feat: initial HSAP platform
Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
211
algorithms/lane_ufld/code/CLRNet-main/README.md
Normal file
211
algorithms/lane_ufld/code/CLRNet-main/README.md
Normal file
@@ -0,0 +1,211 @@
|
||||
<div align="center">
|
||||
|
||||
[](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)
|
||||
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
<div align="center">
|
||||
|
||||
# CLRNet: Cross Layer Refinement Network for Lane Detection
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
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 `$CLRNET_ROOT`
|
||||
```Shell
|
||||
git clone https://github.com/Turoad/clrnet
|
||||
```
|
||||
|
||||
### Create a conda virtual environment and activate it (conda is optional)
|
||||
|
||||
```Shell
|
||||
conda create -n clrnet python=3.8 -y
|
||||
conda activate clrnet
|
||||
```
|
||||
|
||||
### Install dependencies
|
||||
|
||||
```Shell
|
||||
# Install pytorch firstly, the cudatoolkit version should be same in your system.
|
||||
|
||||
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
|
||||
|
||||
# Or you can install via pip
|
||||
pip install torch==1.8.0 torchvision==0.9.0
|
||||
|
||||
# Install python packages
|
||||
python setup.py build develop
|
||||
```
|
||||
|
||||
### Data preparation
|
||||
|
||||
#### CULane
|
||||
|
||||
Download [CULane](https://xingangpan.github.io/projects/CULane.html). Then extract them to `$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 | <center> valid <br><center> mF1 F1@50 F1@75 | <center> test <br> F1@50 |
|
||||
| :---: | :---: | :---:|
|
||||
| [ResNet-18][assets] | <center> 70.83 96.93 85.23 | 96.00 |
|
||||
| [DLA-34][assets] | <center> 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
|
||||
<!--ts-->
|
||||
* [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)
|
||||
<!--te-->
|
||||
Reference in New Issue
Block a user