diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,64 +1,27 @@
-
-
-
-
-
-
+# Kneron AI Training/Deployment Platform (mmsegmentation-based)
-[](https://pypi.org/project/mmsegmentation/)
-[](https://pypi.org/project/mmsegmentation)
-[](https://mmsegmentation.readthedocs.io/en/latest/)
-[](https://github.com/open-mmlab/mmsegmentation/actions)
-[](https://codecov.io/gh/open-mmlab/mmsegmentation)
-[](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
-[](https://github.com/open-mmlab/mmsegmentation/issues)
-[](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.
+ [kneron-mmsegmentation](https://github.com/kneron/kneron-mmsegmentation) is a platform built upon the well-known [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) for mmsegmentation. If you are looking for original mmsegmentation document, please visit [mmsegmentation docs](https://mmsegmentation.readthedocs.io/en/latest/) for detailed mmsegmentation usage.
-The master branch works with **PyTorch 1.5+**.
+ In this repository, we provide an end-to-end training/deployment flow to realize on Kneron's AI accelerators:
-
-
-### 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.
+ 1. **Training/Evalulation:**
+ - Modified model configuration file and verified for Kneron hardware platform
+ - Please see [Overview of Benchmark and Model Zoo](#Overview-of-Benchmark-and-Model-Zoo) for Kneron-Verified model list
+ 2. **Converting to ONNX:**
+ - tools/pytorch2onnx_kneron.py (beta)
+ - Export *optimized* and *Kneron-toolchain supported* onnx
+ - Automatically modify model for arbitrary data normalization preprocess
+ 3. **Evaluation**
+ - tools/test_kneron.py (beta)
+ - Evaluate the model with *pytorch checkpoint, onnx, and kneron-nef*
+ 4. **Testing**
+ - inference_kn (beta)
+ - Verify the converted [NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) model on Kneron USB accelerator with this API
+ 5. **Converting Kneron-NEF:** (toolchain feature)
+ - Convert the trained pytorch model to [Kneron-NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) model, which could be used on Kneron hardware platform.
## License
@@ -66,132 +29,40 @@ This project is released under the [Apache 2.0 license](LICENSE).
## Changelog
-v0.21.0 was released in 2/9/2022.
-Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
+N/A
-## Benchmark and model zoo
+## Overview of Benchmark and Kneron 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)
-
-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)
-
-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)
+| Backbone | Crop Size | Mem (GB) | mIoU | Config | Download |
+|:--------:|:---------:|:--------:|:----:|:------:|:--------:|
+| STDC 1 | 512x1024 | 7.15 | 69.29|[config](https://github.com/kneron/kneron-mmsegmentation/tree/master/configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py)|[model](https://github.com/kneron/Model_Zoo/blob/main/mmsegmentation/stdc_1/latest.zip)
## Installation
+- Please refer to the Step 1 of [docs_kneron/stdc_step_by_step.md#step-1-environment](docs_kneron/stdc_step_by_step.md) for installation.
+- Please refer to [Kneron PLUS - Python: Installation](http://doc.kneron.com/docs/#plus_python/introduction/install_dependency/) for the environment setup for Kneron USB accelerator.
-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.
+## Getting Started
+### Tutorial - Kneron Edition
+- [STDC-Seg: Step-By-Step](docs_kneron/stdc_step_by_step.md): A tutorial for users to get started easily. To see detailed documents, please see below.
-## Get Started
+### Documents - Kneron Edition
+- [Kneron ONNX Export] (under development)
+- [Kneron Inference] (under development)
+- [Kneron Toolchain Step-By-Step (YOLOv3)](http://doc.kneron.com/docs/#toolchain/yolo_example/)
+- [Kneron Toolchain Manual](http://doc.kneron.com/docs/#toolchain/manual/#0-overview)
-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.
-
-## 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}
-}
-```
+### Original mmsegmentation Documents
+- [Original mmsegmentation getting started](https://github.com/open-mmlab/mmsegmentation#getting-started): It is recommended to read the original mmsegmentation getting started documents for other mmsegmentation operations.
+- [Original mmsegmentation readthedoc](https://mmsegmentation.readthedocs.io/en/latest/): Original mmsegmentation documents.
## Contributing
+[kneron-mmsegmentation](https://github.com/kneron/kneron-mmsegmentation) a platform built upon [OpenMMLab-mmsegmentation](https://github.com/open-mmlab/mmsegmentation)
-We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
+- For issues regarding to the original [mmsegmentation](https://github.com/open-mmlab/mmsegmentation):
+We appreciate all contributions to improve [OpenMMLab-mmsegmentation](https://github.com/open-mmlab/mmsegmentation). Ongoing projects can be found in out [GitHub Projects](https://github.com/open-mmlab/mmsegmentation/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
-## Acknowledgement
+- For issues regarding to this repository [kneron-mmsegmentation](https://github.com/kneron/kneron-mmsegmentation): Welcome to leave the comment or submit pull requests here to improve kneron-mmsegmentation
-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.
-- [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.
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation 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.
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-- [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): A powerful toolkit for generative models.
-- [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
-- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab few shot learning 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.
-- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.
+## Related Projects
+- [kneron-mmdetection](https://github.com/kneron/kneron-mmdetection): Kneron training/deployment platform on [OpenMMLab - mmdetection](https://github.com/open-mmlab/mmdetection) object detection toolbox
diff --git a/README_zh-CN.md b/README_zh-CN.md
deleted file mode 100644
index 12b69a3..0000000
--- a/README_zh-CN.md
+++ /dev/null
@@ -1,211 +0,0 @@
-
-
-
-
-
-
-
-[](https://pypi.org/project/mmsegmentation/)
-[](https://pypi.org/project/mmsegmentation)
-[](https://mmsegmentation.readthedocs.io/zh_CN/latest/)
-[](https://github.com/open-mmlab/mmsegmentation/actions)
-[](https://codecov.io/gh/open-mmlab/mmsegmentation)
-[](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
-[](https://github.com/open-mmlab/mmsegmentation/issues)
-[](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。
-
-
-
-### 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 v0.21.1 在 2022.2.9 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)
-
-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)
-
-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)
-
-## 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)。
-
-## 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
-- [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
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
-- [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
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
-- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 生成模型工具箱
-- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
-- [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
-- [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。
-
-
-
- 我们会在 OpenMMLab 社区为大家
-
-- 📢 分享 AI 框架的前沿核心技术
-- 💻 解读 PyTorch 常用模块源码
-- 📰 发布 OpenMMLab 的相关新闻
-- 🚀 介绍 OpenMMLab 开发的前沿算法
-- 🏃 获取更高效的问题答疑和意见反馈
-- 🔥 提供与各行各业开发者充分交流的平台
-
- 干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
-