# CLRNet
**Repository Path**: leeyueleeyue/CLRNet
## Basic Information
- **Project Name**: CLRNet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: deploy
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-04-22
- **Last Updated**: 2025-04-22
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://paperswithcode.com/sota/lane-detection-on-culane?p=clrnet-cross-layer-refinement-network-for)
[](https://paperswithcode.com/sota/lane-detection-on-llamas?p=clrnet-cross-layer-refinement-network-for)
[](https://paperswithcode.com/sota/lane-detection-on-tusimple?p=clrnet-cross-layer-refinement-network-for)
# CLRNet: Cross Layer Refinement Network for Lane Detection
Pytorch implementation of the paper "[CLRNet: Cross Layer Refinement Network for Lane Detection](https://arxiv.org/abs/2203.10350)" (CVPR2022 Acceptance).
## Introduction
- CLRNet exploits more contextual information to detect lanes while leveraging local detailed lane features to improve localization accuracy.
- CLRNet achieves SOTA result on CULane, Tusimple, and LLAMAS datasets.
## Installation
### Prerequisites
Only test on Ubuntu18.04 and 20.04 with:
- Python >= 3.8 (tested with Python3.8)
- PyTorch >= 1.6 (tested with Pytorch1.6)
- CUDA (tested with cuda10.2)
- Other dependencies described in `requirements.txt`
### Clone this repository
Clone this code to your workspace.
We call this directory as `$CLRNET_ROOT`
```Shell
git clone https://github.com/Turoad/clrnet
```
### Create a conda virtual environment and activate it (conda is optional)
```Shell
conda create -n clrnet python=3.8 -y
conda activate clrnet
```
### Install dependencies
```Shell
# Install pytorch firstly, the cudatoolkit version should be same in your system.
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
# Or you can install via pip
pip install torch==1.8.0 torchvision==0.9.0
# Install python packages
python setup.py build develop
```
### Use docker to run CLRNet (recommended).
We recommend to use docker for deployment. If you have not installed docker, see https://docs.docker.com/.
You can pull our pre-build docker image for convenience:
```
docker pull turoad/clrnet:torch1.13-tensorrt8.5
```
Another pre-build docker image is `turoad/clrnet:torch1.8-tensorrt7.2`.
a. You can start the docker with:
```
./docker_scripts/docker_start.sh
```
**Note**, make sure your docker tag is modified in `docker_scripts/env.sh`.
b. You can enter into the docker with:
```
./docker_scripts/docker_into.sh
```
This will create a new bash session in the container.
c. Build for CLRNet:
```
cd $CLRNET_ROOT
python setup.py build develop
```
### Data preparation
#### CULane
Download [CULane](https://xingangpan.github.io/projects/CULane.html). Then extract them to `$CULANEROOT`. Create link to `data` directory.
```Shell
cd $CLRNET_ROOT
mkdir -p data
ln -s $CULANEROOT data/CULane
```
For CULane, you should have structure like this:
```
$CULANEROOT/driver_xx_xxframe # data folders x6
$CULANEROOT/laneseg_label_w16 # lane segmentation labels
$CULANEROOT/list # data lists
```
#### Tusimple
Download [Tusimple](https://github.com/TuSimple/tusimple-benchmark/issues/3). Then extract them to `$TUSIMPLEROOT`. Create link to `data` directory.
```Shell
cd $CLRNET_ROOT
mkdir -p data
ln -s $TUSIMPLEROOT data/tusimple
```
For Tusimple, you should have structure like this:
```
$TUSIMPLEROOT/clips # data folders
$TUSIMPLEROOT/lable_data_xxxx.json # label json file x4
$TUSIMPLEROOT/test_tasks_0627.json # test tasks json file
$TUSIMPLEROOT/test_label.json # test label json file
```
For Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation.
```Shell
python tools/generate_seg_tusimple.py --root $TUSIMPLEROOT
# this will generate seg_label directory
```
#### LLAMAS
Dowload [LLAMAS](https://unsupervised-llamas.com/llamas/). Then extract them to `$LLAMASROOT`. Create link to `data` directory.
```Shell
cd $CLRNET_ROOT
mkdir -p data
ln -s $LLAMASROOT data/llamas
```
Unzip both files (`color_images.zip` and `labels.zip`) into the same directory (e.g., `data/llamas/`), which will be the dataset's root. For LLAMAS, you should have structure like this:
```
$LLAMASROOT/color_images/train # data folders
$LLAMASROOT/color_images/test # data folders
$LLAMASROOT/color_images/valid # data folders
$LLAMASROOT/labels/train # labels folders
$LLAMASROOT/labels/valid # labels folders
```
## Getting Started
### Training
For training, run
```Shell
python main.py [configs/path_to_your_config] --gpus [gpu_num]
```
For example, run
```Shell
python main.py configs/clrnet/clr_resnet18_culane.py --gpus 0
```
### Validation
For testing, run
```Shell
python main.py [configs/path_to_your_config] --[test|validate] --load_from [path_to_your_model] --gpus [gpu_num]
```
For example, run
```Shell
python main.py configs/clrnet/clr_dla34_culane.py --validate --load_from culane_dla34.pth --gpus 0
```
Currently, this code can output the visualization result when testing, just add `--view`.
We will get the visualization result in `work_dirs/xxx/xxx/visualization`.
## Results
[assets]: https://github.com/turoad/CLRNet/releases
### CULane
| Backbone | mF1 | F1@50 | F1@75 |
| :--- | :---: | :---: | :---:|
| [ResNet-18][assets] | 55.23 | 79.58 | 62.21 |
| [ResNet-34][assets] | 55.14 | 79.73 | 62.11 |
| [ResNet-101][assets] | 55.55| 80.13 | 62.96 |
| [DLA-34][assets] | 55.64| 80.47 | 62.78 |
### TuSimple
| Backbone | F1 | Acc | FDR | FNR |
| :--- | ---: | ---: | ---: | ---: |
| [ResNet-18][assets] | 97.89 | 96.84 | 2.28 | 1.92 |
| [ResNet-34][assets] | 97.82 | 96.87 | 2.27 | 2.08 |
| [ResNet-101][assets] | 97.62| 96.83 | 2.37 | 2.38 |
### LLAMAS
| Backbone | valid
mF1 F1@50 F1@75 | test
F1@50 |
| :---: | :---: | :---:|
| [ResNet-18][assets] | 70.83 96.93 85.23 | 96.00 |
| [DLA-34][assets] | 71.57 97.06 85.43 | 96.12 |
“F1@50” refers to the official metric, i.e., F1 score when IoU threshold is 0.5 between the gt and prediction. "F1@75" is the F1 score when IoU threshold is 0.75.
## Citation
If our paper and code are beneficial to your work, please consider citing:
```
@InProceedings{Zheng_2022_CVPR,
author = {Zheng, Tu and Huang, Yifei and Liu, Yang and Tang, Wenjian and Yang, Zheng and Cai, Deng and He, Xiaofei},
title = {CLRNet: Cross Layer Refinement Network for Lane Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {898-907}
}
```
## Acknowledgement
* [open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection)
* [pytorch/vision](https://github.com/pytorch/vision)
* [Turoad/lanedet](https://github.com/Turoad/lanedet)
* [ZJULearning/resa](https://github.com/ZJULearning/resa)
* [cfzd/Ultra-Fast-Lane-Detection](https://github.com/cfzd/Ultra-Fast-Lane-Detection)
* [lucastabelini/LaneATT](https://github.com/lucastabelini/LaneATT)
* [aliyun/conditional-lane-detection](https://github.com/aliyun/conditional-lane-detection)