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Bump to v0.18.0 (#940)
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* bump to v0.18.0

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Junjun2016 authored Oct 7, 2021
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -49,7 +49,7 @@ This project is released under the [Apache 2.0 license](LICENSE).

## Changelog

v0.17.0 was released in 09/01/2021.
v0.18.0 was released in 10/07/2021.
Please refer to [changelog.md](docs/changelog.md) for details and release history.

## Benchmark and model zoo
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2 changes: 1 addition & 1 deletion README_zh-CN.md
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Expand Up @@ -48,7 +48,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O

## 更新日志

最新的月度版本 v0.17.0 在 2021.09.01 发布。
最新的月度版本 v0.18.0 在 2021.10.07 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/changelog.md)

## 基准测试和模型库
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31 changes: 31 additions & 0 deletions docs/changelog.md
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## Changelog

### V0.18 (10/07/2021)

**Highlights**

- Support three real-time segmentation models (ICNet [#884](https://github.com/open-mmlab/mmsegmentation/pull/884), BiSeNetV1 [#851](https://github.com/open-mmlab/mmsegmentation/pull/851), and BiSeNetV2 [#804](https://github.com/open-mmlab/mmsegmentation/pull/804))
- Support one efficient segmentation model (FastFCN [#885](https://github.com/open-mmlab/mmsegmentation/pull/885))
- Support one efficient non-local/self-attention based segmentation model (ISANet [#70](https://github.com/open-mmlab/mmsegmentation/pull/70))
- Support COCO-Stuff 10k and 164k datasets ([#625](https://github.com/open-mmlab/mmsegmentation/pull/625))
- Support evaluate concated dataset separately ([#833](https://github.com/open-mmlab/mmsegmentation/pull/833))
- Support loading GT for evaluation from multi-file backend ([#867](https://github.com/open-mmlab/mmsegmentation/pull/867))

**New Features**

- Support three real-time segmentation models (ICNet [#884](https://github.com/open-mmlab/mmsegmentation/pull/884), BiSeNetV1 [#851](https://github.com/open-mmlab/mmsegmentation/pull/851), and BiSeNetV2 [#804](https://github.com/open-mmlab/mmsegmentation/pull/804))
- Support one efficient segmentation model (FastFCN [#885](https://github.com/open-mmlab/mmsegmentation/pull/885))
- Support one efficient non-local/self-attention based segmentation model (ISANet [#70](https://github.com/open-mmlab/mmsegmentation/pull/70))
- Support COCO-Stuff 10k and 164k datasets ([#625](https://github.com/open-mmlab/mmsegmentation/pull/625))
- Support evaluate concated dataset separately ([#833](https://github.com/open-mmlab/mmsegmentation/pull/833))

**Improvements**

- Support loading GT for evaluation from multi-file backend ([#867](https://github.com/open-mmlab/mmsegmentation/pull/867))
- Auto-convert SyncBN to BN when training on DP automatly([#772](https://github.com/open-mmlab/mmsegmentation/pull/772))
- Refactor Swin-Transformer ([#800](https://github.com/open-mmlab/mmsegmentation/pull/800))

**Bug Fixes**

- Update mmcv installation in dockerfile ([#860](https://github.com/open-mmlab/mmsegmentation/pull/860))
- Fix number of iteration bug when resuming checkpoint in distributed train ([#866](https://github.com/open-mmlab/mmsegmentation/pull/866))
- Fix parsing parse in val_step ([#906](https://github.com/open-mmlab/mmsegmentation/pull/906))

### V0.17 (09/01/2021)

**Highlights**
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1 change: 1 addition & 0 deletions docs/get_started.md
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Expand Up @@ -12,6 +12,7 @@ The compatible MMSegmentation and MMCV versions are as below. Please install the
| MMSegmentation version | MMCV version |
|:-------------------:|:-------------------:|
| master | mmcv-full>=1.3.13, <1.4.0 |
| 0.18.0 | mmcv-full>=1.3.13, <1.4.0 |
| 0.17.0 | mmcv-full>=1.3.7, <1.4.0 |
| 0.16.0 | mmcv-full>=1.3.7, <1.4.0 |
| 0.15.0 | mmcv-full>=1.3.7, <1.4.0 |
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1 change: 1 addition & 0 deletions docs_zh-CN/get_started.md
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| MMSegmentation 版本 | MMCV 版本 |
|:-------------------:|:-------------------:|
| master | mmcv-full>=1.3.13, <1.4.0 |
| 0.18.0 | mmcv-full>=1.3.13, <1.4.0 |
| 0.17.0 | mmcv-full>=1.3.7, <1.4.0 |
| 0.16.0 | mmcv-full>=1.3.7, <1.4.0 |
| 0.15.0 | mmcv-full>=1.3.7, <1.4.0 |
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2 changes: 1 addition & 1 deletion mmseg/version.py
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# Copyright (c) Open-MMLab. All rights reserved.

__version__ = '0.17.0'
__version__ = '0.18.0'


def parse_version_info(version_str):
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