# GALD-DGCNet

**Repository Path**: Felix660/GALD-DGCNet

## Basic Information

- **Project Name**: GALD-DGCNet
- **Description**: Source  code and models GALD net (BMVC-2019)and Dual-Seg Net (BMVC-2019)
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-07-27
- **Last Updated**: 2024-11-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# GALD-Net-v2 (TIP-2021)

Note that our GALD-v2 (improved version of GALD-v1) has been accept by TIP-2021! It achieves 83.5 mIoU using ResNet101 backbone!.


# GALD-Net & Dual-Seg Net （BMVC-2019）
This is PyTorch re-implementation of GALD-net and Dual-Seg.
Both papers were accepted by BMVC-2019 and achieve state-of-the-art results on the Cityscapes and Pascal Context datasets.

# High Performance Road Scene Semantic Segmentaion :tada:


There is also a co-current repo for Fast Road Scene Semantic Segmentation:[Fast_Seg :zap: ](https://github.com/lxtGH/Fast_Seg) and thanks for your attention :smiley:

## GALDNet
![avatar](./fig/gald.jpeg)

## DualGCNSegNet
![avatar](./fig/dual_seg.jpeg)


# Training & Validation

## Requirements

pytorch >= 1.1.0
apex
opencv-python

## Pretrained Model
Baidu Pan Link: https://pan.baidu.com/s/1MWzpkI3PwtnEl1LSOyLrLw  passwd: 4lwf
Google Drive Link: https://drive.google.com/file/d/1JlERBWT8fHvf-uD36k5-LRZ5taqUbraj/view?usp=sharing, https://drive.google.com/file/d/1gGzz_6ZHUSC4A3SO0yg8-uLE0iiPdO4H/view?usp=sharing

## Training 
Note that we use apex to speed up training process. 
At least 8 gpus with 12GB are needed since we need batch size at least 8 and crop size at least 800 on Cityscapes dataset.
Please see [`train_distribute.py`](train_distribute.py) for the details. 

```bash
sh ./exp/train_dual_seg_r50_city_finetrain.sh
```
You will get the model with 79.6~79.8 mIoU.

```bash
sh ./exp/train_dual_seg_r101_city_finetrain.sh
```
You will get the model with 80.3~80.4 mIoU.

## Validation

```bash
sh ./exp/tes_dualseg_r50_city_finetrain.sh
```

# Trained Model
Model trained with the Cityscapes fine dataset: 

Dual-Seg-net: [ResNet 50](https://drive.google.com/file/d/13HnKtJLyCVK7-e0OtQeLrsJub4IO2t0R/view?usp=sharing), [ResNet 101](https://drive.google.com/file/d/1xgePAXsXGummOfTo3XFPKUoe5-1QZcIy/view?usp=sharing)

# Some Advice on Training
Please see the [Common.md](Common.md) for the details for using the coarse data training. 
Or you can refer to our GLAD paper(last part) for reference.


## GALD-Net (BMVC 2019,[arxiv](https://arxiv.org/pdf/1909.07229.pdf))
We propose Global Aggregation then Local Distribution (GALD) scheme to distribute global information to each position adaptively according to the local information around the position. GALD net achieves **top performance** on Cityscapes dataset. Both source code and models will be available soon. The work was done at [DeepMotion AI Research](https://deepmotion.ai/) 

## DGCNet (BMVC 2019,[arxiv](https://arxiv.org/abs/1909.06121)) 
We propose Dual Graph Convolutional Network (DGCNet) to model the global context of the input feature by modelling two orthogonal graphs in a single framework. (Joint work: University of Oxford, Peking University and DeepMotion AI Research)



##  Comparisons with state-of-the-art models on Cityscapes dataset 
Method | Conference | Backbone | mIoU(\%) 
---- | ---- | ---- | ----
RefineNet |  CVPR2017  | ResNet-101  |  73.6 
SAC  |  ICCV2017  | ResNet-101  |  78.1 
PSPNet |  CVPR2017  | ResNet-101  |  78.4
DUC-HDC | WACV2018 | ResNet-101 | 77.6 
AAF |   ECCV2018  | ResNet-101  |  77.1 
BiSeNet |   ECCV2018  | ResNet-101  |  78.9 
PSANet |  ECCV2018  | ResNet-101  |  80.1 
DFN  |  CVPR2018  | ResNet-101  |  79.3 
DSSPN | CVPR2018  | ResNet-101  | 77.8 
DenseASPP  |  CVPR2018  | DenseNet-161  |  80.6
OCNet| - |  ResNet-101 | 81.7 
CCNet| ICCV2019 | ResNet-101 | 81.4
GALD-Net | BMVC2019 | ResNet50 |**80.8**
GALD-Net | BMVC2019| ResNet101 |**81.8**
DGCN-Net | BMVC2019 | ResNet101 | **82.0**
GALD-Net(use coarse data) |BMVC2019 | ResNet101 |**82.9**
GALD-NetV2(use coarse data) |TIP2021 | ResNet101 |**83.5**
GALD-Net(use Mapillary)|BMVC2019 |ResNet101| **83.3**


## Detailed Results are shown 
GALD-Net:
[here](https://www.cityscapes-dataset.com/anonymous-results/?id=5ee0f5098e160aa56db6e9ed01c5fbc73d4ac736b6b61751b50ad31067b0d5bd)   
GFF-Net:[here](https://www.cityscapes-dataset.com/method-details/?submissionID=3719)  
Both are (**Single Model Result**)  


# Citation 
Please refer our paper for more detail. 
If you find the codebase useful, please consider citing our paper.
```
@inproceedings{xiangtl_gald
title={Global Aggregation then Local Distribution in Fully Convolutional Networks},
author={Li, Xiangtai and Zhang, Li and You, Ansheng and Yang, Maoke and Yang, Kuiyuan and Tong, Yunhai},
booktitle={BMVC2019},
}
```

```
@inproceedings{zhangli_dgcn
title={Dual Graph Convolutional Network for Semantic Segmentation},
author={Zhang, Li(*) and Li, Xiangtai(*) and Arnab, Anurag and Yang, Kuiyuan and Tong, Yunhai and Torr, Philip HS},
booktitle={BMVC2019},
}
```

# License
MIT License


# Acknowledgement

Thanks to previous open-sourced repo:  
[Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)    
[CCNet](https://github.com/speedinghzl/CCNet)   
[TorchSeg](https://github.com/ycszen/TorchSeg)  
[pytorchseg](https://github.com/meetshah1995/pytorch-semseg)