# DDRNet.pytorch

**Repository Path**: kingkol/DDRNet.pytorch

## Basic Information

- **Project Name**: DDRNet.pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-26
- **Last Updated**: 2024-09-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

## Introduction
This is the unofficial code of [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://arxiv.org/pdf/2101.06085.pdf). which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data!on single 2080Ti GPU, DDRNet-23-slim yields 77.4% mIoU at 109 FPS on Cityscapes test set and 74.4% mIoU at 230 FPS on CamVid test set.

The code mainly borrows from [HRNet-Semantic-Segmentation OCR](https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/HRNet-OCR) and [the official repository](https://github.com/ydhongHIT/DDRNet), thanks for their work.


| 类别                       | 标签  | 像素类 |
| -------------------------- | ----- | ------ |
| 实线                       | S0001 | 1      |
| 虚线                       | S0002 | 2      |
| 提示前方直行               | S0075 | 3      |
| 提示前方左转               | S0077 | 4      |
| 提示前方右转               | S0078 | 5      |
| 提示前方可直行或左转       | S0076 | 6      |
| 提示前方可直行或右转       | S0079 | 7      |
| 提示直行或左转或右转       | S0106 | 8      |
| 提示前方左转或右转         | S0083 | 9      |
| 提示前方掉头               | S0080 | 10     |
| 提示前方有左弯或需向左合流 | S0084 | 11     |
| 提示前方有右弯或需向右合流 | S0085 | 12     |
| 人行横道                   | S0088 | 13     |
| 停止线（单实线）           | S0089 | 14     |
| 停车让行线（双实线）       | S0090 | 15     |
| 减速让行线（双虚线）       | S0091 | 16     |
| 减速带                     | S0092 | 17     |
</br></br></br>


<!-- ![](figures/ddrnet.png) -->
<figure>
  <text-align: center;>
  <center>
  <img src="./figures/ddrnet.png" alt="hrnet" title="" width="400" height="400" />
  </center>
</figcaption>
</figure>

## requirements
Here I list the software and hardware used in my experiment
- pytorch==1.7.0 [conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia]
- 3080*2
- cuda==11.1

## Quick start

### 0. Data preparation

You need to download the [Cityscapes](https://www.cityscapes-dataset.com/)datasets. and rename the folder `cityscapes`, then put the data under `data` folder. 
```
└── data
  ├── cityscapes
  └── list
```

### 1. Pretrained model

download the pretrained model on imagenet or the segmentation model from the [official](https://github.com/ydhongHIT/DDRNet)，and put the files in `${PROJECT}/pretrained_models` folder


## VAL

use the [official pretrained model](https://github.com/ydhongHIT/DDRNet) and our `eval.py` code. with [ydhongHIT's](https://github.com/ydhongHIT) advice now can reach the same accuracy in the paper. Thanks.

```python
cd ${PROJECT}
python tools/eval.py --cfg experiments/cityscapes/ddrnet23_slim.yaml
```

|     model     | Train Set | Test Set | OHEM  | Multi-scale | Flip  | mIoU  |                      Link                       |
| :-----------: | :-------: | :------: | :---: | :---------: | :---: | :---: | :---------------------------------------------: |
| DDRNet23_slim |  unknown  |   eval   |  Yes  |     No      |  No   | 77.83 | [official](https://github.com/ydhongHIT/DDRNet) |
| DDRNet23_slim |  unknown  |   eval   |  Yes  |     No      |  Yes  | 78.42 | [official](https://github.com/ydhongHIT/DDRNet) |
|   DDRNet23    |  unknown  |   eval   |  Yes  |     No      |  No   | 79.51 | [official](https://github.com/ydhongHIT/DDRNet) |
|   DDRNet23    |  unknown  |   eval   |  Yes  |     No      |  Yes  | 79.98 | [official](https://github.com/ydhongHIT/DDRNet) |


**Note**
- with the `ALIGN_CORNERS: false` in `***.yaml` will reach higher accuracy.


## TRAIN

download [the imagenet pretrained model](https://github.com/ydhongHIT/DDRNet), and then train the model with 2 nvidia-3080

```python
cd ${PROJECT}
python -m torch.distributed.launch --nproc_per_node=2 tools/train.py --cfg experiments/cityscapes/ddrnet23_slim.yaml
```

**the own trained model coming soon**

## OWN model
|     model     | Train Set | Test Set | OHEM  | Multi-scale | Flip  | mIoU  |                                  Link                                  |
| :-----------: | :-------: | :------: | :---: | :---------: | :---: | :---: | :--------------------------------------------------------------------: |
| DDRNet23_slim |   train   |   eval   |  Yes  |     No      |  Yes  | 77.77 | [Baidu/password:it2s](https://pan.baidu.com/s/17pOOTc-HBG6TNf4k_cn4VA) |
| DDRNet23_slim |   train   |   eval   |  Yes  |     Yes     |  Yes  | 79.57 | [Baidu/password:it2s](https://pan.baidu.com/s/17pOOTc-HBG6TNf4k_cn4VA) |
|   DDRNet23    |   train   |   eval   |  Yes  |     No      |  Yes  |   ~   |                                  None                                  |
|   DDRNet39    |   train   |   eval   |  Yes  |     No      |  Yes  |   ~   |                                  None                                  |

**Note**
- set the `ALIGN_CORNERS: true` in `***.yaml`, because i use the default setting in [HRNet-Semantic-Segmentation OCR](https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/HRNet-OCR).
- Multi-scale with scales: 0.5,0.75,1.0,1.25,1.5,1.75. it runs too slow.
- from [ydhongHIT](https://github.com/ydhongHIT), can change the `align_corners=True` with better performance, the default option is `False`

## Reference
[1] [HRNet-Semantic-Segmentation OCR branch](https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/HRNet-OCR) 

[2] [the official repository](https://github.com/ydhongHIT/DDRNet)