https://github.com/0sliverbullet/mmdetection
OpenMMLab Detection Toolbox and Benchmark
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OpenMMLab Detection Toolbox and Benchmark
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Fork of open-mmlab/mmdetection
Created over 2 years ago
· Last pushed about 2 years ago
https://github.com/0SliverBullet/MMDetection/blob/main/
OpenMMLab website HOT OpenMMLab platform TRY IT OUT[](https://pypi.org/project/mmdet) [](https://mmdetection.readthedocs.io/en/latest/) [](https://github.com/open-mmlab/mmdetection/actions) [](https://codecov.io/gh/open-mmlab/mmdetection) [](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE) [](https://github.com/open-mmlab/mmdetection/issues) [](https://github.com/open-mmlab/mmdetection/issues) [](https://openxlab.org.cn/apps?search=mmdet) [Documentation](https://mmdetection.readthedocs.io/en/latest/) | [Installation](https://mmdetection.readthedocs.io/en/latest/get_started.html) | [Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html) | [Update News](https://mmdetection.readthedocs.io/en/latest/notes/changelog.html) | [Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) | [Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)English | [](README_zh-CN.md)
## Introduction MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project. The main branch works with **PyTorch 1.8+**.
Apart from MMDetection, we also released [MMEngine](https://github.com/open-mmlab/mmengine) for model training and [MMCV](https://github.com/open-mmlab/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](https://arxiv.org/abs/2401.02361)** 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](configs/mm_grounding_dino/README.md)Major features
- **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](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). - **State of the art** The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward. The newly released [RTMDet](configs/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.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](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rtmdet). [](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real) [](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real) [](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real) | 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 |
## Installation Please refer to [Installation](https://mmdetection.readthedocs.io/en/latest/get_started.html) for installation instructions. ## Getting Started Please see [Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the general introduction of MMDetection. For detailed user guides and advanced guides, please refer to our [documentation](https://mmdetection.readthedocs.io/en/latest/): - User Guides
- [Train & Test](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#train-test) - [Learn about Configs](https://mmdetection.readthedocs.io/en/latest/user_guides/config.html) - [Inference with existing models](https://mmdetection.readthedocs.io/en/latest/user_guides/inference.html) - [Dataset Prepare](https://mmdetection.readthedocs.io/en/latest/user_guides/dataset_prepare.html) - [Test existing models on standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/test.html) - [Train predefined models on standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/train.html) - [Train with customized datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/train.html#train-with-customized-datasets) - [Train with customized models and standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/new_model.html) - [Finetuning Models](https://mmdetection.readthedocs.io/en/latest/user_guides/finetune.html) - [Test Results Submission](https://mmdetection.readthedocs.io/en/latest/user_guides/test_results_submission.html) - [Weight initialization](https://mmdetection.readthedocs.io/en/latest/user_guides/init_cfg.html) - [Use a single stage detector as RPN](https://mmdetection.readthedocs.io/en/latest/user_guides/single_stage_as_rpn.html) - [Semi-supervised Object Detection](https://mmdetection.readthedocs.io/en/latest/user_guides/semi_det.html) - [Useful Tools](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#useful-tools)- Advanced Guides- [Basic Concepts](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#basic-concepts) - [Component Customization](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#component-customization) - [How to](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#how-to)We also provide object detection colab tutorial [](demo/MMDet_Tutorial.ipynb) and instance segmentation colab tutorial [](demo/MMDet_InstanceSeg_Tutorial.ipynb). To migrate from MMDetection 2.x, please refer to [migration](https://mmdetection.readthedocs.io/en/latest/migration.html). ## Overview of Benchmark and Model Zoo Results and models are available in the [model zoo](docs/en/model_zoo.md).Architectures
Components
| Backbones | Necks | Loss | Common |
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Owner
- Name: ZHENG Zubin
- Login: 0SliverBullet
- Kind: user
- Location: Shenzhen, Guangdong, China
- Company: Southern University of Science and Technology, China
- Repositories: 1
- Profile: https://github.com/0SliverBullet
Zheng Zubin, male, a junior student at Southern University of Science and Technology (2021.8 - ).