This is the official implementation code of the paper "CondLaneNet: a Top-to-down Lane Detection Framework Based on ConditionalConvolution". (Link: https://arxiv.org/abs/2105.05003) We achieve state-of-the-art performance on multiple lane detection benchmarks. Our paper has been accepted by ICCV2021.
This implementation is based on mmdetection(v2.0.0). Please refer to install.md for installation.
We conducted experiments on CurveLanes, CULane and TuSimple. Please refer to dataset.md for installation.
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 |
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] --evaluateIf "--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]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 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
