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MMDetection is an open source object detection toolbox based on PyTorch. It isa part of theOpenMMLab project.

The main branch works withPyTorch 1.8+.

Apart from MMDetection, we also releasedMMEngine for model training andMMCV for computer vision research, which are heavily depended on by this toolbox.

💎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 thetechnical report. Pre-trained models arehere.

Please refer toInstallation for installation instructions.

Please seeOverview for the general introduction of MMDetection.

For detailed user guides and advanced guides, please refer to ourdocumentation:

• 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 tomigration.

Results and models are available in themodel zoo.

Some other methods are also supported inprojects using MMDetection.

Please refer toFAQ for frequently asked questions.

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

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.

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

@article{mmdetection,  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},  author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and             Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and             Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},  journal= {arXiv preprint arXiv:1906.07155},  year={2019}}

This project is released under theApache 2.0 license.

• MMEngine: OpenMMLab foundational library for training deep learning models.
• MMCV: OpenMMLab foundational library for computer vision.
• MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
• MMagic: OpenMMLabAdvanced,Generative andIntelligentCreation toolbox.
• 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.
• MIM: MIM installs OpenMMLab packages.
• MMEval: A unified evaluation library for multiple machine learning libraries.
• Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.