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Bump version to v1.0.0rc5. (#1286)
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mzr1996 authored Dec 30, 2022
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8 changes: 8 additions & 0 deletions README.md
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Expand Up @@ -58,6 +58,14 @@ The `1.x` branch works with **PyTorch 1.6+**.

## What's new

v1.0.0rc5 was released in 30/12/2022

- Support **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** and **MixMIM** backbones.
- Reproduce the training accuracy of **ConvNeXt** and **RepVGG**.
- Support **multi-task** training and testing. See [#1229](https://github.com/open-mmlab/mmclassification/pull/1229) for more details.
- Support Test-time Augmentation. See [#1161](https://github.com/open-mmlab/mmclassification/pull/1161) for
more details.

v1.0.0rc4 was released in 06/12/2022.

- Upgrade API to get pre-defined models of MMClassification. See [#1236](https://github.com/open-mmlab/mmclassification/pull/1236) for more details.
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7 changes: 7 additions & 0 deletions README_zh-CN.md
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Expand Up @@ -57,6 +57,13 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱,是 [O

## 更新日志

2022/12/30 发布了 v1.0.0rc5 版本

- 支持了**EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT****MixMIM** 等骨干网络结构
- 复现了 ConvNeXt 和 RepVGG 的训练精度。
- 支持了 **多任务** 训练和测试,详见 [#1229](https://github.com/open-mmlab/mmclassification/pull/1229)
- 支持了测试时增强(TTA),详见 [#1161](https://github.com/open-mmlab/mmclassification/pull/1161)

2022/12/06 发布了 v1.0.0rc4 版本

- 更新了主要 API 接口,用以方便地获取 MMClassification 中预定义的模型。详见 [#1236](https://github.com/open-mmlab/mmclassification/pull/1236)
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36 changes: 36 additions & 0 deletions docs/en/notes/changelog.md
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# Changelog

## v1.0.0rc5(30/12/2022)

### Highlights

- Support EVA, RevViT, EfficientnetV2, CLIP, TinyViT and MixMIM backbones.
- Reproduce the training accuracy of ConvNeXt and RepVGG.
- Support multi-task training and testing.
- Support Test-time Augmentation.

### New Features

- [Feature] Add EfficientnetV2 Backbone. ([#1253](https://github.com/open-mmlab/mmclassification/pull/1253))
- [Feature] Support TTA and add `--tta` in `tools/test.py`. ([#1161](https://github.com/open-mmlab/mmclassification/pull/1161))
- [Feature] Support Multi-task. ([#1229](https://github.com/open-mmlab/mmclassification/pull/1229))
- [Feature] Add clip backbone. ([#1258](https://github.com/open-mmlab/mmclassification/pull/1258))
- [Feature] Add mixmim backbone with checkpoints. ([#1224](https://github.com/open-mmlab/mmclassification/pull/1224))
- [Feature] Add TinyViT for dev-1.x. ([#1042](https://github.com/open-mmlab/mmclassification/pull/1042))
- [Feature] Add some scripts for development. ([#1257](https://github.com/open-mmlab/mmclassification/pull/1257))
- [Feature] Support EVA. ([#1239](https://github.com/open-mmlab/mmclassification/pull/1239))
- [Feature] Implementation of RevViT. ([#1127](https://github.com/open-mmlab/mmclassification/pull/1127))

### Improvements

- [Reproduce] Reproduce RepVGG Training Accuracy. ([#1264](https://github.com/open-mmlab/mmclassification/pull/1264))
- [Enhance] Support ConvNeXt More Weights. ([#1240](https://github.com/open-mmlab/mmclassification/pull/1240))
- [Reproduce] Update ConvNeXt config files. ([#1256](https://github.com/open-mmlab/mmclassification/pull/1256))
- [CI] Update CI to test PyTorch 1.13.0. ([#1260](https://github.com/open-mmlab/mmclassification/pull/1260))
- [Project] Add ACCV workshop 1st Solution. ([#1245](https://github.com/open-mmlab/mmclassification/pull/1245))
- [Project] Add Example project. ([#1254](https://github.com/open-mmlab/mmclassification/pull/1254))

### Bug Fixes

- [Fix] Fix imports in transforms. ([#1255](https://github.com/open-mmlab/mmclassification/pull/1255))
- [Fix] Fix CAM visualization. ([#1248](https://github.com/open-mmlab/mmclassification/pull/1248))
- [Fix] Fix the requirements and lazy register mmcls models. ([#1275](https://github.com/open-mmlab/mmclassification/pull/1275))

## v1.0.0rc4(06/12/2022)

### Highlights
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2 changes: 1 addition & 1 deletion mmcls/__init__.py
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mmcv_maximum_version = '2.0.0'
mmcv_version = digit_version(mmcv.__version__)

mmengine_minimum_version = '0.2.0'
mmengine_minimum_version = '0.4.0'
mmengine_maximum_version = '1.0.0'
mmengine_version = digit_version(mmengine.__version__)

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2 changes: 1 addition & 1 deletion mmcls/version.py
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# Copyright (c) OpenMMLab. All rights reserved

__version__ = '1.0.0rc4'
__version__ = '1.0.0rc5'


def parse_version_info(version_str):
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2 changes: 1 addition & 1 deletion requirements/mminstall.txt
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mmcv>=2.0.0rc1,<=2.0.0
mmengine>=0.2.0,<1.0.0
mmengine>=0.4.0,<1.0.0

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