# maudzung/Complex-YOLOv4-Pytorch

The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"

Repository: https://github.com/maudzung/Complex-YOLOv4-Pytorch
Canonical: https://ross.abutalabs.com/products/complex-yolov4-pytorch
Homepage: https://arxiv.org/pdf/1803.06199.pdf
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: yolov4, object-detection, real-time, multiprocessing, data-parallel-computing, lidar, lidar-point-cloud, complex-yolo, 3d-object-detection, mish, mosaic, rotated-boxes, giou, rotated-boxes-iou
Last push: 2024-08-30T23:53:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2252, "days_push": 733, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1327, forks 267 (observed 2026-08-28T04:04:22.959346+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: autonomous-vehicles, computer-vision, deep-learning, machine-learning
- platform: python
- tags: lidar, point-cloud, 3d-object-detection, yolov4, pytorch, kitti-dataset, rotated-bounding-boxes, real-time-detection, gpu, linux

## Member repositories
- maudzung/Complex-YOLOv4-Pytorch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:22.959346+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:46:17.979453+00:00, confidence not recorded.
  - readme: https://github.com/maudzung/Complex-YOLOv4-Pytorch (fetched 2026-08-28T04:04:22.959346+00:00, sha 45e99befb0a9)
- Data as of 2026-08-30T08:39:29.467469+00:00.