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Awesome GitHub RepositoriesDistributed Rank Seeds
Assigning unique random seeds to each distributed training rank for diverse data shuffling.
Distinct from Deterministic Training Seeds: Distinct from Deterministic Training Seeds: focuses on per-rank seed assignment for distributed training, not global reproducibility.
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Awesome Distributed Rank Seeds GitHub Repositories
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- open-mmlab/mmdetection3d
open-mmlab/mmdetection3d
6,273Ver en GitHubMMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate
Sets different random seeds for each distributed training rank to ensure diverse data shuffling.
Python3d-object-detectionobject-detectionpoint-cloud
