# HuangJunJie2017/BEVDet

Code base of the BEVDet series .

Repository: https://github.com/HuangJunJie2017/BEVDet
Canonical: https://ross.abutalabs.com/products/bevdet
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-07-04T09:22:26+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1738, "days_push": 790, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1801, forks 311 (observed 2026-08-28T04:05:38.076915+00:00)

## What it is
BEVDet is a Python research codebase implementing the BEVDet series of bird's-eye-view (BEV) 3D object detection models for autonomous driving, including camera-only, temporal (4D), stereo, and LiDAR-camera fusion (DAL) variants. It provides training/evaluation configs, pretrained checkpoints, and published nuScenes benchmarks with mAP/NDS scores and latency/FPS measurements.

## Use cases
- train a camera-based 3D object detection model on nuScenes
- build bird's-eye-view perception for autonomous driving
- benchmark multi-view camera 3D detection models on mAP, NDS, latency, and FPS
- fuse LiDAR and camera data for 3D object detection
- download pretrained BEV detection checkpoints and reproduce paper results
- experiment with temporal aggregation and stereo depth for 3D detection

## When to choose
- You need a reproducible, well-cited baseline for camera-only BEV 3D object detection on nuScenes
- You are researching BEV perception architectures such as depth estimation, temporal fusion, or LiDAR-camera fusion
- You want official configs and pretrained models matching published accuracy and efficiency numbers

## When to avoid
- You need production-grade, safety-certified perception for a deployed vehicle rather than a research codebase
- Your task is not 3D object detection, or your datasets are not nuScenes-compatible without significant adaptation
- You lack CUDA-capable GPUs or prefer not to work in the Python/PyTorch (MMDetection3D) ecosystem

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, deep-learning, computer-vision
- domain: autonomous-vehicles, computer-vision, deep-learning, machine-learning, artificial-intelligence
- platform: python
- tags: 3d-object-detection, bird-eye-view, nuscenes, autonomous-driving, camera-based-perception, lidar-camera-fusion, multi-view-cameras, model-zoo, research-code, pytorch, gpu, linux

## Member repositories
- HuangJunJie2017/BEVDet (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:38.076915+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 2026-08-30T03:22:03.595532+00:00, confidence not recorded.
  - readme: https://github.com/HuangJunJie2017/BEVDet (fetched 2026-08-28T04:05:38.076915+00:00, sha ee99186054cf)
- Data as of 2026-08-30T08:39:29.467469+00:00.