Skip to content

update github.io docs #730

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Aug 1, 2024
Merged

update github.io docs #730

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
139 changes: 139 additions & 0 deletions CONTRIBUTING_CN.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,139 @@
# MindOCR 贡献指南

欢迎投稿，非常感谢！每一点帮助都有作用，功劳也总是会被认可的。

## 贡献者许可协议

在您首次向 MindOCR 社区提交代码之前，需要签署 CLA。

对于个人贡献者，请参阅[ICLA在线文档]（https://www.mindspore.cn/icla）以获取详细信息。

## 贡献类型

### 报告 Bugs

在 https://github.com/mindspore-lab/mindocr/issues 报告bug。

如果您要报告bug，请包括：

* 您的操作系统名称和版本。
* 有关您的本地设置的任何详细信息，可能有助于故障排除。
* 重现错误的详细步骤。

### 修复 Bugs

查看GitHub问题中的错误。任何标记为“bug”和“help wanted”的内容，都向任何想要修复它的人开放。

### 实现特性

查看 GitHub 问题以获取功能。任何带有“enhancement”和“help wanted”的标签，都对任何想要实现它的人开放。

### 编写文档

MindOCR总是可以使用更多的文档，无论是作为官方MindOCR文档的一部分，在文档字符串中，还是在博客文章、文章等网络资源中。

### 提交反馈

发送反馈的最佳方式是向 https://github.com/mindspore-lab/mindocr/issues 提交问题。

如果您正在提议一项功能：

* 详细解释它是如何工作的。
* 尽可能缩小范围，以便于实施。
* 请记住，这是一个志愿者驱动的项目，欢迎做出贡献:)

## 入门

准备好做出贡献了吗？以下是为本地开发设置“mindocr”的方法。

1. 在 [GitHub]（https://github.com/mindspore-lab/mindocr）上fork 'mindocr' 仓库。
2. 在本地克隆你的 fork：

```shell
git clone git@github.com:your_name_here/mindocr.git
```

之后，您应该将官方仓库添加为上游仓库：

```shell
git remote add upstream git@github.com:mindspore-lab/mindocr
```

3. 将本地副本安装到 conda 环境中。假设你已经安装了 conda，以下是你如何设置你的分支进行本地开发的方法：

```shell
conda create -n mindocr python=3.8
conda activate mindocr
cd mindocr
pip install -e .
```

4. 创建本地开发分支：

```shell
git checkout -b name-of-your-bugfix-or-feature
```

现在，您可以在本地进行更改。

5. 完成更改后，请检查您的更改是否通过了测试：

```shell
pre-commit run --show-diff-on-failure --color=always --all-files
pytest
```

如果所有静态测试都通过了，您将得到如下输出：

![提交成功前]（https://user-images.githubusercontent.com/74176172/221346245-ea868015-bb09-4e53-aa56-73b015e1e336.png）

否则，您需要根据输出修复警告：

![提交前失败]（https://user-images.githubusercontent.com/74176172/221346251-7d8f531f-9094-474b-97f0-fd5a55e6d3de.png）

要获取 pre-commit 和 pytest，只需将它们 pip 安装到您的 conda 环境中。

6. 提交您的更改并将您的分支推送到 GitHub：

```shell
git add .
git commit -m "你更改内容的详细描述"
git push origin name-of-your-bugfix-or-feature
```

7. 通过 GitHub 网站提交拉取请求。

## 拉取请求指南

在提交拉取请求之前，请检查它是否满足以下准则：

1. 拉取请求应包含tests。
2. 如果拉取请求添加了功能，则应更新文档。将你的新功能放入一个带有文档字符串的函数中，并将该功能添加到README.md中的列表中。
3. 拉取请求应该适用于 Python 3.7、3.8 和 3.9，以及 PyPy。检查 https://github.com/mindspore-lab/mindocr/actions
并确保所有受支持的 Python 版本的测试都通过。

## 小贴士

您可以安装 git hook 脚本，而不是手动使用 'pre-commit run -a' 进行 linting。

运行以下命令设置 Git Hook 脚本

```shell
pre-commit install
```

现在 'pre-commit' 将在 'git commit' 上自动运行！

## 发布

提醒维护人员如何部署。
请确保已提交所有更改。
然后运行：

```shell
bump2version patch # possible: major / minor / patch
git push
git push --tags
```

如果测试通过，GitHub Action 将部署到 PyPI。
99 changes: 50 additions & 49 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@
[![PRs](https://img.shields.io/badge/PRs-welcome-pink.svg)](https://github.com/mindspore-lab/mindocr/pulls)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

English | [中文](README_CN.md)
English | [中文](https://github.com/mindspore-lab/mindocr/blob/main/README_CN.md)

[📝Introduction](#introduction) |
[🔨Installation](#installation) |
Expand Down Expand Up @@ -210,6 +210,7 @@ For more illustration and usage, please refer to the model training section in [
### 3. Model Offline Inference - Quick Guideline

You can do MindSpore Lite inference in MindOCR using **MindOCR models** or **Third-party models** (PaddleOCR, MMOCR, etc.). Please refer to the following documents

- [Python/C++ Inference on Ascend 310](docs/en/inference/inference_tutorial.md)
- [MindOCR Models Offline Inference - Quick Start](docs/en/inference/inference_quickstart.md)
- [Third-party Models Offline Inference - Quick Start](docs/en/inference/inference_thirdparty_quickstart.md).
Expand All @@ -226,80 +227,80 @@ You can do MindSpore Lite inference in MindOCR using **MindOCR models** or **Thi
- [Distributed Training](docs/en/tutorials/distribute_train.md)
- [Advance: Gradient Accumulation, EMA, Resume Training, etc](docs/en/tutorials/advanced_train.md)
- Inference with MindSpore
- [Python Online Inference](tools/infer/text/README.md)
- [Python Online Inference](https://github.com/mindspore-lab/mindocr/blob/main/tools/infer/text/README.md)
- Inference with MindSpore Lite
- [Python/C++ Inference on Ascend 310](docs/en/inference/inference_tutorial.md)
- [MindOCR Models Offline Inference - Quick Start](docs/en/inference/inference_quickstart.md)
- [Third-party Models Offline Inference - Quick Start](docs/en/inference/inference_thirdparty_quickstart.md)
- [Model Conversion](docs/en/inference/convert_tutorial.md)
- Developer Guides
- [Customize Dataset](mindocr/data/README.md)
- [Customize Data Transformation](mindocr/data/transforms/README.md)
- [Customize a New Model](mindocr/models/README.md)
- [Customize Postprocessing Method](mindocr/postprocess/README.md)
- [Customize Dataset](https://github.com/mindspore-lab/mindocr/blob/main/mindocr/data/README.md)
- [Customize Data Transformation](https://github.com/mindspore-lab/mindocr/blob/main/mindocr/data/transforms/README.md)
- [Customize a New Model](https://github.com/mindspore-lab/mindocr/blob/main/mindocr/models/README.md)
- [Customize Postprocessing Method](https://github.com/mindspore-lab/mindocr/blob/main/mindocr/postprocess/README.md)

## Model List

<details open markdown>
<summary>Text Detection</summary>

- [x] [DBNet](configs/det/dbnet/README.md) (AAAI'2020)
- [x] [DBNet++](configs/det/dbnet/README.md) (TPAMI'2022)
- [x] [PSENet](configs/det/psenet/README.md) (CVPR'2019)
- [x] [EAST](configs/det/east/README.md)(CVPR'2017)
- [x] [FCENet](configs/det/fcenet/README.md) (CVPR'2021)
- [x] [DBNet](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/dbnet/README.md) (AAAI'2020)
- [x] [DBNet++](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/dbnet/README.md) (TPAMI'2022)
- [x] [PSENet](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/psenet/README.md) (CVPR'2019)
- [x] [EAST](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/east/README.md)(CVPR'2017)
- [x] [FCENet](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/fcenet/README.md) (CVPR'2021)

</details>

<details open markdown>
<summary>Text Recognition</summary>

- [x] [CRNN](configs/rec/crnn/README.md) (TPAMI'2016)
- [x] [CRNN-Seq2Seq/RARE](configs/rec/rare/README.md) (CVPR'2016)
- [x] [SVTR](configs/rec/svtr/README.md) (IJCAI'2022)
- [x] [MASTER](configs/rec/master/README.md) (PR'2019)
- [x] [VISIONLAN](configs/rec/visionlan/README.md) (ICCV'2021)
- [x] [RobustScanner](configs/rec/robustscanner/README.md) (ECCV'2020)
- [x] [ABINet](configs/rec/abinet/README.md) (CVPR'2021)
- [x] [CRNN](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/README.md) (TPAMI'2016)
- [x] [CRNN-Seq2Seq/RARE](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/README.md) (CVPR'2016)
- [x] [SVTR](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/README.md) (IJCAI'2022)
- [x] [MASTER](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/master/README.md) (PR'2019)
- [x] [VISIONLAN](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/visionlan/README.md) (ICCV'2021)
- [x] [RobustScanner](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/robustscanner/README.md) (ECCV'2020)
- [x] [ABINet](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/abinet/README.md) (CVPR'2021)

</details>

<details open markdown>
<summary>Layout Analysis</summary>

- [x] [YOLOv8](configs/layout/yolov8/README.md) ([Ultralytics Inc.](https://github.com/ultralytics/ultralytics))
- [x] [YOLOv8](https://github.com/mindspore-lab/mindocr/blob/main/configs/layout/yolov8/README.md) ([Ultralytics Inc.](https://github.com/ultralytics/ultralytics))

</details>

<details open markdown>
<summary>Key Information Extraction</summary>

- [x] [LayoutXLM](configs/kie/vi_layoutxlm/README_CN.md) (arXiv'2021)
- [x] [LayoutLMv3](configs/kie/layoutlmv3/README.md) (arXiv'2022)
- [x] [LayoutXLM](https://github.com/mindspore-lab/mindocr/blob/main/configs/kie/vi_layoutxlm/README.md) (arXiv'2021)
- [x] [LayoutLMv3](https://github.com/mindspore-lab/mindocr/blob/main/configs/kie/layoutlmv3/README.md) (arXiv'2022)

</details>

<details open markdown>
<summary>Table Recognition</summary>

- [x] [TableMaster](configs/table/README.md) (arXiv'2021)
- [x] [TableMaster](https://github.com/mindspore-lab/mindocr/blob/main/configs/table/README.md) (arXiv'2021)

</details>

<details open markdown>
<summary>OCR large model</summary>

- [x] [Vary](configs/llm/vary/README.md) (arXiv'2023)
- [x] [Vary](https://github.com/mindspore-lab/mindocr/blob/main/configs/llm/vary/README.md) (arXiv'2023)

</details>

For the detailed performance of the trained models, please refer to [configs](./configs).
For the detailed performance of the trained models, please refer to [https://github.com/mindspore-lab/mindocr/blob/main/configs](./configs).

For details of MindSpore Lite and ACL inference models support, please refer to [MindOCR Models Support List](docs/en/inference/inference_quickstart.md) and [Third-party Models Support List](docs/en/inference/inference_thirdparty_quickstart.md) (PaddleOCR, MMOCR, etc.).

## Dataset List

MindOCR provides a [dataset conversion tool](tools/dataset_converters) to OCR datasets with different formats and support customized dataset by users. We have validated the following public OCR datasets in model training/evaluation.
MindOCR provides a [dataset conversion tool](https://github.com/mindspore-lab/mindocr/blob/main/tools/dataset_converters) to OCR datasets with different formats and support customized dataset by users. We have validated the following public OCR datasets in model training/evaluation.

<details close markdown>
<summary>General OCR Datasets</summary>
Expand Down Expand Up @@ -363,67 +364,67 @@ Frequently asked questions about configuring environment and mindocr, please ref

- 2023/04/01
1. Add new trained models
- [LayoutLMv3](configs/kie/layoutlmv3/) for key information extraction
- [LayoutLMv3](https://github.com/mindspore-lab/mindocr/blob/main/configs/kie/layoutlmv3/) for key information extraction

- 2024/03/20
1. Add new trained models
- [Vary-toy](configs/llm/vary/vary_toy.yaml) for OCR large model, providing Qwen-1.8B LLM-based object detection and OCR abilities
- [Vary-toy](https://github.com/mindspore-lab/mindocr/blob/main/configs/llm/vary/vary_toy.yaml) for OCR large model, providing Qwen-1.8B LLM-based object detection and OCR abilities

- 2023/12/25
1. Add new trained models
- [TableMaster](configs/table/table_master.yaml) for table recognition
- [TableMaster](https://github.com/mindspore-lab/mindocr/blob/main/configs/table/table_master.yaml) for table recognition
2. Add more benchmark datasets and their results
- [PubTabNet](configs/table/README.md)
- [PubTabNet](https://github.com/mindspore-lab/mindocr/blob/main/configs/table/README.md)

- 2023/12/14
1. Add new trained models
- [LayoutXLM](configs/kie/layoutxlm) for key information extraction
- [VI-LayoutXLM](configs/kie/vi_layoutxlm) for key information extraction
- [PP-OCRv3 DBNet](configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml) for text detection and [PP-OCRv3 SVTR](configs/rec/svtr/svtr_ppocrv3_ch.yaml) for recognition, supporting online inferece and finetuning
- [LayoutXLM](https://github.com/mindspore-lab/mindocr/blob/main/configs/kie/layoutxlm) for key information extraction
- [VI-LayoutXLM](https://github.com/mindspore-lab/mindocr/blob/main/configs/kie/vi_layoutxlm) for key information extraction
- [PP-OCRv3 DBNet](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml) for text detection and [PP-OCRv3 SVTR](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr/svtr_ppocrv3_ch.yaml) for recognition, supporting online inferece and finetuning
2. Add more benchmark datasets and their results
- [XFUND](configs/kie/vi_layoutxlm/README_CN.md)
- [XFUND](https://github.com/mindspore-lab/mindocr/blob/main/configs/kie/vi_layoutxlm/README_CN.md)
3. Multiple specifications support for Ascend 910: DBNet ResNet-50, DBNet++ ResNet-50, CRNN VGG7, SVTR-Tiny, FCENet, ABINet
- 2023/11/28
1. Add offline inference support for PP-OCRv4
- [PP-OCRv4 DBNet](deploy/py_infer/src/configs/det/ppocr/ch_PP-OCRv4_det_cml.yaml) for text detection and [PP-OCRv4 CRNN](deploy/py_infer/src/configs/rec/ppocr/ch_PP-OCRv4_rec_distillation.yaml) for text recognition, supporting offline inferece
- [PP-OCRv4 DBNet](https://github.com/mindspore-lab/mindocr/blob/main/deploy/py_infer/src/configs/det/ppocr/ch_PP-OCRv4_det_cml.yaml) for text detection and [PP-OCRv4 CRNN](https://github.com/mindspore-lab/mindocr/blob/main/deploy/py_infer/src/configs/rec/ppocr/ch_PP-OCRv4_rec_distillation.yaml) for text recognition, supporting offline inferece
2. Fix bugs of third-party models offline inference
- 2023/11/17
1. Add new trained models
- [YOLOv8](configs/layout/yolov8) for layout analysis
- [YOLOv8](https://github.com/mindspore-lab/mindocr/blob/main/configs/layout/yolov8) for layout analysis
2. Add more benchmark datasets and their results
- [PublayNet](configs/layout/yolov8/README_CN.md)
- [PublayNet](https://github.com/mindspore-lab/mindocr/blob/main/configs/layout/yolov8/README_CN.md)
- 2023/07/06
1. Add new trained models
- [RobustScanner](configs/rec/robustscanner) for text recognition
- [RobustScanner](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/robustscanner) for text recognition
- 2023/07/05
1. Add new trained models
- [VISIONLAN](configs/rec/visionlan) for text recognition
- [VISIONLAN](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/visionlan) for text recognition
- 2023/06/29
1. Add new trained models
- [FCENet](configs/det/fcenet) for text detection
- [MASTER](configs/rec/master) for text recognition
- [FCENet](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/fcenet) for text detection
- [MASTER](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/master) for text recognition
- 2023/06/07
1. Add new trained models
- [PSENet](configs/det/psenet) for text detection
- [EAST](configs/det/east) for text detection
- [SVTR](configs/rec/svtr) for text recognition
- [PSENet](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/psenet) for text detection
- [EAST](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/east) for text detection
- [SVTR](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/svtr) for text recognition
2. Add more benchmark datasets and their results
- [totaltext](docs/en/datasets/totaltext.md)
- [mlt2017](docs/en/datasets/mlt2017.md)
- [chinese_text_recognition](docs/en/datasets/chinese_text_recognition.md)
3. Add resume training function, which can be used in case of unexpected interruption in training. Usage: add the `resume` parameter under the `model` field in the yaml config, e.g.,`resume: True`, load and resume training from {ckpt_save_dir}/train_resume.ckpt or `resume: /path/to/train_resume.ckpt`, load and resume training from the given path.
4. Improve postprocessing for detection: re-scale detected text polygons to original image space by default,
which can be enabled by add "shape_list" to the `eval.dataset.output_columns` list.
5. Refactor online inference to support more models, see [README.md](tools/infer/text/README.md) for details.
5. Refactor online inference to support more models, see [README.md](https://github.com/mindspore-lab/mindocr/blob/main/tools/infer/text/README.md) for details.

- 2023/05/15
1. Add new trained models
- [DBNet++](configs/det/dbnet) for text detection
- [CRNN-Seq2Seq](configs/rec/rare) for text recognition
- [DBNet++](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/dbnet) for text detection
- [CRNN-Seq2Seq](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare) for text recognition
- DBNet pretrained on SynthText is now available: [checkpoint url](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_synthtext-40655acb.ckpt)
2. Add more benchmark datasets and their results
- [SynthText](docs/en/datasets/synthtext.md), [MSRA-TD500](docs/en/datasets/td500.md), [CTW1500](docs/en/datasets/ctw1500.md)
- More benchmark results for DBNet are reported [here](configs/det/dbnet/README.md).
- More benchmark results for DBNet are reported [here](https://github.com/mindspore-lab/mindocr/blob/main/configs/det/dbnet/README.md).
3. Add checkpoint manager for saving top-k checkpoints and improve log.
4. Python inference code refactored.
5. Bug fix: use Meter to average loss for large datasets, disable `pred_cast_fp32` for ctcloss in AMP training, fix error when invalid polygons exist.
Expand Down Expand Up @@ -463,11 +464,11 @@ which can be enabled by add "shape_list" to the `eval.dataset.output_columns` li

We appreciate all kinds of contributions including issues and PRs to make MindOCR better.

Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline. Please follow the [Model Template and Guideline](mindocr/models/README.md) for contributing a model that fits the overall interface :)
Please refer to [CONTRIBUTING.md](https://github.com/mindspore-lab/mindocr/blob/main/CONTRIBUTING.md) for the contributing guideline. Please follow the [Model Template and Guideline](https://github.com/mindspore-lab/mindocr/blob/main/mindocr/models/README.md) for contributing a model that fits the overall interface :)

### License

This project follows the [Apache License 2.0](LICENSE) open-source license.
This project follows the [Apache License 2.0](https://github.com/mindspore-lab/mindocr/blob/main/LICENSE) open-source license.

### Citation

Expand Down
Loading
Loading