PytorchAutoDrive is a comprehensive PyTorch framework for autonomous driving perception tasks, offering a wide array of semantic segmentation and lane detection models. It targets researchers and engineers by providing end-to-end support from model training and benchmarking to visualization and deployment, aiming to accelerate development and improve performance through efficient, config-based implementations.

How It Works

The framework employs a modular design, allowing users to easily switch between various segmentation (ERFNet, ENet, DeepLab, FCN) and lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet) on diverse backbones. It emphasizes fast training, even on single GPUs, and offers features like mixed-precision training, TensorBoard logging, and ONNX/TensorRT deployment support, contributing to its efficiency and ease of use.

Quick Start & Requirements

• Installation: Follow instructions in INSTALL.md.
• Datasets: Setup required datasets as per DATASET.md.
• Getting Started: Refer to LANEDETECTION.md or SEGMENTATION.md.
• Prerequisites: PyTorch, Python. Specific dependencies detailed in INSTALL.md.

Highlighted Details

• Supports a broad range of models and backbones for semantic segmentation and lane detection.
• Config-based implementation simplifies customization and experimentation.
• Offers tools for visualization, benchmarking (FPS, FLOPs, memory), and deployment (ONNX, TensorRT).
• Claims faster training and often better performance compared to other implementations.

Maintenance & Community

Maintained by Zhengyang Feng and Shaohua Guo, with contributions from several GitHub users. Sponsorships from individuals are acknowledged. Active development is noted, with guidance for legacy code users.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is under active development, and some features like BDD100K dataset support and PRNet model integration are marked as "In progress." Legacy code users should be aware of potential deprecations.