From c8f5df9c58f5a7dc2357a4905ff499d545fbad1c Mon Sep 17 00:00:00 2001 From: majorli Date: Thu, 1 Dec 2022 06:40:43 +0000 Subject: [PATCH] fix APCNet model readme issue link #I63W7K Signed-off-by: majorli --- .../apcnet/pytorch/README.md | 20 +- .../apcnet/pytorch/README_origin.md | 205 ---------------- .../apcnet/pytorch/README_zh-CN.md | 220 ------------------ 3 files changed, 14 insertions(+), 431 deletions(-) delete mode 100644 cv/semantic_segmentation/apcnet/pytorch/README_origin.md delete mode 100644 cv/semantic_segmentation/apcnet/pytorch/README_zh-CN.md diff --git a/cv/semantic_segmentation/apcnet/pytorch/README.md b/cv/semantic_segmentation/apcnet/pytorch/README.md index 97b72a317..81cb2a946 100644 --- a/cv/semantic_segmentation/apcnet/pytorch/README.md +++ b/cv/semantic_segmentation/apcnet/pytorch/README.md @@ -12,17 +12,25 @@ And then calculates a context vector with these affinities. ### Install packages ```shell -$ pip3 install -r requirements.txt +pip3 install -r requirements.txt ``` ### Build Extension ```shell -$ python3 setup.py build && cp build/lib.linux*/mmcv/_ext.cpython* mmcv +python3 setup.py build && cp build/lib.linux*/mmcv/_ext.cpython* mmcv ``` +## Step 2: Prepare Datasets -## Step 2: Training +Download cityscapes from file server or official website [Cityscapes](https://www.cityscapes-dataset.com) + +```shell +mkdir -p data/ +ln -s ${CITYSCAPES_DATASET_PATH} data/cityscapes +``` + +## Step 3: Training **The available configs are as follows:** @@ -52,18 +60,18 @@ apcnet_r101-d8_769x769_80k_cityscapes ### Training on single card ```shell -$ bash train.sh [training args] # config file can be found in the configs directory +bash train.sh [training args] # config file can be found in the configs directory ``` ### Training on mutil-cards ```shell -$ bash train_dist.sh [training args] # config file can be found in the configs directory +bash train_dist.sh [training args] # config file can be found in the configs directory ``` ### Example ```shell -bash train_dist.sh apcnet_r50-d8_512x1024_40k_cityscapes 8 +bash train_dist.sh configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py 8 ``` ### Training arguments diff --git a/cv/semantic_segmentation/apcnet/pytorch/README_origin.md b/cv/semantic_segmentation/apcnet/pytorch/README_origin.md deleted file mode 100644 index ba1d3a444..000000000 --- a/cv/semantic_segmentation/apcnet/pytorch/README_origin.md +++ /dev/null @@ -1,205 +0,0 @@ -
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- -[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmsegmentation)](https://pypi.org/project/mmsegmentation/) -[![PyPI](https://img.shields.io/pypi/v/mmsegmentation)](https://pypi.org/project/mmsegmentation) -[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/en/latest/) -[![badge](https://github.com/open-mmlab/mmsegmentation/workflows/build/badge.svg)](https://github.com/open-mmlab/mmsegmentation/actions) -[![codecov](https://codecov.io/gh/open-mmlab/mmsegmentation/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmsegmentation) -[![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE) -[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues) -[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues) - -Documentation: https://mmsegmentation.readthedocs.io/ - -English | [简体中文](README_zh-CN.md) - -## Introduction - -MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. -It is a part of the OpenMMLab project. - -The master branch works with **PyTorch 1.5+**. - -![demo image](resources/seg_demo.gif) - -### Major features - -- **Unified Benchmark** - - We provide a unified benchmark toolbox for various semantic segmentation methods. - -- **Modular Design** - - We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules. - -- **Support of multiple methods out of box** - - The toolbox directly supports popular and contemporary semantic segmentation frameworks, *e.g.* PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc. - -- **High efficiency** - - The training speed is faster than or comparable to other codebases. - -## License - -This project is released under the [Apache 2.0 license](LICENSE). - -## Changelog - -v0.24.1 was released in 5/1/2022. -Please refer to [changelog.md](docs/en/changelog.md) for details and release history. - -## Benchmark and model zoo - -Results and models are available in the [model zoo](docs/en/model_zoo.md). - -Supported backbones: - -- [x] ResNet (CVPR'2016) -- [x] ResNeXt (CVPR'2017) -- [x] [HRNet (CVPR'2019)](configs/hrnet) -- [x] [ResNeSt (ArXiv'2020)](configs/resnest) -- [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2) -- [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3) -- [x] [Vision Transformer (ICLR'2021)](configs/vit) -- [x] [Swin Transformer (ICCV'2021)](configs/swin) -- [x] [Twins (NeurIPS'2021)](configs/twins) -- [x] [BEiT (ICLR'2022)](configs/beit) -- [x] [ConvNeXt (CVPR'2022)](configs/convnext) -- [x] [MAE (CVPR'2022)](configs/mae) - -Supported methods: - -- [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn) -- [x] [ERFNet (T-ITS'2017)](configs/erfnet) -- [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet) -- [x] [PSPNet (CVPR'2017)](configs/pspnet) -- [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3) -- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1) -- [x] [PSANet (ECCV'2018)](configs/psanet) -- [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus) -- [x] [UPerNet (ECCV'2018)](configs/upernet) -- [x] [ICNet (ECCV'2018)](configs/icnet) -- [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net) -- [x] [EncNet (CVPR'2018)](configs/encnet) -- [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn) -- [x] [DANet (CVPR'2019)](configs/danet) -- [x] [APCNet (CVPR'2019)](configs/apcnet) -- [x] [EMANet (ICCV'2019)](configs/emanet) -- [x] [CCNet (ICCV'2019)](configs/ccnet) -- [x] [DMNet (ICCV'2019)](configs/dmnet) -- [x] [ANN (ICCV'2019)](configs/ann) -- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet) -- [x] [FastFCN (ArXiv'2019)](configs/fastfcn) -- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn) -- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet) -- [x] [OCRNet (ECCV'2020)](configs/ocrnet) -- [x] [DNLNet (ECCV'2020)](configs/dnlnet) -- [x] [PointRend (CVPR'2020)](configs/point_rend) -- [x] [CGNet (TIP'2020)](configs/cgnet) -- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2) -- [x] [STDC (CVPR'2021)](configs/stdc) -- [x] [SETR (CVPR'2021)](configs/setr) -- [x] [DPT (ArXiv'2021)](configs/dpt) -- [x] [Segmenter (ICCV'2021)](configs/segmenter) -- [x] [SegFormer (NeurIPS'2021)](configs/segformer) -- [x] [K-Net (NeurIPS'2021)](configs/knet) - -Supported datasets: - -- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#cityscapes) -- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-voc) -- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k) -- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-context) -- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-10k) -- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-164k) -- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#chase-db1) -- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#drive) -- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#hrf) -- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#stare) -- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#dark-zurich) -- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#nighttime-driving) -- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#loveda) -- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-potsdam) -- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-vaihingen) -- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isaid) - -## Installation - -Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation. - -## Get Started - -Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation. -There are also tutorials for [customizing dataset](docs/en/tutorials/customize_datasets.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing modules](docs/en/tutorials/customize_models.md), and [customizing runtime](docs/en/tutorials/customize_runtime.md). -We also provide many [training tricks](docs/en/tutorials/training_tricks.md) for better training and [useful tools](docs/en/useful_tools.md) for deployment. - -A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab. - -Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. - -## Citation - -If you find this project useful in your research, please consider cite: - -```bibtex -@misc{mmseg2020, - title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark}, - author={MMSegmentation Contributors}, - howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}}, - year={2020} -} -``` - -## Contributing - -We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. - -## Acknowledgement - -MMSegmentation is an open source project that welcome any contribution and feedback. -We wish that the toolbox and benchmark could serve the growing research -community by providing a flexible as well as standardized toolkit to reimplement existing methods -and develop their own new semantic segmentation methods. - -## Projects in OpenMMLab - -- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. -- [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. diff --git a/cv/semantic_segmentation/apcnet/pytorch/README_zh-CN.md b/cv/semantic_segmentation/apcnet/pytorch/README_zh-CN.md deleted file mode 100644 index f7b3a91cd..000000000 --- a/cv/semantic_segmentation/apcnet/pytorch/README_zh-CN.md +++ /dev/null @@ -1,220 +0,0 @@ -
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- -[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmsegmentation)](https://pypi.org/project/mmsegmentation/) -[![PyPI](https://img.shields.io/pypi/v/mmsegmentation)](https://pypi.org/project/mmsegmentation) -[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/zh_CN/latest/) -[![badge](https://github.com/open-mmlab/mmsegmentation/workflows/build/badge.svg)](https://github.com/open-mmlab/mmsegmentation/actions) -[![codecov](https://codecov.io/gh/open-mmlab/mmsegmentation/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmsegmentation) -[![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE) -[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues) -[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues) - -文档: https://mmsegmentation.readthedocs.io/zh_CN/latest - -[English](README.md) | 简体中文 - -## 简介 - -MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 OpenMMLab 项目的一部分。 - -主分支代码目前支持 PyTorch 1.5 以上的版本。 - -![示例图片](resources/seg_demo.gif) - -### 主要特性 - -- **统一的基准平台** - - 我们将各种各样的语义分割算法集成到了一个统一的工具箱，进行基准测试。 - -- **模块化设计** - - MMSegmentation 将分割框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的分割模型。 - -- **丰富的即插即用的算法和模型** - - MMSegmentation 支持了众多主流的和最新的检测算法，例如 PSPNet，DeepLabV3，PSANet，DeepLabV3+ 等. - -- **速度快** - - 训练速度比其他语义分割代码库更快或者相当。 - -## 开源许可证 - -该项目采用 [Apache 2.0 开源许可证](LICENSE)。 - -## 更新日志 - -最新版本 v0.24.1 在 2022.5.1 发布。 -如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。 - -## 基准测试和模型库 - -测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。 - -已支持的骨干网络： - -- [x] ResNet (CVPR'2016) -- [x] ResNeXt (CVPR'2017) -- [x] [HRNet (CVPR'2019)](configs/hrnet) -- [x] [ResNeSt (ArXiv'2020)](configs/resnest) -- [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2) -- [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3) -- [x] [Vision Transformer (ICLR'2021)](configs/vit) -- [x] [Swin Transformer (ICCV'2021)](configs/swin) -- [x] [Twins (NeurIPS'2021)](configs/twins) -- [x] [BEiT (ICLR'2022)](configs/beit) -- [x] [ConvNeXt (CVPR'2022)](configs/convnext) -- [x] [MAE (CVPR'2022)](configs/mae) - -已支持的算法： - -- [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn) -- [x] [ERFNet (T-ITS'2017)](configs/erfnet) -- [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet) -- [x] [PSPNet (CVPR'2017)](configs/pspnet) -- [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3) -- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1) -- [x] [PSANet (ECCV'2018)](configs/psanet) -- [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus) -- [x] [UPerNet (ECCV'2018)](configs/upernet) -- [x] [ICNet (ECCV'2018)](configs/icnet) -- [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net) -- [x] [EncNet (CVPR'2018)](configs/encnet) -- [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn) -- [x] [DANet (CVPR'2019)](configs/danet) -- [x] [APCNet (CVPR'2019)](configs/apcnet) -- [x] [EMANet (ICCV'2019)](configs/emanet) -- [x] [CCNet (ICCV'2019)](configs/ccnet) -- [x] [DMNet (ICCV'2019)](configs/dmnet) -- [x] [ANN (ICCV'2019)](configs/ann) -- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet) -- [x] [FastFCN (ArXiv'2019)](configs/fastfcn) -- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn) -- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet) -- [x] [OCRNet (ECCV'2020)](configs/ocrnet) -- [x] [DNLNet (ECCV'2020)](configs/dnlnet) -- [x] [PointRend (CVPR'2020)](configs/point_rend) -- [x] [CGNet (TIP'2020)](configs/cgnet) -- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2) -- [x] [STDC (CVPR'2021)](configs/stdc) -- [x] [SETR (CVPR'2021)](configs/setr) -- [x] [DPT (ArXiv'2021)](configs/dpt) -- [x] [Segmenter (ICCV'2021)](configs/segmenter) -- [x] [SegFormer (NeurIPS'2021)](configs/segformer) -- [x] [K-Net (NeurIPS'2021)](configs/knet) - -已支持的数据集： - -- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#cityscapes) -- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-voc) -- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#ade20k) -- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#pascal-context) -- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-10k) -- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#coco-stuff-164k) -- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#chase-db1) -- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#drive) -- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#hrf) -- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#stare) -- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#dark-zurich) -- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#nighttime-driving) -- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#loveda) -- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-potsdam) -- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-vaihingen) -- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isaid) - -## 安装 - -请参考[快速入门文档](docs/zh_cn/get_started.md#installation)进行安装，参考[数据集准备](docs/zh_cn/dataset_prepare.md)处理数据。 - -## 快速入门 - -请参考[训练教程](docs/zh_cn/train.md)和[测试教程](docs/zh_cn/inference.md)学习 MMSegmentation 的基本使用。 -我们也提供了一些进阶教程，内容覆盖了[增加自定义数据集](docs/zh_cn/tutorials/customize_datasets.md)，[设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)，[增加自定义模型](docs/zh_cn/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)。 -除此之外，我们也提供了很多实用的[训练技巧说明](docs/zh_cn/tutorials/training_tricks.md)和模型部署相关的[有用的工具](docs/zh_cn/useful_tools.md)。 - -同时，我们提供了 Colab 教程。你可以在[这里](demo/MMSegmentation_Tutorial.ipynb)浏览教程，或者直接在 Colab 上[运行](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb)。 - -如果遇到问题，请参考 [常见问题解答](docs/zh_cn/faq.md)。 - -## 引用 - -如果你觉得本项目对你的研究工作有所帮助，请参考如下 bibtex 引用 MMSegmentation。 - -```bibtex -@misc{mmseg2020, - title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark}, - author={MMSegmentation Contributors}, - howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}}, - year={2020} -} -``` - -## 贡献指南 - -我们感谢所有的贡献者为改进和提升 MMSegmentation 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。 - -## 致谢 - -MMSegmentation 是一个由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。 - -## OpenMMLab 的其他项目 - -- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库 -- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口 -- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱 -- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱 -- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台 -- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准 -- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱 -- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包 -- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱 -- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准 -- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准 -- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准 -- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准 -- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱 -- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台 -- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准 -- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱 -- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱 -- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架 - -## 欢迎加入 OpenMMLab 社区 - - 扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 [OpenMMLab 团队](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) 以及 [MMSegmentation](https://jq.qq.com/?_wv=1027&k=ukevz6Ie) 的 QQ 群。 - -
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