Authored by: Chenhui Gou u7194588, Zicheng Duan u7170273, Yunxiang Liu u7191378
This reporsitory is developed based on PytorchAutoDrive framework, we conduct all the exmpeiments based on this framwork.
Please follow the dataset and code intructions provided in: INSTALL.md. Then follow the instructions in DATASET.md to set up datasets. THIS IS VERY IMPORTANT!
We directly borrow the same training command and configuration to train the BézierLaneNet. However, we made specific modifications to fit our design. You can follow the steps below to reproduce our main experiments.
Train:
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet18_tusimple_RM_LKEA.py
Test:
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet18_tusimple_RM_LKEA.py
Run official script to obtain score:
./autotest_tusimple.sh resnet18_bezierlanenet_tusimple_LKEA_RM test checkpoints
Infer/Test:
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet18_tusimple_RM_LKEA_pretrained.py
Run official script to obtain pretrained model score:
./autotest_tusimple.sh resnet18_bezierlanenet_tusimple_RM_LKEA_pretrained test checkpoints
Training:
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet18_culane_RM_LKEA.py"
Testing:
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet18_culane_RM_LKEA.py"
Run official script to obtain score:
./autotest_culane.sh resnet18_bezierlanenet_culane_RM_LKEA test checkpoints
Train:
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet18_llamas_RM_LKEA.py
Validation:
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet18_llamas_RM_LKEA.py
Run official script to obtain score:
./autotest_llamas.sh resnet18_bezierlanenet_llamas_LKEA_RM test checkpoints
Infer/Val:
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet18_llamas_RM_LKEA_pretrained.py
Run official script to obtain pretrained model score:
./autotest_llamas.sh resnet18_bezierlanenet_llamas_RM_LKEA_pretrained test checkpoints
PytorchAutoDrive is a pure Python framework includes semantic segmentation models, lane detection models based on PyTorch. Here we provide full stack supports from research (model training, testing, fair benchmarking by simply writing configs) to application (visualization, model deployment).
Paper: Rethinking Efficient Lane Detection via Curve Modeling (CVPR 2022)
Poster: PytorchAutoDrive: Toolkit & Fair Benchmark for Autonomous Driving Research (PyTorch Developer Day 2021)
This repository is under active development, results with models uploaded are stable. For legacy code users, please check deprecations for changes.
A demo video from ERFNet:
demo_3.0.mp4
Various methods on a wide range of backbones, config based implementations, modulated and easily understood codes, image/keypoint loading, transformations and visualizations, mixed precision training, tensorboard logging and deployment support with ONNX and TensorRT.
Models from this repo are faster to train (single card trainable) and often have better performance than other implementations, see wiki for reasons and technical specification of models.
| Task | Dataset |
|---|---|
| semantic segmentation | PASCAL VOC 2012 |
| semantic segmentation | Cityscapes |
| semantic segmentation | GTAV* |
| semantic segmentation | SYNTHIA* |
| lane detection | CULane |
| lane detection | TuSimple |
| lane detection | LLAMAS |
| lane detection | BDD100K (In progress) |
* The UDA baseline setup, with Cityscapes val set as validation.
| Task | Backbone | Model/Method |
|---|---|---|
| semantic segmentation | ResNet-101 | FCN |
| semantic segmentation | ResNet-101 | DeeplabV2 |
| semantic segmentation | ResNet-101 | DeeplabV3 |
| semantic segmentation | - | ENet |
| semantic segmentation | - | ERFNet |
| lane detection | ENet, ERFNet, VGG16, ResNets (18, 34, 50, 101), MobileNets (V2, V3-Large), RepVGGs (A0, A1, B0, B1g2, B2), Swin (Tiny) | Baseline |
| lane detection | ERFNet, VGG16, ResNets (18, 34, 50, 101), RepVGGs (A1) | SCNN |
| lane detection | ResNets (18, 34, 50, 101), MobileNets (V2, V3-Large), ERFNet | RESA |
| lane detection | ERFNet, ENet | SAD (Postponed) |
| lane detection | ERFNet | PRNet (In progress) |
| lane detection | ResNets (18, 34, 50, 101), ResNet18-reduced | LSTR |
| lane detection | ResNets (18, 34) | LaneATT |
| lane detection | ResNets (18, 34) | BézierLaneNet |
We provide solid results (average/best/detailed), training time, shell scripts and trained models available for download in MODEL_ZOO.md.
Please prepare the environment and code with INSTALL.md. Then follow the instructions in DATASET.md to set up datasets.
Get started with LANEDETECTION.md for lane detection.
Get started with SEGMENTATION.md for semantic segmentation.
Refer to VISUALIZATION.md for a visualization & inference tutorial, for image and video inputs.
Refer to BENCHMARK.md for a benchmarking tutorial, including FPS test, FLOPs & memory count for each supported model.
Refer to DEPLOY.md for ONNX and TensorRT deployment supports.
Checkout ADVANCED_TUTORIAL.md for advanced use cases and how to code in PytorchAutoDrive.
Refer to CONTRIBUTING.md for contribution guides.
If you feel this framework substantially helped your research or you want a reference when using our results, please cite the following paper that made the official release of PytorchAutoDrive:
@inproceedings{feng2022rethinking,
title={Rethinking efficient lane detection via curve modeling},
author={Feng, Zhengyang and Guo, Shaohua and Tan, Xin and Xu, Ke and Wang, Min and Ma, Lizhuang},
booktitle={Computer Vision and Pattern Recognition},
year={2022}
}
PytorchAutoDrive is maintained by Zhengyang Feng (voldemortX) and Shaohua Guo (cedricgsh).
Contributors (GitHub ID): kalkun, LittleJohnKhan, francis0407
People who sponsored us (e.g., with hardware): Lizhuang Ma, Xin Tan, Junshu Tang (junshutang), Fengqi Liu (FengqiLiu1221)