Benchmark Models Overview

Through benchmark models, our Autonomous Driving Learning Center aims to provideAdvanced Learnersa structured, standardized learning resource library. Benchmark models bring together recent research results and allow learners to contribute their own models through collaborative development. A systematic process is defined for each task, helping learners get started quickly and participate in community building.

  • Guides:To help learners build an environment from scratch and begin practicing smoothly, we provide detailed guides covering every step from dataset download to environment configuration. Both beginners and experienced developers can quickly acquire the skills they need.

  • Unified Format:Another feature of benchmark models is a unified code format and structure. To make code easier to understand and maintain, we provide consistent file-naming rules, recommended function interfaces, and data-processing workflows for different tasks. This helps learners adopt new models quickly and supports future code contribution and maintenance.

  • Collaborative Development:The ultimate goal of the OnSite Learning Center is to build an open community where learners and researchers jointly develop and improve models. Everyone is encouraged to contribute models for different problems, tasks, and datasets, formatted using our templates. This enriches the model library, gives learners more choices, and promotes communication and collaboration across the community.