Mmdetection

<div align="center"> <img src="resources/mmdet-logo.png" width="600"/> <div>&nbsp;</div> <div align="center"> <b><font size="5">OpenMMLab website</font></b> ^{<a href="https://openmmlab.com"> <i><font size="4">HOT</font></i> </a>} &nbsp;&nbsp;&nbsp;&nbsp; <b><font size="5">OpenMMLab platform</font></b> ^{<a href="https://platform.openmmlab.com"> <i><font size="4">TRY IT OUT</font></i> </a>} </div> <div>&nbsp;</div>

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

</div> <div align="center">

English | 简体中文

</div> <div align="center"> <a href="https://openmmlab.medium.com/" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" /> <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;"> <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a> </div> <div align="center"> <img src="https://github.com/open-mmlab/mmdetection/assets/17425982/6c29886f-ae7a-4a55-8be4-352ee85b7d3e"/> </div>

Introduction

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

The main branch works with PyTorch 1.8+.

<img src="https://user-images.githubusercontent.com/12907710/187674113-2074d658-f2fb-42d1-ac15-9c4a695e64d7.png"/> <details open> <summary>Major features</summary>

• Modular Design

  We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.

• Support of multiple tasks out of box

  The toolbox directly supports multiple detection tasks such as object detection, instance segmentation, panoptic segmentation, and semi-supervised object detection.

• High efficiency

  All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and SimpleDet.

• State of the art

  The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward. The newly released RTMDet also obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection.

</details>

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

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

Highlight

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

<div align=center> <img src="https://github.com/open-mmlab/mmdetection/assets/17425982/fb14d1ee-5469-44d2-b865-aac9850c429c"/> </div>

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.

| Task | Dataset | AP | FPS(TRT FP16 BS1 3090) | | ------------------------ | ------- | ------------------------------------ | ---------------------- | | Object Detection | COCO | 52.8 | 322 | | Instance Segmentation | COCO | 44.6 | 188 | | Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 |

<div align=center> <img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/> </div>

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

  <details>
  • Train & Test
    • [Learn about Configs](https://mmdetection.readthedocs.io/en