File size: 17,992 Bytes
3bbb319 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 |
<div align="center">
<img src="resources/mmdet-logo.png" width="600"/>
<div> </div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div> </div>
[![PyPI](https://img.shields.io/pypi/v/mmdet)](https://pypi.org/project/mmdet)
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection.readthedocs.io/en/latest/)
[![badge](https://github.com/open-mmlab/mmdetection/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection)
[![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/master/LICENSE)
[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
[📘Documentation](https://mmdetection.readthedocs.io/en/stable/) |
[🛠️Installation](https://mmdetection.readthedocs.io/en/stable/get_started.html) |
[👀Model Zoo](https://mmdetection.readthedocs.io/en/stable/model_zoo.html) |
[🆕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>
## 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
**2.26.0** was released in 23/11/2022:
- Support training on [NPU](docs/en/device/npu.md).
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
A brand new version of **MMDetection v3.0.0rc0** was released in 31/8/2022:
- Unifies interfaces of all components based on [MMEngine](https://github.com/open-mmlab/mmengine).
- Faster training and testing speed with complete support of mixed precision training.
- Refactored and more flexible [architecture](https://mmdetection.readthedocs.io/en/v3.0.0rc0/overview.html).
- Provides more strong baselines and a general semi-supervised object detection framework. See [tutorial of semi-supervised detection](https://mmdetection.readthedocs.io/en/v3.0.0rc0/user_guides/semi_det.html).
- Allows any kind of single-stage model as an RPN in a two-stage model. See [tutorial](https://mmdetection.readthedocs.io/en/v3.0.0rc0/user_guides/single_stage_as_rpn.html).
Find more new features in [3.x branch](https://github.com/open-mmlab/mmdetection/tree/3.x). Issues and PRs are welcome!
## Installation
Please refer to [Installation](docs/en/get_started.md/#Installation) for installation instructions.
## 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>
</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>
</ul>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
Some other methods are also supported in [projects using MMDetection](./docs/en/projects.md).
## 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.
|