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Bump version to 1.0.0rc5 (#1662)
* Bump version to 1.0.0rc5 * fix * update
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‎README.md

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The toolbox provides a comprehensive set of utilities which can help users assess the performance of models. It includes visualizers which allow visualization of images, ground truths as well as predicted bounding boxes, and a validation tool for evaluating checkpoints during training. It also includes data converters to demonstrate how to convert your own data to the annotation files which the toolbox supports.
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## What's New
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## Latest Updates
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v1.0.0rc5 was released in 2023-01-06.
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1. Two models, Aster and SVTR, are added to our model zoo. The full implementation of ABCNet is also available now.
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2. Dataset Preparer supports 5 more datasets: CocoTextV2, FUNSD, TextOCR, NAF, SROIE.
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3. We have 4 more text recognition transforms, and two more helper transforms.
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4. The transform, `FixInvalidPolygon`, is getting smarter at dealing with invalid polygons, and now capable of handling more weird annotations. As a result, a complete training cycle on TotalText dataset can be performed bug-free. The weights of DBNet and FCENet pretrained on TotalText are also released.
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Read [Changelog](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) for more details!
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## What's New in MMOCR 1.0
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1. **New engines**. MMOCR 1.x is based on [MMEngine](https://github.com/open-mmlab/mmengine), which provides a general and powerful runner that allows more flexible customizations and significantly simplifies the entrypoints of high-level interfaces.
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8. **More models**. MMOCR 1.0 supports more tasks and more state-of-the-art models!
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Read [Changelog](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) for more details!
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## Installation
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MMOCR depends on [PyTorch](https://pytorch.org/), [MMEngine](https://github.com/open-mmlab/mmengine), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection).

‎README_zh-CN.md

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该工具箱提供了一套全面的实用程序，可以帮助用户评估模型的性能。它包括可对图像，标注的真值以及预测结果进行可视化的可视化工具，以及用于在训练过程中评估模型的验证工具。它还包括数据转换器，演示了如何将用户自建的标注数据转换为 MMOCR 支持的标注文件。
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## 最新进展
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## 近期更新
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最新的版本 v1.0.0rc5 于 2023-01-06 发布。
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1. 新增了 Aster 和 SVTR 模型，并完整支持了 ABCNet 的训练与测试；
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2. Dataset Preparer 新支持了5个数据集：CocoTextV2, FUNSD, TextOCR, NAF, SROIE；
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3. 新增了4个文本识别以及2个辅助运行过程的数据变换。
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4. 数据变换 `FixInvalidPolygon` 现在可以处理更多种类的非法多边形，因此各模型如今也可以顺畅地在 TotalText 数据集上完成训练流程。我们同时也发布了 DBNet 和 FCENet 在 TotalText 上的预训练模型。
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阅读[更新日志](https://mmocr.readthedocs.io/zh_CN/dev-1.x/notes/changelog.html)以获取更多信息。
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## MMOCR 1.0 更新汇总
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1. 架构升级：MMOCR 1.x 是基于 [MMEngine](https://github.com/open-mmlab/mmengine)，提供了一个通用的、强大的执行器，允许更灵活的定制，提供了统一的训练和测试入口。
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8. 更多新模型：MMOCR 1.0 支持了更多模型和模型种类。
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阅读[更新日志](https://mmocr.readthedocs.io/zh_CN/dev-1.x/notes/changelog.html)以获取更多信息。
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## 安装
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MMOCR 依赖 [PyTorch](https://pytorch.org/), [MMEngine](https://github.com/open-mmlab/mmengine), [MMCV](https://github.com/open-mmlab/mmcv) 和 [MMDetection](https://github.com/open-mmlab/mmdetection)，以下是安装的简要步骤。

‎docs/en/get_started/install.md

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| MMOCR | MMEngine | MMCV | MMDetection |
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| -------------- | --------------------------- | -------------------------- | --------------------------- |
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| dev-1.x | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
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| 1.0.0rc4 | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
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| 1.0.0rc\[4-5\] | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
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| 1.0.0rc\[0-3\] | 0.0.0 \<= mmengine \< 0.2.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |

‎docs/en/notes/changelog.md

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# Changelog of v1.x
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## v1.0.0rc5 (06/01/2023)
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### Highlights
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1. Two models, Aster and SVTR, are added to our model zoo. The full implementation of ABCNet is also available now.
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2. Dataset Preparer supports 5 more datasets: CocoTextV2, FUNSD, TextOCR, NAF, SROIE.
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3. We have 4 more text recognition transforms, and two helper transforms. See https://github.com/open-mmlab/mmocr/pull/1646 https://github.com/open-mmlab/mmocr/pull/1632 https://github.com/open-mmlab/mmocr/pull/1645 for details.
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4. The transform, `FixInvalidPolygon`, is getting smarter at dealing with invalid polygons, and now capable of handling more weird annotations. As a result, a complete training cycle on TotalText dataset can be performed bug-free. The weights of DBNet and FCENet pretrained on TotalText are also released.
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### New Features & Enhancements
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- Update ic15 det config according to DataPrepare by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1617
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- Refactor icdardataset metainfo to lowercase. by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1620
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- Add ASTER Encoder by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1239
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- Add ASTER decoder by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1625
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- Add ASTER config by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1238
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- Update ASTER config by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1629
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- Support browse_dataset.py to visualize original dataset by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1503
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- Add CocoTextv2 to dataset preparer by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1514
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- Add Funsd to dataset preparer by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1550
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- Add TextOCR to Dataset Preparer by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1543
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- Refine example projects and readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1628
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- Enhance FixInvalidPolygon, add RemoveIgnored transform by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1632
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- ConditionApply by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1646
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- Add NAF to dataset preparer by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1609
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- Add SROIE to dataset preparer by @FerryHuang in https://github.com/open-mmlab/mmocr/pull/1639
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- Add svtr decoder by @willpat1213 in https://github.com/open-mmlab/mmocr/pull/1448
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- Add missing unit tests by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1651
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- Add svtr encoder by @willpat1213 in https://github.com/open-mmlab/mmocr/pull/1483
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- ABCNet train by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1610
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- Totaltext cfgs for DB and FCE by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1633
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- Add Aliases to models by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1611
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- SVTR transforms by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1645
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- Add SVTR framework and configs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1621
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- Issue Template by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1663
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### Docs
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- Add Chinese translation for browse_dataset.py by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1647
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- updata abcnet doc by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1658
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- update the dbnetpp\`s readme file by @zhuyue66 in https://github.com/open-mmlab/mmocr/pull/1626
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### Bug Fixes
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- nn.SmoothL1Loss beta can not be zero in PyTorch 1.13 version by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1616
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- ctc loss bug if target is empty by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1618
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- Add torch 1.13 by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1619
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- Remove outdated tutorial link by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1627
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- Dev 1.x some doc mistakes by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1630
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- Support custom font to visualize some languages (e.g. Korean) by @ProtossDragoon in https://github.com/open-mmlab/mmocr/pull/1567
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- db_module_loss，negative number encountered in sqrt by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1640
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- Use int instead of np.int by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1636
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- Remove support for py3.6 by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1660
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### New Contributors
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- @zhuyue66 made their first contribution in https://github.com/open-mmlab/mmocr/pull/1626
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- @KevinNuNu made their first contribution in https://github.com/open-mmlab/mmocr/pull/1630
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- @FerryHuang made their first contribution in https://github.com/open-mmlab/mmocr/pull/1639
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- @willpat1213 made their first contribution in https://github.com/open-mmlab/mmocr/pull/1448
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**Full Changelog**: https://github.com/open-mmlab/mmocr/compare/v1.0.0rc4...v1.0.0rc5
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## v1.0.0rc4 (06/12/2022)
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### Highlights

‎docs/zh_cn/get_started/install.md

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| MMOCR | MMEngine | MMCV | MMDetection |
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| -------------- | --------------------------- | -------------------------- | --------------------------- |
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| dev-1.x | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
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| 1.0.0rc4 | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
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| 1.0.0rc\[4-5\] | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
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| 1.0.0rc\[0-3\] | 0.0.0 \<= mmengine \< 0.2.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |

‎mmocr/version.py

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# Copyright (c) Open-MMLab. All rights reserved.
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__version__ = '1.0.0rc4'
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__version__ = '1.0.0rc5'
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short_version = __version__

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