CondLaneNet: a Top-to-down Lane Detection Framework Based on ConditionalConvolution

This is the official implementation code of the paper "CondLaneNet: a Top-to-down Lane Detection Framework Based on ConditionalConvolution". We achieve state-of-the-art performance on multiple lane detection benchmarks.

Installation

python setup.py develop

Datasets

We conducted experiments on CurveLanes, CULane and TuSimple. Please refer to dataset.md for installation.

Models

For your convenience, we provide the following trained models on Curvelanes, CULane, and TuSimple datasets

Model	Speed	F1	Link
curvelanes_small	154FPS	85.09	download
curvelanes_medium	109FPS	85.92	download
curvelanes_large	48FPS	86.10	download
culane_small	220FPS	78.14	download
culane_medium	152FPS	78.74	download
culane_large	58FPS	79.48	download
tusimple_small	220FPS	97.01	download
tusimple_medium	152FPS	96.98	download
tusimple_large	58FPS	97.24	download

Testing

Visualize Prediction

1 Edit the "data_root" in the config file to your Curvelanes dataset path. For example, for the small version, open "configs/curvelanes/curvelanes_small_test_custom.py" and set "data_root" to "[your-image-path]".

2 run the test script

cd [project-root]
python tools/condlanenet/curvelanes/vis_curvelanes.py configs/condlanenet/curvelanes/curvelanes_small_test_custom.py [model-path] --show_dst [output-path] 

CurveLanes

1 Edit the "data_root" in the config file to your Curvelanes dataset path. For example, for the small version, open "configs/curvelanes/curvelanes_small_test.py" and set "data_root" to "[your-data-path]/curvelanes".

2 run the test script

cd [project-root]
python tools/condlanenet/curvelanes/test_curvelanes.py configs/condlanenet/curvelanes/curvelanes_small_test.py [model-path] --evaluate

If "--evaluate" is added, the evaluation results will be printed. If you want to save the visualization results, you can add "--show" and add "--show_dst" to specify the save path.

CULane

1 Edit the "data_root" in the config file to your CULane dataset path. For example,for the small version, you should open "configs/culane/culane_small_test.py" and set the "data_root" to "[your-data-path]/culane".

2 run the test script

cd [project-root]
python tools/condlanenet/culane/test_culane.py configs/condlanenet/culane/culane_small_test.py [model-path]

• you can add "--show" and add "--show_dst" to specify the save path.
• you can add "--results_dst" to specify the result saving path.

3 We use the official evaluation tools of SCNN to evaluate the results.

TuSimple

1 Edit the "data_root" in the config file to your TuSimple dataset path. For example,for the small version, you should open "configs/tusimple/tusimple_small_test.py" and set the "data_root" to "[your-data-path]/tuSimple".

2 run the test script

cd [project-root]
python tools/condlanenet/tusimple/test_tusimple.py configs/condlanenet/tusimple/tusimple_small_test.py [model-path]

• you can add "--show" and add "--show_dst" to specify the save path.
• you can add "--results_dst" to specify the result saving path.

3 We use the official evaluation tools of TuSimple to evaluate the results.

Speed Test

cd [project-root]
python tools/condlanenet/speed_test.py configs/condlanenet/culane/culane_small_test.py [model-path]

Training

For example, train CULane using 4 gpus:

cd [project-root]
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29001 tools/dist_train.sh configs/condlanenet/culane/culane_small_train.py 4 --no-validate 

Results

CurveLanes

Model	F1	Speed	GFLOPS
Small(ResNet-18)	85.09	154FPS	10.3
Medium(ResNet-34)	85.92	109FPS	19.7
Large(ResNet-101)	86.10	48FPS	44.9

CULane

Model	F1	Speed	GFLOPS
Small(ResNet-18)	78.14	220FPS	10.2
Medium(ResNet-34)	78.74	152FPS	19.6
Large(ResNet-101)	79.48	58FPS	44.8

TuSimple

Model	F1	Speed	GFLOPS
Small(ResNet-18)	97.01	220FPS	10.2
Medium(ResNet-34)	96.98	152FPS	19.6
Large(ResNet-101)	97.24	58FPS	44.8

Visualization results