PytorchAutoDrive: A Comprehensive Toolkit for Self-Driving Perception
PytorchAutoDrive is a remarkable framework crafted entirely in Python, designed to streamline tasks related to semantic segmentation and lane detection using the powerful PyTorch library. This framework offers extensive support ranging from research activities like model training and testing to practical applications such as visualization and deployment.
Core Features
PytorchAutoDrive stands out by supporting a multitude of methodologies across various backbones. Its implementation is primarily configuration-driven, featuring modular and easily comprehensible code structures. This includes capabilities for image and keypoint loading, transformation, and visualization processes. Additionally, the framework supports mixed precision training, TensorBoard logging, and deployment through ONNX and TensorRT.
The models built using this framework are engineered to train quickly, often outperforming other implementations in speed and efficiency. Users can achieve satisfactory training results even on a single GPU card. For detailed insight into these advantages, the framework’s wiki provides extensive notes and technical specifications.
Dataset Support
PytorchAutoDrive accommodates several prominent datasets, organized by their primary tasks:
- Semantic Segmentation: Supports datasets like PASCAL VOC 2012, Cityscapes, GTAV, and SYNTHIA.
- Lane Detection: Offers compatibility with datasets such as CULane, TuSimple, LLAMAS, and BDD100K (currently in progress).
Model Compatibility
The framework supports a wide array of models, categorized as follows:
- Semantic Segmentation: Compatible models include FCN, DeeplabV2, DeeplabV3, ENet, and ERFNet.
- Lane Detection: Models like SCNN, RESA, SAD, PRNet, LSTR, LaneATT, and BézierLaneNet are supported, built on backbones such as ENet, ERFNet, VGG16, ResNets, and more.
Model Zoo
PytorchAutoDrive provides a Model Zoo, a resource hub featuring comprehensive results, training times, and shell scripts. Pre-trained models are readily available for download, offering a head start for users aiming to benchmark or deploy these models.
Getting Started
For individuals eager to explore lane detection or semantic segmentation, PytorchAutoDrive supplies ample beginner-friendly guides housed in LANEDETECTION.md and SEGMENTATION.md, respectively.
Visualization and Benchmark Tools
The framework includes tools for visualization and benchmarking, each with its respective guide:
- Visualization Tools: Available in VISUALIZATION.md, this guide provides insights into handling image and video input for visualization and inference.
- Benchmark Tools: BENCHMARK.md explains how to perform thorough benchmarking of models, including FPS testing, FLOPs, and memory counting.
Deployment and Advanced Usage
Deployment support is meticulously explained in DEPLOY.md, covering ONNX and TensorRT options. Advanced users are encouraged to explore ADVANCED_TUTORIAL.md, which delves into more sophisticated use cases and customization within the framework.
Contribution and Citation
Community involvement is highly encouraged, and potential contributors can find pertinent information in CONTRIBUTING.md. If PytorchAutoDrive significantly aids your research, consider citing the associated paper titled "Rethinking Efficient Lane Detection via Curve Modeling" presented at Computer Vision and Pattern Recognition 2022.
Acknowledgments
The ongoing development of PytorchAutoDrive is driven by Zhengyang Feng and Shaohua Guo, with contributions from numerous developers and sponsorship support from several prominent figures and organizations.
In summary, PytorchAutoDrive serves as a robust and comprehensive solution for tackling self-driving perception challenges, suitable for both researchers and developers looking to leverage advanced machine learning models in the realm of autonomous driving.