Ultra-Fast-Lane-Detection-v2 pytorch版

# Ultra-Fast-Lane-Detection-V2 PyTorch implementation of the paper "[Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification](https://arxiv.org/abs/2206.07389)".  # Demo <a href="https://youtu.be/VkvpoHlaMe0 " target="_blank"><img src="http://img.youtube.com/vi/VkvpoHlaMe0/0.jpg" alt="Demo" width="240" height="180" border="10" /></a> # Install Please see [INSTALL.md](./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](https://drive.google.com/file/d/1oEjJraFr-3lxhX_OXduAGFWalWa6Xh3W/view?usp=sharing)/[Baidu](https://pan.baidu.com/s/1Z3W4y3eA9xrXJ51-voK4WQ?pwd=pdzs) | | CULane | ResNet34 | 76.0 | [Google](https://drive.google.com/file/d/1AjnvAD3qmqt_dGPveZJsLZ1bOyWv62Yj/view?usp=sharing)/[Baidu](https://pan.baidu.com/s/1PHNpVHboQlmpjM5NXl9IxQ?pwd=jw8f) | | Tusimple | ResNet18 | 96.11 | [Google](https://drive.google.com/file/d/1Clnj9-dLz81S3wXiYtlkc4HVusCb978t/view?usp=sharing)/[Baidu](https://pan.baidu.com/s/1umHo0RZIAQ1l_FzL2aZomw?pwd=6xs1) | | Tusimple | ResNet34 | 96.24 | [Google](https://drive.google.com/file/d/1pkz8homK433z39uStGK3ZWkDXrnBAMmX/view?usp=sharing)/[Baidu](https://pan.baidu.com/s/1Eq7oxnDoE0vcQGzs1VsGZQ?pwd=b88p) | | CurveLanes | ResNet18 | 80.42 | [Google](https://drive.google.com/file/d/1VfbUvorKKMG4tUePNbLYPp63axgd-8BX/view?usp=sharing)/[Baidu](https://pan.baidu.com/s/1jCqKqgSQdh6nwC5pYpYO1A?pwd=urhe) | | CurveLanes | ResNet34 | 81.34 | [Google](https://drive.google.com/file/d/1O1kPSr85Icl2JbwV3RBlxWZYhLEHo8EN/view?usp=sharing)/[Baidu](https://pan.baidu.com/s/1fk2Wg-1QoHXTnTlasSM6uQ?pwd=4mn3) | For evaluation, run ```Shell 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. ```Shell 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 ``` # Visualization We provide a script to visualize the detection results. Run the following commands to visualize on the testing set of CULane. ``` python demo.py configs/culane_res18.py --test_model /path/to/your/culane_res18.pth ``` # Tensorrt Deploy We also provide a python script to do tensorrt inference on videos. 1. Convert to onnx model ``` python deploy/pt2onnx.py --config_path configs/culane_res34.py --model_path weights/culane_res34.pth ``` Or you can download the onnx model using the following script: https://github.com/PINTO0309/PINTO_model_zoo/blob/main/324_Ultra-Fast-Lane-Detection-v2/download.sh. And copy `ufldv2_culane_res34_320x1600.onnx` to `weights/ufldv2_culane_res34_320x1600.onnx` 2. Convert to tensorrt model Use trtexec to convert engine model `trtexec --onnx=weights/culane_res34.onnx --saveEngine=weights/culane_res34.engine` 3. Do inference ``` python deploy/trt_infer.py --config_path configs/culane_res34.py --engine_path weights/culane_res34.engine --video_path example.mp4 ``` # Citation ```BibTeX @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} } ```