maudzung/Complex-YOLOv4-Pytorch

Health v2 · maintenance only

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1327 stars · 267 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of Complex-YOLO, a YOLOv4-based model for real-time 3D object detection on LiDAR point clouds. It supports distributed data parallel training, mosaic/cutout augmentation, and GIoU loss for rotated boxes, evaluated on the KITTI 3D detection dataset.

Use cases

• detect 3d objects from lidar point clouds
• train a yolov4 model on kitti point cloud data
• real-time 3d object detection for autonomous driving
• run 3d object detection on a gpu with pytorch
• experiment with rotated box giou loss for lidar detection
• visualize lidar point cloud detections

When to choose

• you need a ready-to-train PyTorch baseline for 3D object detection on LiDAR point clouds
• you want YOLOv4-style real-time detection with rotated bounding boxes on KITTI-format data
• you need distributed data parallel training for a point cloud detection model

When to avoid

• you need 2D image object detection rather than 3D point cloud detection
• you want the author's newer, faster anchor-free approach without non-max suppression
• you need production-supported software with ongoing feature development
• your point cloud data is not in KITTI format and you cannot convert it

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing autonomous-vehicles computer-vision deep-learning machine-learning python lidar point-cloud 3d-object-detection yolov4 pytorch kitti-dataset rotated-bounding-boxes real-time-detection gpu linux

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