Skip to content

Latest commit

 

History

History
336 lines (284 loc) · 19.4 KB

README.md

File metadata and controls

336 lines (284 loc) · 19.4 KB
 
OpenMMLab website HOT      OpenMMLab platform TRY IT OUT
 

docs badge codecov license

News:

We have renamed the branch 1.1 to main and switched the default branch from master to main. We encourage users to migrate to the latest version, though it comes with some cost. Please refer to Migration Guide for more details.

v1.1.0 was released in 6/4/2023

We have supported more LiDAR-based segmentation methods, including Cylinder3D, MinkUNet and SPVCNN. More new features about 3D perception are on the way. Please stay tuned!

v1.1.0rc3 was released in 7/1/2023

The compatibilities of models are broken due to the unification and simplification of coordinate systems after v1.0.0rc0. For now, most models are benchmarked with similar performance, though few models are still being benchmarked. In the following release, we will update all the model checkpoints and benchmarks. See more details in the Changelog and Changelog-v1.0.x.

Documentation: https://mmdetection3d.readthedocs.io/

Introduction

English | 简体中文

The master branch works with PyTorch 1.6+.

MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project developed by MMLab.

demo image

Major features

  • Support multi-modality/single-modality detectors out of box

    It directly supports multi-modality/single-modality detectors including MVXNet, VoteNet, PointPillars, etc.

  • Support indoor/outdoor 3D detection out of box

    It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI. For nuScenes dataset, we also support nuImages dataset.

  • Natural integration with 2D detection

    All the about 300+ models, methods of 40+ papers, and modules supported in MMDetection can be trained or used in this codebase.

  • High efficiency

    It trains faster than other codebases. The main results are as below. Details can be found in benchmark.md. We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by .

    Methods MMDetection3D OpenPCDet votenet Det3D
    VoteNet 358 77
    PointPillars-car 141 140
    PointPillars-3class 107 44
    SECOND 40 30
    Part-A2 17 14

Like MMDetection and MMCV, MMDetection3D can also be used as a library to support different projects on top of it.

License

This project is released under the Apache 2.0 license.

Changelog

1.1.0 was released in 6/4/2023.

Please refer to changelog.md for details and release history.

Benchmark and model zoo

Results and models are available in the model zoo.

Components
Backbones Heads Features
Architectures
3D Object Detection Monocular 3D Object Detection Multi-modal 3D Object Detection 3D Semantic Segmentation
  • Outdoor
  • Indoor
  • Outdoor
  • Indoor
  • Outdoor
  • Indoor
  • Outdoor
  • Indoor
  • ResNet PointNet++ SECOND DGCNN RegNetX DLA MinkResNet Cylinder3D MinkUNet
    SECOND
    PointPillars
    FreeAnchor
    VoteNet
    H3DNet
    3DSSD
    Part-A2
    MVXNet
    CenterPoint
    SSN
    ImVoteNet
    FCOS3D
    PointNet++
    Group-Free-3D
    ImVoxelNet
    PAConv
    DGCNN
    SMOKE
    PGD
    MonoFlex
    SA-SSD
    FCAF3D
    PV-RCNN
    Cylinder3D
    MinkUNet
    SPVCNN

    Note: All the about 300+ models, methods of 40+ papers in 2D detection supported by MMDetection can be trained or used in this codebase.

    Installation

    Please refer to get_started.md for installation.

    Get Started

    Please see get_started.md for the basic usage of MMDetection3D. We provide guidance for quick run with existing dataset and with new dataset for beginners. There are also tutorials for learning configuration systems, customizing dataset, designing data pipeline, customizing models, customizing runtime settings and Waymo dataset.

    Please refer to FAQ for frequently asked questions. When updating the version of MMDetection3D, please also check the compatibility doc to be aware of the BC-breaking updates introduced in each version.

    Citation

    If you find this project useful in your research, please consider cite:

    @misc{mmdet3d2020,
        title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
        author={MMDetection3D Contributors},
        howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
        year={2020}
    }

    Contributing

    We appreciate all contributions to improve MMDetection3D. Please refer to CONTRIBUTING.md for the contributing guideline.

    Acknowledgement

    MMDetection3D is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new 3D detectors.

    Projects in OpenMMLab

    • MMEngine: OpenMMLab foundational library for training deep learning models.
    • MMCV: OpenMMLab foundational library for computer vision.
    • MMEval: A unified evaluation library for multiple machine learning libraries.
    • MIM: MIM installs OpenMMLab packages.
    • MMClassification: OpenMMLab image classification toolbox and benchmark.
    • MMDetection: OpenMMLab detection toolbox and benchmark.
    • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
    • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
    • MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
    • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
    • MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
    • MMPose: OpenMMLab pose estimation toolbox and benchmark.
    • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
    • MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
    • MMRazor: OpenMMLab model compression toolbox and benchmark.
    • MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
    • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
    • MMTracking: OpenMMLab video perception toolbox and benchmark.
    • MMFlow: OpenMMLab optical flow toolbox and benchmark.
    • MMEditing: OpenMMLab image and video editing toolbox.
    • MMGeneration: OpenMMLab image and video generative models toolbox.
    • MMDeploy: OpenMMLab model deployment framework.