2 个仓库
Awesome GitHub RepositoriesPerception Module Training
Training processes for creating stable weights in object tracking and environmental mapping modules.
Distinct from Perception Dataset Processors: Focuses on the training of the modules' weights via feature aggregation rather than the processing of the datasets.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Perception Module Training. Refine with filters or upvote what's useful.
Awesome Perception Module Training GitHub Repositories
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- opendrivelab/uniad
OpenDriveLab/UniAD
4,645在 GitHub 上查看UniAD 是一个用于自动驾驶的统一深度学习框架，将感知、预测和规划集成到一个端到端的模型中。它作为一种神经网络架构，将原始传感器数据直接映射为驾驶轨迹和运动规划。 本项目是规划导向方法的科研实现，联合训练占用空间、地图构建和目标跟踪模块。它采用多任务感知框架来优化整体驾驶性能。 该系统涵盖了广泛的功能，包括端到端驾驶流水线、车辆运动优化和视觉特征聚合。它协调各种自动驾驶任务，在单个训练周期内优化整个驾驶过程。
Creates stable weights for tracking and mapping by aggregating visual features across multiple video frames.
Pythonautonomous-drivingautonomous-driving-frameworkbev-segmentation - open-mmlab/mmtracking
open-mmlab/mmtracking
3,881在 GitHub 上查看mmtracking is a PyTorch video perception framework designed for training and deploying computer vision models that analyze sequential image data. It provides specialized tools for multi-object tracking, video instance segmentation, and a configuration-driven system for managing deep learning models. The project utilizes a deep learning model registry and a configuration-driven pipeline to swap model backbones and detectors without modifying the core codebase. This modular approach allows for the development of custom perception architectures by combining various components and configurations.
Provides specialized training processes and schedules for optimizing object tracking and video perception modules.
Pythonmulti-object-trackingsingle-object-trackingtracking
