diff --git a/README.md b/README.md index 4901afa9..18419dee 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@
 
[![build](https://github.com/open-mmlab/mmocr/workflows/build/badge.svg)](https://github.com/open-mmlab/mmocr/actions) -[![docs](https://readthedocs.org/projects/mmocr/badge/?version=latest)](https://mmocr.readthedocs.io/en/latest/?badge=latest) +[![docs](https://readthedocs.org/projects/mmocr/badge/?version=dev-1.x)](https://mmocr.readthedocs.io/en/dev-1.x/?badge=dev-1.x) [![codecov](https://codecov.io/gh/open-mmlab/mmocr/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmocr) [![license](https://img.shields.io/github/license/open-mmlab/mmocr.svg)](https://github.com/open-mmlab/mmocr/blob/main/LICENSE) [![PyPI](https://badge.fury.io/py/mmocr.svg)](https://pypi.org/project/mmocr/) @@ -27,10 +27,10 @@ [![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmocr.svg)](https://github.com/open-mmlab/mmocr/issues) -[📘Documentation](https://mmocr.readthedocs.io/) | -[🛠️Installation](https://mmocr.readthedocs.io/en/latest/install.html) | -[👀Model Zoo](https://mmocr.readthedocs.io/en/latest/modelzoo.html) | -[🆕Update News](https://mmocr.readthedocs.io/en/latest/changelog.html) | +[📘Documentation](https://mmocr.readthedocs.io/en/dev-1.x/) | +[🛠️Installation](https://mmocr.readthedocs.io/en/dev-1.x/get_started/install.html) | +[👀Model Zoo](https://mmocr.readthedocs.io/en/dev-1.x/modelzoo.html) | +[🆕Update News](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) | [🤔Reporting Issues](https://github.com/open-mmlab/mmocr/issues/new/choose) @@ -48,7 +48,7 @@ MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detect The main branch works with **PyTorch 1.6+**.
- +
### Major Features @@ -63,7 +63,7 @@ The main branch works with **PyTorch 1.6+**. - **Modular Design** - The modular design of MMOCR enables users to define their own optimizers, data preprocessors, and model components such as backbones, necks and heads as well as losses. Please refer to [Getting Started](https://mmocr.readthedocs.io/en/latest/getting_started.html) for how to construct a customized model. + The modular design of MMOCR enables users to define their own optimizers, data preprocessors, and model components such as backbones, necks and heads as well as losses. Please refer to [Overview](https://mmocr.readthedocs.io/en/dev-1.x/get_started/overview.html) for how to construct a customized model. - **Numerous Utilities** @@ -71,37 +71,42 @@ The main branch works with **PyTorch 1.6+**. ## What's New -v0.6.0 was released in 2022-05-05. +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. -1. We support [MASTER](https://arxiv.org/abs/1910.02562) and [DBNet++](https://arxiv.org/abs/2202.10304) now! -2. Three dataset converters are added: LSVT, RCTW, and HierText. -3. MMOCR now can load data from LMDB dataset. [Doc](https://mmocr.readthedocs.io/en/latest/tools.html#convert-text-recognition-dataset-to-lmdb-format) -4. We provide a script to convert .json labels obtained by **Labelme** into MMOCR-supported data format. +2. **Unified interfaces**. As a part of the OpenMMLab 2.0 projects, MMOCR 1.x unifies and refactors the interfaces and internal logics of train, testing, datasets, models, evaluation, and visualization. All the OpenMMLab 2.0 projects share the same design in those interfaces and logics to allow the emergence of multi-task/modality algorithms. -Read [Changelog](https://mmocr.readthedocs.io/en/latest/changelog.html) for more details! +3. **Cross project calling**. Benefiting from the unified design, you can use the models implemented in other OpenMMLab projects, such as MMDet. We provide an example of how to use MMDetection's Mask R-CNN through `MMDetWrapper`. Check our documents for more details. More wrappers will be released in the future. + +4. **Stronger visualization**. We provide a series of useful tools which are mostly based on brand-new visualizers. As a result, it is more convenient for the users to explore the models and datasets now. + +5. **More documentation and tutorials**. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it [here](https://mmocr.readthedocs.io/en/dev-1.x/). + +Read [Changelog](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) for more details! ## Installation -MMOCR depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection). +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). Below are quick steps for installation. -Please refer to [Install Guide](https://mmocr.readthedocs.io/en/latest/install.html) for more detailed instruction. +Please refer to [Install Guide](https://mmocr.readthedocs.io/en/dev-1.x/get_started/install.html) for more detailed instruction. ```shell conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y conda activate open-mmlab pip3 install openmim -mim install mmcv-full -mim install mmdet +mim install mmengine +mim install 'mmcv>=2.0.0rc1' +mim install 'mmdet>=3.0.0rc0' git clone https://github.com/open-mmlab/mmocr.git cd mmocr +git checkout 1.x pip3 install -e . ``` ## Get Started -Please see [Getting Started](https://mmocr.readthedocs.io/en/latest/getting_started.html) for the basic usage of MMOCR. +Please see [Quick Run](https://mmocr.readthedocs.io/en/dev-1.x/get_started/quick_run.html) for the basic usage of MMOCR. -## [Model Zoo](https://mmocr.readthedocs.io/en/latest/modelzoo.html) +## [Model Zoo](https://mmocr.readthedocs.io/en/dev-1.x/modelzoo.html) Supported algorithms: @@ -138,7 +143,7 @@ Supported algorithms: -Please refer to [model_zoo](https://mmocr.readthedocs.io/en/latest/modelzoo.html) for more details. +Please refer to [model_zoo](https://mmocr.readthedocs.io/en/dev-1.x/modelzoo.html) for more details. ## Contributing @@ -168,6 +173,7 @@ This project is released under the [Apache 2.0 license](LICENSE). ## Projects in OpenMMLab +- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages. - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. diff --git a/README_zh-CN.md b/README_zh-CN.md index 014fd9ae..919a522b 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -19,7 +19,7 @@
 
[![build](https://github.com/open-mmlab/mmocr/workflows/build/badge.svg)](https://github.com/open-mmlab/mmocr/actions) -[![docs](https://readthedocs.org/projects/mmocr/badge/?version=latest)](https://mmocr.readthedocs.io/en/latest/?badge=latest) +[![docs](https://readthedocs.org/projects/mmocr/badge/?version=dev-1.x)](https://mmocr.readthedocs.io/en/dev-1.x/?badge=dev-1.x) [![codecov](https://codecov.io/gh/open-mmlab/mmocr/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmocr) [![license](https://img.shields.io/github/license/open-mmlab/mmocr.svg)](https://github.com/open-mmlab/mmocr/blob/main/LICENSE) [![PyPI](https://badge.fury.io/py/mmocr.svg)](https://pypi.org/project/mmocr/) @@ -27,10 +27,10 @@ [![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmocr.svg)](https://github.com/open-mmlab/mmocr/issues) -[📘文档](https://mmocr.readthedocs.io/zh_CN/latest/) | -[🛠️安装](https://mmocr.readthedocs.io/zh_CN/latest/install.html) | -[👀模型库](https://mmocr.readthedocs.io/zh_CN/latest/modelzoo.html) | -[🆕更新日志](https://mmocr.readthedocs.io/zh_CN/latest/changelog.html) | +[📘文档](https://mmocr.readthedocs.io/zh_CN/dev-1.x/) | +[🛠️安装](https://mmocr.readthedocs.io/zh_CN/dev-1.x/get_started/install.html) | +[👀模型库](https://mmocr.readthedocs.io/zh_CN/dev-1.x/modelzoo.html) | +[🆕更新日志](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) | [🤔报告问题](https://github.com/open-mmlab/mmocr/issues/new/choose) @@ -48,7 +48,7 @@ MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检 主分支目前支持 **PyTorch 1.6 以上**的版本。
- +
### 主要特性 @@ -64,7 +64,7 @@ MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检 -**模块化设计** MMOCR 的模块化设计使用户可以定义自己的优化器，数据预处理器，模型组件如主干模块，颈部模块和头部模块，以及损失函数。有关如何构建自定义模型的信 -息，请参考[快速入门](https://mmocr.readthedocs.io/zh_CN/latest/getting_started.html)。 +息，请参考[概览](https://mmocr.readthedocs.io/zh_CN/dev-1.x/get_started/overview.html)。 -**众多实用工具** @@ -72,36 +72,41 @@ MMOCR 的模块化设计使用户可以定义自己的优化器，数据预处 ## 最新进展 -最新的月度版本 v0.6.0 在 2022.05.05 发布。 +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。 -1. 支持了两个新模型：[MASTER](https://arxiv.org/abs/1910.02562) 和 [DBNet++](https://arxiv.org/abs/2202.10304) -2. 新支持了三个数据集：LSVT, RCTW 和 HierText -3. 支持从 LMDB 数据集读取数据 [查看文档](https://mmocr.readthedocs.io/en/latest/tools.html#convert-text-recognition-dataset-to-lmdb-format) -4. 提供了 Labelme 数据格式转换脚本，可使 MMOCR 支持由 Labelme 标注的数据 +2. 统一接口：MMOCR 1.x 统一了数据集、模型、评估和可视化的接口和内部逻辑。支持更强的扩展性。 -阅读[更新日志](https://mmocr.readthedocs.io/en/latest/changelog.html)以获取更多信息。 +3. 跨项目调用：受益于统一的设计，你可以使用其他OpenMMLab项目中实现的模型，如MMDet。 我们提供了一个例子，说明如何通过MMDetWrapper使用MMDetection的Mask R-CNN。查看我们的文档以了解更多细节。更多的包装器将在未来发布。 + +4. 更强的可视化：我们提供了一系列可视化工具， 用户现在可以更方便可视化数据。 + +5. 更多的文档和教程：我们增加了更多的教程，降低用户的学习门槛。详见[教程](https://mmocr.readthedocs.io/zh_CN/dev-1.x/)。 + +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。 ## 安装 -MMOCR 依赖 [PyTorch](https://pytorch.org/), [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。 -更详细的安装指南请参考 [安装文档](https://mmocr.readthedocs.io/zh_CN/latest/install.html)。 +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。 +更详细的安装指南请参考 [安装文档](https://mmocr.readthedocs.io/zh_CN/dev-1.x/get_started/install.html)。 ```shell conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y conda activate open-mmlab pip3 install openmim -mim install mmcv-full -mim install mmdet +mim install mmengine +mim install 'mmcv>=2.0.0rc1' +mim install 'mmdet>=3.0.0rc0' git clone https://github.com/open-mmlab/mmocr.git cd mmocr +git checkout 1.x pip3 install -e . ``` ## 快速入门 -请参考[快速入门](https://mmocr.readthedocs.io/zh_CN/latest/getting_started.html)文档学习 MMOCR 的基本使用。 +请参考[快速入门](https://mmocr.readthedocs.io/zh_CN/dev-1.x/get_started/quick_run.html)文档学习 MMOCR 的基本使用。 -## [模型库](https://mmocr.readthedocs.io/en/latest/modelzoo.html) +## [模型库](https://mmocr.readthedocs.io/zh_CN/dev-1.x/modelzoo.html) 支持的算法： @@ -138,7 +143,7 @@ pip3 install -e . -请点击[模型库](https://mmocr.readthedocs.io/en/latest/modelzoo.html)查看更多关于上述算法的详细信息。 +请点击[模型库](https://mmocr.readthedocs.io/zh_CN/dev-1.x/modelzoo.html)查看更多关于上述算法的详细信息。 ## 贡献指南 @@ -167,6 +172,7 @@ MMOCR 是一款由来自不同高校和企业的研发人员共同参与贡献 ## OpenMMLab 的其他项目 +- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库 - [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱