damonmin / ultra-fast-lane-detection

Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020)

Ultra-Fast-Lane-Detection

The implementation of the paper "Ultra Fast Structure-aware Deep Lane Detection".

Updates: Our paper has been accepted by ECCV2020.

The evaluation code is modified from SCNN and Tusimple Benchmark.

Demo

Install

Please see INSTALL.md

Get Started

Copy a config from configs/culane.py or configs/tusimple.py, then modifiy data_root, log_path and other settings in your config.

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For single gpu training, run

python train.py configs/path_to_your_config

For multi-gpu training, run

sh launch_training.sh

or

python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py configs/path_to_your_config

If there is no pretrained torchvision model, multi-gpu training may result in multiple downloading. You can first download the corresponding models manually, and then restart the multi-gpu training.

Since our code has auto backup function which will copy all codes to the log_path according to the gitignore, additional temp file might also be copied if it is not filtered by gitignore, which may block the execution if the temp files are large. So you should keep the working directory clean.

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Besides config style settings, we also support command line style one. You can override a setting like

python train.py configs/path_to_your_config --batch_size 8

The batch_size will be set to 8 during training.

---

To visualize the log with tensorboard, run

tensorboard --logdir log_path --bind_all

Trained models

We provide two trained Res-18 models on CULane and Tusimple.

Dataset	Metric paper	Metric This repo	Avg FPS on GTX 1080Ti	Model
Tusimple	95.87	95.82	306	GoogleDrive/BaiduDrive(code:bghd)
CULane	68.4	69.7	324	GoogleDrive/BaiduDrive(code:w9tw)

For evaluation, run

mkdir tmp
# This a bad example, you should put the temp files outside the project.

python test.py configs/culane.py --test_model path_to_culane_18.pth --test_work_dir ./tmp

python test.py configs/tusimple.py --test_model path_to_tusimple_18.pth --test_work_dir ./tmp

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

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}
}