onedl-mmdetection 3.5.1

 

VBTI Website      OneDL platform

 

📘 Documentation | 🛠️ Installation | 👀 Model Zoo | 🆕 Update News | 🚀Ongoing Projects | 🤔 Reporting Issues |

Introduction

MMDetection is an open source object detection toolbox based on PyTorch.

The main branch works with PyTorch 2.0+.

Apart from MMDetection, we also released MMEngine for model training and MMCV for computer vision research, which are heavily depended on by this toolbox.

What's New

The VBTI development team is reviving MMLabs code, making it work with newer pytorch versions and fixing bugs. We are only a small team, so your help is appreciated.

Highlight

v3.4.x was released in 2025: VBTI updated several dependencies to make it work with newer versions of several important packages as well as making it work with modern python (and installers).

💎 We have released the pre-trained weights for MM-Grounding-DINO Swin-B and Swin-L, welcome to try and give feedback.

v3.3.0 was released in 5/1/2024:

MM-Grounding-DINO: An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Grounding DINO is a grounding pre-training model that unifies 2d open vocabulary object detection and phrase grounding, with wide applications. However, its training part has not been open sourced. Therefore, we propose MM-Grounding-DINO, which not only serves as an open source replication version of Grounding DINO, but also achieves significant performance improvement based on reconstructed data types, exploring different dataset combinations and initialization strategies. Moreover, we conduct evaluations from multiple dimensions, including OOD, REC, Phrase Grounding, OVD, and Fine-tune, to fully excavate the advantages and disadvantages of Grounding pre-training, hoping to provide inspiration for future work.

code: mm_grounding_dino/README.md

We are excited to announce our latest work on real-time object recognition tasks, RTMDet, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the technical report. Pre-trained models are here.

Installation

Please refer to Installation for installation instructions.

Getting Started

Please see Overview for the general introduction of MMDetection.

For detailed user guides and advanced guides, please refer to our documentation:

• User Guides

  • Train & Test
    • Learn about Configs
    • Inference with existing models
    • Dataset Prepare
    • Test existing models on standard datasets
    • Train predefined models on standard datasets
    • Train with customized datasets
    • Train with customized models and standard datasets
    • Finetuning Models
    • Test Results Submission
    • Weight initialization
    • Use a single stage detector as RPN
    • Semi-supervised Object Detection
  • Useful Tools

• Advanced Guides

  • Basic Concepts
  • Component Customization
  • How to

We also provide object detection colab tutorial and instance segmentation colab tutorial .

To migrate from MMDetection 2.x, please refer to migration.

Overview of Benchmark and Model Zoo

Results and models are available in the model zoo.

^{* Requires onedl-mmcv with compiled C++/CUDA extensions for a core architectural component (e.g. deformable convolution, corner pooling, deformable attention, CARAFE upsampling, point sampling, or lane NMS). Note: all detection models also require batched_nms from mmcv.ops at inference time — so you would probably need onedl-mmcv with ops. See the FAQ for installation instructions.}

Some other methods are also supported in projects using MMDetection.

FAQ

Please refer to FAQ for frequently asked questions.

Contributing

We appreciate all contributions to improve MMDetection. Ongoing projects can be found in out GitHub Projects. Welcome community users to participate in these projects. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, 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 detectors.

Citation

If you use this toolbox or benchmark in your research, please cite this project.

@article{mmdetection,
  title   = {{OneDL-MMDetection}: OneDL MMLab Detection Toolbox and Benchmark},
  author  = {OneDL MMDetection Contributors},
  year={2025}
}

License

This project is released under the Apache 2.0 license.

Projects in VBTI-development

• OneDL-MMEngine: Foundational library for training deep learning models.
• OneDL-MMCV: Foundational library for computer vision.
• OneDL-MMPreTrain: Pre-training toolbox and benchmark.
• OneDL-MMDetection: Detection toolbox and benchmark.
• OneDL-MMRotate: Rotated object detection toolbox and benchmark.
• OneDL-MMSegmentation: Semantic segmentation toolbox and benchmark.
• OneDL-MMDeploy: Model deployment framework.
• OneDL-MIM: MIM installs VBTI packages.