huyhoang17 / Ultra-Fast-Lane-Detection-v2

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Ultra-Fast-Lane-Detection-V2

PyTorch implementation of the paper "Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification".

Demo

Demo

Install

Please see INSTALL.md

Get started

Please modify the data_root in any configs you would like to run. We will use configs/culane_res18.py as an example.

To train the model, you can run:

python train.py configs/culane_res18.py --log_path /path/to/your/work/dir

or

python -m torch.distributed.launch --nproc_per_node=8 train.py configs/culane_res18.py --log_path /path/to/your/work/dir

It should be noted that if you use different number of GPUs, the learning rate should be adjusted accordingly. The configs' learning rates correspond to 8-GPU training on CULane and CurveLanes datasets. If you want to train on CULane or CurveLanes with single GPU, please decrease the learning rate by a factor of 1/8. On the Tusimple, the learning rate corresponds to single GPU training.

Trained models

We provide trained models on CULane, Tusimple, and CurveLanes.

Dataset Backbone F1 Link
CULane ResNet18 75.0 Google/Baidu
CULane ResNet34 76.0 Google/Baidu
Tusimple ResNet18 96.11 Google/Baidu
Tusimple ResNet34 96.24 Google/Baidu
CurveLanes ResNet18 80.42 Google/Baidu
CurveLanes ResNet34 81.34 Google/Baidu

For evaluation, run

mkdir tmp

python test.py configs/culane_res18.py --test_model /path/to/your/model.pth --test_work_dir ./tmp

Same as training, multi-gpu evaluation is also supported.

mkdir tmp

python -m torch.distributed.launch --nproc_per_node=8 test.py configs/culane_res18.py --test_model /path/to/your/model.pth --test_work_dir ./tmp

Citation

@InProceedings{qin2020ultra,
author = {Qin, Zequn and Wang, Huanyu and Li, Xi},
title = {Ultra Fast Structure-aware Deep Lane Detection},
booktitle = {The European Conference on Computer Vision (ECCV)},
year = {2020}
}

@ARTICLE{qin2022ultrav2,
  author={Qin, Zequn and Zhang, Pengyi and Li, Xi},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Ultra Fast Deep Lane Detection With Hybrid Anchor Driven Ordinal Classification}, 
  year={2022},
  volume={},
  number={},
  pages={1-14},
  doi={10.1109/TPAMI.2022.3182097}
}

About

License:MIT License


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