visdet 2.28.4
Visia's fork of MMDetection - Object Detection Toolbox and Benchmark
License: Apache License 2.0
Requires Python: <3.13,>=3.10
Keywords: computer vision, mmdetection, object detection

<div align="center">
  <img src="resources/visia-logo-white.svg" width="600"/>
  <div>&nbsp;</div>

[![PyPI](https://img.shields.io/pypi/v/visdet)](https://pypi.org/project/visdet)
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[📘Documentation](https://binitai.github.io/visdet/) |
[🛠️Installation](https://binitai.github.io/visdet/getting-started/installation/) |
[👀Model Zoo](https://binitai.github.io/visdet/model-zoo/) |
[🆕Update News](https://mmdetection.readthedocs.io/en/stable/changelog.html) |
[🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)

</div>

<div align="center">

English | [简体中文](README_zh-CN.md)

</div>

<div align="center">
  <a href="https://openmmlab.medium.com/" style="text-decoration:none;">
    <img src="https://user-images.githubusercontent.com/25839884/218352562-cdded397-b0f3-4ca1-b8dd-a60df8dca75b.png" width="3%" alt="" /></a>
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    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
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  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
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  <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>
</div>

> **Note**: This is a fork of the MMDetection library, customized for internal use at [Visia](https://www.visia.ai/). The original project can be found at https://github.com/open-mmlab/mmdetection
>
> Maintained by the Visia ML Engineering team

## Introduction

MMDetection is an open source object detection toolbox based on PyTorch. It is
a part of the [OpenMMLab](https://openmmlab.com/) project.

The master branch works with **PyTorch 1.5+**.

<img src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.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 frameworks out of box**

  The toolbox directly supports popular and contemporary detection frameworks, *e.g.* Faster RCNN, Mask RCNN, RetinaNet, etc.

- **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.

</details>

Apart from MMDetection, we also released a library [mmcv](https://github.com/open-mmlab/mmcv) for computer vision research, which is heavily depended on by this toolbox.

## What's New

### 💎 Stable version

**2.28.2** was released in 27/2/2023:

- Fixed some known documentation, configuration and linking error issues

Please refer to [changelog.md](docs/en/changelog.md) for details and release history.

For compatibility changes between different versions of MMDetection, please refer to [compatibility.md](docs/en/compatibility.md).

### 🌟 Preview of 3.x version

#### Highlight

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](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet).

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](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                    |

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

A brand new version of **MMDetection v3.0.0rc6** was released in 27/2/2023:

- Support [Boxinst](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/boxinst), [Objects365 Dataset](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/objects365), and [Separated and Occluded COCO metric](https://github.com/open-mmlab/mmdetection/tree/3.x/docs/en/user_guides/useful_tools.md#coco-separated--occluded-mask-metric)
- Support [ConvNeXt-V2](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/ConvNeXt-V2), [DiffusionDet](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/DiffusionDet), and inference of [EfficientDet](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/EfficientDet) and [Detic](https://github.com/open-mmlab/mmdetection/tree/3.x/projects/Detic) in `Projects`
- Refactor [DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/detr) series and support [Conditional-DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/conditional_detr), [DAB-DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/dab_detr), and [DINO](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/dino)
- Support DetInferencer, Test Time Augmentation, and auto import modules from registry
- Support RTMDet-Ins ONNXRuntime and TensorRT [deployment](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet/README.md#deployment-tutorial)
- Support [calculating FLOPs of detectors](https://github.com/open-mmlab/mmdetection/tree/3.x/docs/en/user_guides/useful_tools.md#Model-Complexity)

Find more new features in [3.x branch](https://github.com/open-mmlab/mmdetection/tree/3.x). Issues and PRs are welcome!

## Installation

Install visdet using [uv](https://docs.astral.sh/uv/):

```bash
uv pip install visdet
```

Or with pip:

```bash
pip install visdet
```

For detailed installation instructions including development setup, please refer to [Installation](https://binitai.github.io/visdet/getting-started/installation/).

## Getting Started

Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMDetection. We provide [colab tutorial](demo/MMDet_Tutorial.ipynb) and [instance segmentation colab tutorial](demo/MMDet_InstanceSeg_Tutorial.ipynb), and other tutorials for:

- [with existing dataset](docs/en/1_exist_data_model.md)
- [with new dataset](docs/en/2_new_data_model.md)
- [with existing dataset_new_model](docs/en/3_exist_data_new_model.md)
- [learn about configs](docs/en/tutorials/config.md)
- [customize_datasets](docs/en/tutorials/customize_dataset.md)
- [customize data pipelines](docs/en/tutorials/data_pipeline.md)
- [customize_models](docs/en/tutorials/customize_models.md)
- [customize runtime settings](docs/en/tutorials/customize_runtime.md)
- [customize_losses](docs/en/tutorials/customize_losses.md)
- [finetuning models](docs/en/tutorials/finetune.md)
- [export a model to ONNX](docs/en/tutorials/pytorch2onnx.md)
- [export ONNX to TRT](docs/en/tutorials/onnx2tensorrt.md)
- [weight initialization](docs/en/tutorials/init_cfg.md)
- [how to xxx](docs/en/tutorials/how_to.md)

## Overview of Benchmark and Model Zoo

Results and models are available in the [model zoo](docs/en/model_zoo.md).

<div align="center">
  <b>Architectures</b>
</div>
<table align="center">
  <tbody>
    <tr align="center" valign="bottom">
      <td>
        <b>Object Detection</b>
      </td>
      <td>
        <b>Instance Segmentation</b>
      </td>
      <td>
        <b>Panoptic Segmentation</b>
      </td>
      <td>
        <b>Other</b>
      </td>
    </tr>
    <tr valign="top">
      <td>
        <ul>
            <li><a href="configs/fast_rcnn">Fast R-CNN (ICCV'2015)</a></li>
            <li><a href="configs/faster_rcnn">Faster R-CNN (NeurIPS'2015)</a></li>
            <li><a href="configs/rpn">RPN (NeurIPS'2015)</a></li>
            <li><a href="configs/ssd">SSD (ECCV'2016)</a></li>
            <li><a href="configs/retinanet">RetinaNet (ICCV'2017)</a></li>
            <li><a href="configs/cascade_rcnn">Cascade R-CNN (CVPR'2018)</a></li>
            <li><a href="configs/yolo">YOLOv3 (ArXiv'2018)</a></li>
            <li><a href="configs/cornernet">CornerNet (ECCV'2018)</a></li>
            <li><a href="configs/grid_rcnn">Grid R-CNN (CVPR'2019)</a></li>
            <li><a href="configs/guided_anchoring">Guided Anchoring (CVPR'2019)</a></li>
            <li><a href="configs/fsaf">FSAF (CVPR'2019)</a></li>
            <li><a href="configs/centernet">CenterNet (ArXiv'2019)</a></li>
            <li><a href="configs/libra_rcnn">Libra R-CNN (CVPR'2019)</a></li>
            <li><a href="configs/tridentnet">TridentNet (ICCV'2019)</a></li>
            <li><a href="configs/fcos">FCOS (ICCV'2019)</a></li>
            <li><a href="configs/reppoints">RepPoints (ICCV'2019)</a></li>
            <li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li>
            <li><a href="configs/cascade_rpn">CascadeRPN (NeurIPS'2019)</a></li>
            <li><a href="configs/foveabox">Foveabox (TIP'2020)</a></li>
            <li><a href="configs/double_heads">Double-Head R-CNN (CVPR'2020)</a></li>
            <li><a href="configs/atss">ATSS (CVPR'2020)</a></li>
            <li><a href="configs/nas_fcos">NAS-FCOS (CVPR'2020)</a></li>
            <li><a href="configs/centripetalnet">CentripetalNet (CVPR'2020)</a></li>
            <li><a href="configs/autoassign">AutoAssign (ArXiv'2020)</a></li>
            <li><a href="configs/sabl">Side-Aware Boundary Localization (ECCV'2020)</a></li>
            <li><a href="configs/dynamic_rcnn">Dynamic R-CNN (ECCV'2020)</a></li>
            <li><a href="configs/detr">DETR (ECCV'2020)</a></li>
            <li><a href="configs/paa">PAA (ECCV'2020)</a></li>
            <li><a href="configs/vfnet">VarifocalNet (CVPR'2021)</a></li>
            <li><a href="configs/sparse_rcnn">Sparse R-CNN (CVPR'2021)</a></li>
            <li><a href="configs/yolof">YOLOF (CVPR'2021)</a></li>
            <li><a href="configs/yolox">YOLOX (ArXiv'2021)</a></li>
            <li><a href="configs/deformable_detr">Deformable DETR (ICLR'2021)</a></li>
            <li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
            <li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
      </ul>
      </td>
      <td>
        <ul>
          <li><a href="configs/mask_rcnn">Mask R-CNN (ICCV'2017)</a></li>
          <li><a href="configs/cascade_rcnn">Cascade Mask R-CNN (CVPR'2018)</a></li>
          <li><a href="configs/ms_rcnn">Mask Scoring R-CNN (CVPR'2019)</a></li>
          <li><a href="configs/htc">Hybrid Task Cascade (CVPR'2019)</a></li>
          <li><a href="configs/yolact">YOLACT (ICCV'2019)</a></li>
          <li><a href="configs/instaboost">InstaBoost (ICCV'2019)</a></li>
          <li><a href="configs/solo">SOLO (ECCV'2020)</a></li>
          <li><a href="configs/point_rend">PointRend (CVPR'2020)</a></li>
          <li><a href="configs/detectors">DetectoRS (CVPR'2021)</a></li>
          <li><a href="configs/solov2">SOLOv2 (NeurIPS'2020)</a></li>
          <li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
          <li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
          <li><a href="configs/mask2former">Mask2Former (CVPR'2022)</a></li>
        </ul>
      </td>
      <td>
        <ul>
          <li><a href="configs/panoptic_fpn">Panoptic FPN (CVPR'2019)</a></li>
          <li><a href="configs/maskformer">MaskFormer (NeurIPS'2021)</a></li>
          <li><a href="configs/mask2former">Mask2Former (CVPR'2022)</a></li>
        </ul>
      </td>
      <td>
        </ul>
          <li><b>Contrastive Learning</b></li>
        <ul>
        <ul>
          <li><a href="configs/selfsup_pretrain">SwAV (NeurIPS'2020)</a></li>
          <li><a href="configs/selfsup_pretrain">MoCo (CVPR'2020)</a></li>
          <li><a href="configs/selfsup_pretrain">MoCov2 (ArXiv'2020)</a></li>
        </ul>
        </ul>
        </ul>
          <li><b>Distillation</b></li>
        <ul>
        <ul>
          <li><a href="configs/ld">Localization Distillation (CVPR'2022)</a></li>
          <li><a href="configs/lad">Label Assignment Distillation (WACV'2022)</a></li>
        </ul>
        </ul>
      </ul>
        <li><b>Receptive Field Search</b></li>
      <ul>
        <ul>
          <li><a href="configs/rfnext">RF-Next (TPAMI'2022)</a></li>
        </ul>
        </ul>
      </ul>
      </td>
    </tr>
</td>
    </tr>
  </tbody>
</table>

<div align="center">
  <b>Components</b>
</div>
<table align="center">
  <tbody>
    <tr align="center" valign="bottom">
      <td>
        <b>Backbones</b>
      </td>
      <td>
        <b>Necks</b>
      </td>
      <td>
        <b>Loss</b>
      </td>
      <td>
        <b>Common</b>
      </td>
    </tr>
    <tr valign="top">
      <td>
      <ul>
        <li>VGG (ICLR'2015)</li>
        <li>ResNet (CVPR'2016)</li>
        <li>ResNeXt (CVPR'2017)</li>
        <li>MobileNetV2 (CVPR'2018)</li>
        <li><a href="configs/hrnet">HRNet (CVPR'2019)</a></li>
        <li><a href="configs/empirical_attention">Generalized Attention (ICCV'2019)</a></li>
        <li><a href="configs/gcnet">GCNet (ICCVW'2019)</a></li>
        <li><a href="configs/res2net">Res2Net (TPAMI'2020)</a></li>
        <li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
        <li><a href="configs/resnest">ResNeSt (CVPRW'2022)</a></li>
        <li><a href="configs/pvt">PVT (ICCV'2021)</a></li>
        <li><a href="configs/swin">Swin (ICCV'2021)</a></li>
        <li><a href="configs/pvt">PVTv2 (CVMJ'2022)</a></li>
        <li><a href="configs/resnet_strikes_back">ResNet strikes back (NeurIPSW'2021)</a></li>
        <li><a href="configs/efficientnet">EfficientNet (ICML'2019)</a></li>
        <li><a href="configs/convnext">ConvNeXt (CVPR'2022)</a></li>
      </ul>
      </td>
      <td>
      <ul>
        <li><a href="configs/pafpn">PAFPN (CVPR'2018)</a></li>
        <li><a href="configs/nas_fpn">NAS-FPN (CVPR'2019)</a></li>
        <li><a href="configs/carafe">CARAFE (ICCV'2019)</a></li>
        <li><a href="configs/fpg">FPG (ArXiv'2020)</a></li>
        <li><a href="configs/groie">GRoIE (ICPR'2020)</a></li>
        <li><a href="configs/dyhead">DyHead (CVPR'2021)</a></li>
      </ul>
      </td>
      <td>
        <ul>
          <li><a href="configs/ghm">GHM (AAAI'2019)</a></li>
          <li><a href="configs/gfl">Generalized Focal Loss (NeurIPS'2020)</a></li>
          <li><a href="configs/seesaw_loss">Seasaw Loss (CVPR'2021)</a></li>
        </ul>
      </td>
      <td>
        <ul>
          <li><a href="configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py">OHEM (CVPR'2016)</a></li>
          <li><a href="configs/gn">Group Normalization (ECCV'2018)</a></li>
          <li><a href="configs/dcn">DCN (ICCV'2017)</a></li>
          <li><a href="configs/dcnv2">DCNv2 (CVPR'2019)</a></li>
          <li><a href="configs/gn+ws">Weight Standardization (ArXiv'2019)</a></li>
          <li><a href="configs/pisa">Prime Sample Attention (CVPR'2020)</a></li>
          <li><a href="configs/strong_baselines">Strong Baselines (CVPR'2021)</a></li>
          <li><a href="configs/resnet_strikes_back">Resnet strikes back (NeurIPSW'2021)</a></li>
          <li><a href="configs/rfnext">RF-Next (TPAMI'2022)</a></li>
        </ul>
      </td>
    </tr>
</td>
    </tr>
  </tbody>
</table>

Some other methods are also supported in [projects using MMDetection](./docs/en/projects.md).

## Alternatives

There are several other object detection frameworks available. Below is a comparison of popular alternatives:

| Framework | Repository | Description | Key Features |
|-----------|------------|-------------|--------------|
| **RT-DETR** | [lyuwenyu/RT-DETR](https://github.com/lyuwenyu/RT-DETR) | | |
| **D-FINE** | [Peterande/D-FINE](https://github.com/Peterande/D-FINE) | | |
| **Ultralytics** | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) | | |
| **Detrex** | [IDEA-Research/detrex](https://github.com/IDEA-Research/detrex) | | |
| **Detectron2** | [facebookresearch/detectron2](https://github.com/facebookresearch/detectron2) | | |

## FAQ

Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.

## Contributing

We appreciate all contributions to improve MMDetection. Ongoing projects can be found in out [GitHub Projects](https://github.com/open-mmlab/mmdetection/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/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   = {{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}
}
```

## License

This project is released under the [Apache 2.0 license](LICENSE).

## Projects in OpenMMLab

- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.