytorch.org/) and [**Torchvision**](https://pytorch.org/) needs to be installed before running the scripts, PyTorch v1.1 or later is supported.
```bash
pip3 install -r requirements.txt
```
### Models
The project supports these semantic segmentation models as follows:
- (**SQNet**) Speeding up Semantic Segmentation for Autonomous Driving [[Paper]](https://openreview.net/pdf?id=S1uHiFyyg)
- (**LinkNet**) Exploiting Encoder Representations for Efficient Semantic Segmentation [[Paper]](https://arxiv.org/pdf/1707.03718.pdf)
- (**SegNet**) A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [[Paper]](https://arxiv.org/pdf/1511.00561.pdf)
- (**UNet**) Convolutional Networks for Biomedical Image Segmentation [[Paper]](https://arxiv.org/pdf/1505.04597.pdf)
- (**ENet**) A Deep Neural Network Architecture for Real-Time Semantic Segmentation [[Paper]](https://arxiv.org/pdf/1606.02147.pdf)
- (**ERFNet**) Efficient ConvNet for Real-time Semantic Segmentation [[Paper]](http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf)
- (**EDANet**) Efficient Dense Modules of Asymmetric Convolution for Real-Time Segmentation [[Paper]](https://arxiv.org/ftp/arxiv/papers/1809/1809.06323.pdf)
- (**ESPNet**) Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation [[Paper]](https://arxiv.org/pdf/1803.06815v2.pdf)
- (**ESPNetv2**) A Light-weight, Power Efficient, and General Purpose ConvNet [[Paper]](https://arxiv.org/pdf/1811.11431.pdf)
- (**LEDNet**) A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation [[Paper]](https://arxiv.org/pdf/1905.02423v3.pdf)
- (**FSSNet**) Fast Semantic Segmentation for Scene Perception [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8392426)
- (**ESNet**) An Efficient Symmetric Network for Real-time Semantic Segmentation [[Paper]](https://arxiv.org/pdf/1906.09826v1.pdf)
- (**CGNet**) A Light-weight Context Guided Network for Semantic Segmentation [[Paper]](https://arxiv.org/pdf/1811.08201.pdf)
- (**Fast-SCNN**) Fast Semantic Segmentation Network [[Paper]](https://arxiv.org/pdf/1902.04502.pdf)
- (**DABNet**) Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation [[Paper]](https://arxiv.org/pdf/1907.11357.pdf)
- (**ContextNet**) Exploring Context and Detail for Semantic Segmentation in Real-time [[Paper]](https://arxiv.org/pdf/1805.04554.pdf)
- (**FPENet**) Feature Pyramid Encoding Network for Real-time Semantic Segmentation [[Paper]](https://arxiv.org/pdf/1909.08599v1.pdf)
- ...
#### Losses
The project supports these loss functions:
> 1. Weighted Cross Entropy
> 2. Weighted Cross Entropy with Label Smooth
> 3. Focal Loss
> 4. Ohem Cross Entropy
> 5. [LovaszSoftmax](https://github.com/bermanmaxim/LovaszSoftmax)
> 6. [SegLoss-List](https://github.com/JunMa11/SegLoss)
> 7. ...
#### Optimizers
The project supports these optimizers:
> 1. SGD
> 2. Adam
> 3. AdamW
> 4. [RAdam](https://github.com/LiyuanLucasLiu/RAdam)
> 5. RAdam + Lookahead
> 6. ...
#### Activations
> 1. ReLu
> 2. PReLU
> 3. ReLU6
> 4. Swish
> 5. [Mish](https://github.com/digantamisra98/Mish) : A Self Regularized Non-Monotonic Neural Activation Function
> 6. ...
#### Learning Rate Scheduler
The project supports these LR_Schedulers:
> 1. Poly decay
> 2. Warmup Poly
> 3. ...
#### Normalization methods
> 1. [In-Place Activated BatchNorm](https://github.com/mapillary/inplace_abn)
> 2. [Switchable Normalization](https://github.com/switchablenorms/Switchable-Normalization)
> 3. [Weight Standardization](https://github.com/joe-siyuan-qiao/WeightStandardization)
> 4. ...
#### Enhancing Semantic Feature Learning Method
> 1. [Attention Family](https://github.com/implus/PytorchInsight)
> 2. [NAS Family](https://github.com/D-X-Y/NAS-Projects)
> 3. ...
#### Some useful Tools
> 1. [pytorch-OpCounter](https://github.com/Lyken17/pytorch-OpCounter)
> 2. [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch)
> 3. [Netron](https://github.com/lutzroeder/Netron) : Visualizer for neural network models, On line URL: [Netron](https://lutzroeder.github.io/netron/)
> 4. [Falshtorch](https://github.com/MisaOgura/flashtorch): Visualization toolkit for neural networks in PyTorch !
> 5. [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://github.com/weiaicunzai/Bag_of_Tricks_for_Image_Classification_with_Convolutional_Neural_Networks)
> 6. ...
### Dataset-Setting
This project has been tailored to suit the [Cityscapes](https://www.cityscapes-dataset.com/) and [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) datasets. The folds of your dataset need satisfy the following structures:
```
|-- dataset
| |-- camvid
| | |-- train
| | |-- trainannot
| | |-- val
| | |-- valannot
| | |-- test
| | |-- testannot
| | |-- ...
| |-- cityscapes
| | |-- leftImg8bit
| | | |-- train
| | | |-- val
| | | |-- test
| | |-- gtFine
| | | |-- train
| | | |-- val
| | | |-- test
| | |-- ...
```
- You can download [**cityscapes**](https://www.cityscapes-dataset.com/) dataset from [here](https://www.cityscapes-dataset.com/downloads/). Note: please download [leftImg8bit_trainvaltest.zip(11GB)](https://www.cityscapes-dataset.com/file-handling/?packageID=4) and [gtFine_trainvaltest(241MB)](https://www.cityscapes-dataset.com/file-handling/?packageID=1).
- You can download [**camvid**](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) dataset from [here](https://github.com/alexgkendall/SegNet-Tutorial/tree/master/CamVid).
- The **Cityscapes dataset scripts** for inspection, preparation, and evaluation can download from [here](https://github.com/mcordts/cityscapesScripts).
### Usage
#### Clone this Repo
```
git clone https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks
cd Efficient-Segmentation-Networks
```
Currently, the code supports [Python 3](https://www.python.org/)
Torch dependencies:
- [PyTorch](https://pytorch.org/) (>=1.1.0)
- torchvision(>=0.3.0)
Data dependencies:
- [Cityscapes](https://www.cityscapes-dataset.com/) + [scripts](https://github.com/mcordts/cityscapesScripts)
Download Cityscapes and run the script `createTrainIdLabelImgs.py` to create annotations based on the training labels. Make sure that the folder is named *cityscapes*
##### Training
- For Cityscapes / CamVid
1. training on **train** set
```
python train.py --help
```
2. training on **train+val** set
```
python train.py --help
```
##### Testing
- For Cityscapes / CamVid
```
python test.py --help
```
##### Predicting
- For Cityscapes
```
python predict.py --help
```
##### Evaluating
- For Cityscapes
```
cd tools
python trainID2labelID.py
```
### Contact
If you think this work useful, please give me a star! And if you find any errors or have any suggestions, please contact me.
**GitHub:** `xiaoyufenfei`
**Email:** `[email protected]`
### Refer to this Rep
You are encouraged to cite the following papers if this work helps your research.
```bash
@misc{Efficient-Segmentation-Networks,
author = {Yu Wang},
title = {Efficient-Segmentation-Networks Pytorch Implementation},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks}},
commit = {master}
}
```
### License
This project is released under the MIT License. See [LICENSE](https://github.com/xiaoyufenfei/Efficient-Segmentation-Networks/blob/master/LICENSE) for additional details.
[python-image]: https://img.shields.io/badge/Python-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/