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) | 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) | 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).
Community contributors (GitHub ID): kalkun
People who sponsored us with hardware: Junshu Tang (junshutang)