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https://github.com/aliyun/conditional-lane-detection
https://github.com/aliyun/conditional-lane-detection
Last synced: 19 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/aliyun/conditional-lane-detection
- Owner: aliyun
- License: apache-2.0
- Created: 2021-05-25T12:54:17.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2023-10-13T03:39:32.000Z (about 1 year ago)
- Last Synced: 2024-08-01T05:13:15.621Z (4 months ago)
- Language: Python
- Size: 7.04 MB
- Stars: 383
- Watchers: 13
- Forks: 87
- Open Issues: 56
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
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](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.
## Installation
This implementation is based on mmdetection(v2.0.0). Please refer to [install.md](docs/install.md) for installation.
## Datasets
We conducted experiments on CurveLanes, CULane and TuSimple. Please refer to [dataset.md](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](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/curvelanes/curvelanes_small.pth)
curvelanes_medium | 109FPS | 85.92 | [download](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/curvelanes/curvelanes_medium.pth)
curvelanes_large | 48FPS | 86.10 | [download](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/curvelanes/curvelanes_large.pth)
culane_small | 220FPS | 78.14 | [download](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/culane/culane_small.pth)
culane_medium | 152FPS | 78.74 | [download](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/culane/culane_medium.pth)
culane_large | 58FPS | 79.48 | [download](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/culane/culane_large.pth)
tusimple_small | 220FPS | 97.01 | [download](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/tusimple/tusimple_small.pth)
tusimple_medium | 152FPS | 96.98 | [download](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/tusimple/tusimple_medium.pth)
tusimple_large | 58FPS | 97.24 | [download](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/CondLaneNet/models/tusimple/tusimple_large.pth)
## Testing
**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
``` bash
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
``` bash
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](https://github.com/XingangPan/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
``` bash
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](https://github.com/TuSimple/tusimple-benchmark) to evaluate the results.
**Speed Test**
``` bash
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:
``` bash
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**
