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[Doc] Update README for new models (PaddlePaddle#1684)
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README.md

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@@ -35,7 +35,6 @@ Welcome to PaddleSeg! PaddleSeg is an end-to-end image segmentation development
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---------------
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## Core Features
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* <img src="./docs/images/f1.png" width="20"/> **High-Performance Model**: Based on the high-performance backbone trained by Baidu's self-developed [semi-supervised label knowledge distillation scheme (SSLD)](https://paddleclas.readthedocs.io/zh_CN/latest/advanced_tutorials/distillation/distillation.html#ssld), combined with the state of the art segmentation technology, we provide 50+ high-quality pre-training models, which are better than other open-source implementations.
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* [CVPR Champion Solution](./contrib/AutoNUE)
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* [Interactive Segmentation](./EISeg)
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* [Matting](./contrib/Matting)
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* [Domain Adaptation](./contrib/DomainAdaptation)
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## Feedbacks and Contact
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* The dynamic version is still under development, if you find any issue or have an idea on new features, please don't hesitate to contact us via [GitHub Issues](https://github.com/PaddlePaddle/PaddleSeg/issues).
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* Thanks [zjhellofss](https://github.com/zjhellofss) (Fu Shenshen) for contributing Attention U-Net, and Dice Loss.
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* Thanks [liuguoyu666](https://github.com/liguoyu666), [geoyee](https://github.com/geoyee) for contributing U-Net++ and U-Net3+.
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* Thanks [yazheng0307](https://github.com/yazheng0307) (LIU Zheng) for contributing quick-start document.
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* Thanks [CuberrChen](https://github.com/CuberrChen) for contributing STDC (rethink BiSeNet), PointRend and DetailAggregateLoss.
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* Thanks [CuberrChen](https://github.com/CuberrChen) for contributing STDC(rethink BiSeNet), PointRend and DetailAggregateLoss.
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* Thanks [stuartchen1949](https://github.com/stuartchen1949) for contributing SegNet.
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* Thanks [justld](https://github.com/justld) (Lang Du) for contributing ESPNet,HRNet_W48_Contrast and PixelContrastCrossEntropyLoss.
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* Thanks [justld](https://github.com/justld) (Lang Du) for contributing ESPNetV2, DMNet, ENCNet, HRNet_W48_Contrast, SECrossEntropyLoss and PixelContrastCrossEntropyLoss.
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* Thanks [Herman-Hu-saber](https://github.com/Herman-Hu-saber) (Hu Huiming) for contributing ESPNet.
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* Thanks [zhangjin12138](https://github.com/zhangjin12138) for contributing RandomCenterCrop.
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* Thanks [simuler](https://github.com/simuler) for contributing ESPNetV1.
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* Thanks [ETTR123](https://github.com/ETTR123)(Zhang Kai) for contributing PFPNNet.
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## Citation
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If you find our project useful in your research, please consider citing:

README_CN.md

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- [全景分割](./contrib/PanopticDeepLab)
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- [交互式分割](./EISeg)
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- [深度抠图](./contrib/Matting)
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- [领域自适应](./contrib/DomainAdaptation)
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## 代码贡献
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- 非常感谢[yazheng0307](https://github.com/yazheng0307) (刘正)贡献快速开始教程文档。
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- 非常感谢[CuberrChen](https://github.com/CuberrChen)贡献STDC (rethink BiSeNet) PointRend,和 Detail Aggregate损失函数。
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- 非常感谢[stuartchen1949](https://github.com/stuartchen1949)贡献 SegNet。
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- 非常感谢[justld](https://github.com/justld)(郎督)贡献 ESPNet,HRNet_W48_Contrast 和 PixelContrastCrossEntropyLoss 损失函数
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- 非常感谢[justld](https://github.com/justld)(郎督)贡献 ESPNetV2, DMNet, ENCNet, HRNet_W48_Contrast, SECrossEntropyLoss 和PixelContrastCrossEntropyLoss
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- 非常感谢[Herman-Hu-saber](https://github.com/Herman-Hu-saber)(胡慧明)参与贡献 ESPNet。
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- 非常感谢[zhangjin12138](https://github.com/zhangjin12138)贡献数据增强方法 RandomCenterCrop。
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- 非常感谢[simuler](https://github.com/simuler) 贡献 ESPNetV1。
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- 非常感谢[ETTR123](https://github.com/ETTR123)(张恺) 贡献 PFPNNet。
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## 学术引用 <img src="./docs/images/yinyong.png" width="30"/>

configs/espnet/README.md

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#### Additional Requirement
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- paddlepaddle >= 2.2.0
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- ESPNetV2 does not support exporting.
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- paddlepaddle develop version after 20211230

docs/model_zoo_overview.md

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|[SegNet](../configs/segnet)|-|-|-|-|
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|[ESPNetV2](../configs/espnet)|-|-|-|-|
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|[HRNetW48Contrast](../configs/hrnet_w48_contrast)|-|-|-||
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|[DMNet](../configs/dmnet)|-||-|-|
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|[ESPNetV1](../configs/espnetv1)|-|-|-|-|
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|[ENCNet](../configs/encnet)|-||-|-|
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|[PFPNNet](../configs/pfpn)|-||-|-|
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### Transformer系列
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* [SETR](../configs/setr)

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