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Merge pull request #707 from myhloli/master 

refactor(docs): update model download instructions and configuration process
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README.md
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......@@ -198,34 +198,20 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com
Refer to [How to Download Model Files](docs/how_to_download_models_en.md) for detailed instructions.
> ❗️After downloading the models, please make sure to verify the completeness of the model files.
>
> Check if the model file sizes match the description on the webpage. If possible, use sha256 to verify the integrity of the files.
#### 3. Modify the Configuration File for Additional Configuration
#### 3. Copy and configure the template file
After completing the [2. Download model weight files](#2-download-model-weight-files) step, the script will automatically generate a `magic-pdf.json` file in the user directory and configure the default model path.
You can find the `magic-pdf.json` file in your 【user directory】.
You can find the `magic-pdf.template.json` template configuration file in the root directory of the repository.
> The user directory for Windows is "C:\\Users\\username", for Linux it is "/home/username", and for macOS it is "/Users/username".
> ❗️Make sure to execute the following command to copy the configuration file to your **user directory**; otherwise, the program will not run.
>
> The user directory for Windows is `C:\Users\YourUsername`, for Linux it is `/home/YourUsername`, and for macOS it is `/Users/YourUsername`.
You can modify certain configurations in this file to enable or disable features, such as table recognition:
```bash
cp magic-pdf.template.json ~/magic-pdf.json
```
Find the `magic-pdf.json` file in your user directory and configure the "models-dir" path to point to the directory where the model weight files were downloaded in [Step 2](#2-download-model-weight-files).
> ❗️Make sure to correctly configure the **absolute path** to the model weight files directory, otherwise the program will not run because it can't find the model files.
>
> On Windows, this path should include the drive letter and all backslashes (`\`) in the path should be replaced with forward slashes (`/`) to avoid syntax errors in the JSON file due to escape sequences.
>
> For example: If the models are stored in the "models" directory at the root of the D drive, the "model-dir" value should be `D:/models`.
> If the following items are not present in the JSON, please manually add the required items and remove the comment content (standard JSON does not support comments).
```json
{
// other config
"models-dir": "D:/models",
"table-config": {
"model": "TableMaster", // Another option of this value is 'struct_eqtable'
"is_table_recog_enable": false, // Table recognition is disabled by default, modify this value to enable it
......
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<div id="top">
<p align="center">
<img src="docs/images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
</p>
</div>
<div align="center">
[![stars](https://img.shields.io/github/stars/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
[![forks](https://img.shields.io/github/forks/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
[![open issues](https://img.shields.io/github/issues-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)
[![issue resolution](https://img.shields.io/github/issues-closed-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)
[![PyPI version](https://badge.fury.io/py/magic-pdf.svg)](https://badge.fury.io/py/magic-pdf)
[![Downloads](https://static.pepy.tech/badge/magic-pdf)](https://pepy.tech/project/magic-pdf)
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<a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 200px; height: 55px;"/></a>
[English](README.md) | [简体中文](README_zh-CN.md) | [日本語](README_ja-JP.md)
</div>
<div align="center">
<p align="center">
<a href="https://github.com/opendatalab/MinerU">MinerU: An end-to-end PDF parsing tool based on PDF-Extract-Kit, supporting conversion from PDF to Markdown.</a>🚀🚀🚀<br>
<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: A Comprehensive Toolkit for High-Quality PDF Content Extraction</a>🔥🔥🔥
</p>
<p align="center">
👋 join us on <a href="https://discord.gg/gPxmVeGC" target="_blank">Discord</a> and <a href="https://cdn.vansin.top/internlm/mineru.jpg" target="_blank">WeChat</a>
</p>
</div>
# MinerU
## Introduction
MinerU is a one-stop, open-source, high-quality data extraction tool, includes the following primary features:
- [Magic-PDF](#Magic-PDF) PDF Document Extraction
- [Magic-Doc](#Magic-Doc) Webpage & E-book Extraction
# Magic-PDF
## Introduction
Magic-PDF is a tool designed to convert PDF documents into Markdown format, capable of processing files stored locally or on object storage supporting S3 protocol.
Key features include:
- Support for multiple front-end model inputs
- Removal of headers, footers, footnotes, and page numbers
- Human-readable layout formatting
- Retains the original document's structure and formatting, including headings, paragraphs, lists, and more
- Extraction and display of images and tables within markdown
- Conversion of equations into LaTeX format
- Automatic detection and conversion of garbled PDFs
- Compatibility with CPU and GPU environments
- Available for Windows, Linux and macOS platforms
https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
## Project Panorama
![Project Panorama](docs/images/project_panorama_en.png)
## Flowchart
![Flowchart](docs/images/flowchart_en.png)
### Dependency repositorys
- [PDF-Extract-Kit : A Comprehensive Toolkit for High-Quality PDF Content Extraction](https://github.com/opendatalab/PDF-Extract-Kit) 🚀🚀🚀
## Getting Started
### Requirements
- Python >= 3.9
Using a virtual environment is recommended to avoid potential dependency conflicts; both venv and conda are suitable.
For example:
```bash
conda create -n MinerU python=3.10
conda activate MinerU
```
### Installation and Configuration
#### 1. Install Magic-PDF
**1.Install dependencies**
The full-feature package depends on detectron2, which requires a compilation installation.
If you need to compile it yourself, please refer to https://github.com/facebookresearch/detectron2/issues/5114
Alternatively, you can directly use our precompiled whl package (limited to Python 3.10):
```bash
pip install detectron2 --extra-index-url https://wheels.myhloli.com
```
**2.Install the full-feature package with pip**
>Note: The pip-installed package supports CPU-only and is ideal for quick tests.
>
>For CUDA/MPS acceleration in production, see [Acceleration Using CUDA or MPS](#4-Acceleration-Using-CUDA-or-MPS).
```bash
pip install magic-pdf[full]==0.6.2b1
```
> ❗️❗️❗️
> We have pre-released the 0.6.2 beta version, addressing numerous issues mentioned in our logs. However, this build has not undergone full QA testing and does not represent the final release quality. Should you encounter any problems, please promptly report them to us via issues or revert to using version 0.6.1.
> ```bash
> pip install magic-pdf[full-cpu]==0.6.1
> ```
#### 2. Downloading model weights files
For detailed references, please see below [how_to_download_models](docs/how_to_download_models_en.md)
After downloading the model weights, move the 'models' directory to a directory on a larger disk space, preferably an SSD.
#### 3. Copy the Configuration File and Make Configurations
You can get the [magic-pdf.template.json](magic-pdf.template.json) file in the repository root directory.
```bash
cp magic-pdf.template.json ~/magic-pdf.json
```
In magic-pdf.json, configure "models-dir" to point to the directory where the model weights files are located.
```json
{
"models-dir": "/tmp/models"
}
```
#### 4. Acceleration Using CUDA or MPS
If you have an available Nvidia GPU or are using a Mac with Apple Silicon, you can leverage acceleration with CUDA or MPS respectively.
##### CUDA
You need to install the corresponding PyTorch version according to your CUDA version.
This example installs the CUDA 11.8 version.More information https://pytorch.org/get-started/locally/
```bash
pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
```
> ❗ ️Make sure to specify version
> ```bash
> torch==2.3.1 torchvision==0.18.1
> ```
> in the command, as these are the highest versions we support. Failing to specify the versions may result in automatically installing higher versions which can cause the program to fail.
Also, you need to modify the value of "device-mode" in the configuration file magic-pdf.json.
```json
{
"device-mode":"cuda"
}
```
##### MPS
For macOS users with M-series chip devices, you can use MPS for inference acceleration.
You also need to modify the value of "device-mode" in the configuration file magic-pdf.json.
```json
{
"device-mode":"mps"
}
```
### Usage
#### 1.Usage via Command Line
###### simple
```bash
magic-pdf pdf-command --pdf "pdf_path" --inside_model true
```
After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".
You can find the corresponding xxx_model.json file in the markdown directory.
If you intend to do secondary development on the post-processing pipeline, you can use the command:
```bash
magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
```
In this way, you won't need to re-run the model data, making debugging more convenient.
###### more
```bash
magic-pdf --help
```
#### 2. Usage via Api
###### Local
```python
image_writer = DiskReaderWriter(local_image_dir)
image_dir = str(os.path.basename(local_image_dir))
jso_useful_key = {"_pdf_type": "", "model_list": []}
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
pipe.pipe_classify()
pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
```
###### Object Storage
```python
s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
image_dir = "s3://img_bucket/"
s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
jso_useful_key = {"_pdf_type": "", "model_list": []}
pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
pipe.pipe_classify()
pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
```
Demo can be referred to [demo.py](demo/demo.py)
# Magic-Doc
## Introduction
Magic-Doc is a tool designed to convert web pages or multi-format e-books into markdown format.
Key Features Include:
- Web Page Extraction
- Cross-modal precise parsing of text, images, tables, and formula information.
- E-Book Document Extraction
- Supports various document formats including epub, mobi, with full adaptation for text and images.
- Language Type Identification
- Accurate recognition of 176 languages.
https://github.com/opendatalab/MinerU/assets/11393164/a5a650e9-f4c0-463e-acc3-960967f1a1ca
https://github.com/opendatalab/MinerU/assets/11393164/0f4a6fe9-6cca-4113-9fdc-a537749d764d
https://github.com/opendatalab/MinerU/assets/11393164/20438a02-ce6c-4af8-9dde-d722a4e825b2
## Project Repository
- [Magic-Doc](https://github.com/InternLM/magic-doc)
Outstanding Webpage and E-book Extraction Tool
# All Thanks To Our Contributors
<a href="https://github.com/opendatalab/MinerU/graphs/contributors">
<img src="https://contrib.rocks/image?repo=opendatalab/MinerU" />
</a>
# License Information
[LICENSE.md](LICENSE.md)
The project currently leverages PyMuPDF to deliver advanced functionalities; however, its adherence to the AGPL license may impose limitations on certain use cases. In upcoming iterations, we intend to explore and transition to a more permissively licensed PDF processing library to enhance user-friendliness and flexibility.
# Acknowledgments
- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
- [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)
# Citation
```bibtex
@article{he2024opendatalab,
title={Opendatalab: Empowering general artificial intelligence with open datasets},
author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
journal={arXiv preprint arXiv:2407.13773},
year={2024}
}
@misc{2024mineru,
title={MinerU: A One-stop, Open-source, High-quality Data Extraction Tool},
author={MinerU Contributors},
howpublished = {\url{https://github.com/opendatalab/MinerU}},
year={2024}
}
```
# Star History
<a>
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
</picture>
</a>
# Links
- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)
- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)
README_zh-CN.md
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......@@ -200,35 +200,19 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com -i h
详细参考 [如何下载模型文件](docs/how_to_download_models_zh_cn.md)
> ❗️模型下载后请务必检查模型文件是否下载完整
>
> 请检查目录下的模型文件大小与网页上描述是否一致，如果可以的话，最好通过sha256校验模型是否下载完整
#### 3. 修改配置文件以进行额外配置
#### 3. 拷贝配置文件并进行配置
在仓库根目录可以获得 [magic-pdf.template.json](magic-pdf.template.json) 配置模版文件
> ❗️务必执行以下命令将配置文件拷贝到【用户目录】下，否则程序将无法运行
>
完成[2. 下载模型权重文件](#2-下载模型权重文件)步骤后，脚本会自动生成用户目录下的magic-pdf.json文件，并自动配置默认模型路径。
您可在【用户目录】下找到magic-pdf.json文件。
> windows的用户目录为 "C:\\Users\\用户名", linux用户目录为 "/home/用户名", macOS用户目录为 "/Users/用户名"
```bash
cp magic-pdf.template.json ~/magic-pdf.json
```
在用户目录中找到magic-pdf.json文件并配置"models-dir"为[2. 下载模型权重文件](#2-下载模型权重文件)中下载的模型权重文件所在目录
> ❗️务必正确配置模型权重文件所在目录的【绝对路径】，否则会因为找不到模型文件而导致程序无法运行
>
> windows系统中此路径应包含盘符，且需把路径中所有的`"\"`替换为`"/"`,否则会因为转义原因导致json文件语法错误。
>
您可修改该文件中的部分配置实现功能的开关，如表格识别功能：
> 例如：模型放在D盘根目录的models目录，则model-dir的值应为"D:/models"
>如json内没有如下项目，请手动添加需要的项目，并删除注释内容（标准json不支持注释）
```json
{
// other config
"models-dir": "D:/models",
"table-config": {
"model": "TableMaster", // 使用structEqTable请修改为'struct_eqtable'
"is_table_recog_enable": false, // 表格识别功能默认是关闭的，如果需要修改此处的值
......
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<div align="center" xmlns="http://www.w3.org/1999/html">
<!-- logo -->
<p align="center">
<img src="docs/images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
</p>
<!-- icon -->
[![stars](https://img.shields.io/github/stars/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
[![forks](https://img.shields.io/github/forks/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
[![open issues](https://img.shields.io/github/issues-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)
[![issue resolution](https://img.shields.io/github/issues-closed-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)
[![PyPI version](https://badge.fury.io/py/magic-pdf.svg)](https://badge.fury.io/py/magic-pdf)
[![Downloads](https://static.pepy.tech/badge/magic-pdf)](https://pepy.tech/project/magic-pdf)
[![Downloads](https://static.pepy.tech/badge/magic-pdf/month)](https://pepy.tech/project/magic-pdf)
<a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
<!-- language -->
[English](README.md) | [简体中文](README_zh-CN.md)
<!-- hot link -->
<p align="center">
<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: 高质量PDF解析工具箱</a>🔥🔥🔥
</p>
<!-- join us -->
<p align="center">
👋 join us on <a href="https://discord.gg/Tdedn9GTXq" target="_blank">Discord</a> and <a href="https://cdn.vansin.top/internlm/mineru.jpg" target="_blank">WeChat</a>
</p>
</div>
# 更新记录
- 2024/08/09 0.7.0b1发布，简化安装步骤提升易用性，加入表格识别功能
- 2024/08/01 0.6.2b1发布，优化了依赖冲突问题和安装文档
- 2024/07/05 首次开源
<!-- TABLE OF CONTENT -->
<details open="open">
<summary><h2 style="display: inline-block">文档目录</h2></summary>
<ol>
<li>
<a href="#mineru">MinerU</a>
<ul>
<li><a href="#项目简介">项目简介</a></li>
<li><a href="#主要功能">主要功能</a></li>
<li><a href="#快速开始">快速开始</a>
<ul>
<li><a href="#在线体验">在线体验</a></li>
<li><a href="#使用CPU快速体验">使用CPU快速体验</a></li>
<li><a href="#使用GPU">使用GPU</a></li>
</ul>
</li>
<li><a href="#使用">使用方式</a>
<ul>
<li><a href="#命令行">命令行</a></li>
<li><a href="#api">API</a></li>
<li><a href="#二次开发">二次开发</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#todo">TODO</a></li>
<li><a href="#known-issues">Known Issues</a></li>
<li><a href="#faq">FAQ</a></li>
<li><a href="#all-thanks-to-our-contributors">Contributors</a></li>
<li><a href="#license-information">License Information</a></li>
<li><a href="#acknowledgments">Acknowledgements</a></li>
<li><a href="#citation">Citation</a></li>
<li><a href="#star-history">Star History</a></li>
<li><a href="#magic-doc">magic-doc快速提取PPT/DOC/PDF</a></li>
<li><a href="#magic-html">magic-html提取混合网页内容</a></li>
<li><a href="#links">Links</a></li>
</ol>
</details>
# MinerU
## 项目简介
MinerU是一款将PDF转化为机器可读格式的工具（如markdown、json），可以很方便地抽取为任意格式。
MinerU诞生于[书生-浦语](https://github.com/InternLM/InternLM)的预训练过程中，我们将会集中精力解决科技文献中的符号转化问题，希望在大模型时代为科技发展做出贡献。
相比国内外知名商用产品MinerU还很年轻，如果遇到问题或者结果不及预期请到[issue](https://github.com/opendatalab/MinerU/issues)提交问题，同时**附上相关PDF**。
https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
## 主要功能
- 删除页眉、页脚、脚注、页码等元素，保持语义连贯
- 对多栏输出符合人类阅读顺序的文本
- 保留原文档的结构，包括标题、段落、列表等
- 提取图像、图片标题、表格、表格标题
- 自动识别文档中的公式并将公式转换成latex
- 自动识别文档中的表格并将表格转换成latex
- 乱码PDF自动检测并启用OCR
- 支持CPU和GPU环境
- 支持windows/linux/mac平台
## 快速开始
如果遇到任何安装问题，请先查询 <a href="#faq">FAQ</a> </br>
如果遇到解析效果不及预期，参考 <a href="#known-issues">Known Issues</a></br>
有3种不同方式可以体验MinerU的效果：
- [在线体验(无需任何安装)](#在线体验)
- [使用CPU快速体验（Windows，Linux，Mac）](#使用cpu快速体验)
- [Linux/Windows + CUDA](#使用gpu)
**⚠️安装前必看——软硬件环境支持说明**
为了确保项目的稳定性和可靠性，我们在开发过程中仅对特定的软硬件环境进行优化和测试。这样当用户在推荐的系统配置上部署和运行项目时，能够获得最佳的性能表现和最少的兼容性问题。
通过集中资源和精力于主线环境，我们团队能够更高效地解决潜在的BUG，及时开发新功能。
在非主线环境中，由于硬件、软件配置的多样性，以及第三方依赖项的兼容性问题，我们无法100%保证项目的完全可用性。因此，对于希望在非推荐环境中使用本项目的用户，我们建议先仔细阅读文档以及FAQ，大多数问题已经在FAQ中有对应的解决方案，除此之外我们鼓励社区反馈问题，以便我们能够逐步扩大支持范围。
<table>
<tr>
<td colspan="3" rowspan="2">操作系统</td>
</tr>
<tr>
<td>Ubuntu 22.04 LTS</td>
<td>Windows 10 / 11</td>
<td>macOS 11+</td>
</tr>
<tr>
<td colspan="3">CPU</td>
<td>x86_64</td>
<td>x86_64</td>
<td>x86_64 / arm64</td>
</tr>
<tr>
<td colspan="3">内存</td>
<td colspan="3">大于等于16GB，推荐32G以上</td>
</tr>
<tr>
<td colspan="3">python版本</td>
<td colspan="3">3.10</td>
</tr>
<tr>
<td colspan="3">Nvidia Driver 版本</td>
<td>latest(专有驱动)</td>
<td>latest</td>
<td>None</td>
</tr>
<tr>
<td colspan="3">CUDA环境</td>
<td>自动安装[12.1(pytorch)+11.8(paddle)]</td>
<td>11.8(手动安装)+cuDNN v8.7.0(手动安装)</td>
<td>None</td>
</tr>
<tr>
<td rowspan="2">GPU硬件支持列表</td>
<td colspan="2">最低要求 8G+显存</td>
<td colspan="2">3060ti/3070/3080/3080ti/4060/4070/4070ti<br>
8G显存仅可开启lavout和公式识别加速</td>
<td rowspan="2">None</td>
</tr>
<tr>
<td colspan="2">推荐配置 16G+显存</td>
<td colspan="2">3090/3090ti/4070tisuper/4080/4090<br>
16G及以上可以同时开启layout，公式识别和ocr加速</td>
</tr>
</table>
### 在线体验
[在线体验点击这里](https://opendatalab.com/OpenSourceTools/Extractor/PDF)
### 使用CPU快速体验
#### 1. 安装magic-pdf
最新版本国内镜像源同步可能会有延迟，请耐心等待
```bash
conda create -n MinerU python=3.10
conda activate MinerU
pip install magic-pdf[full]==0.7.0b1 --extra-index-url https://wheels.myhloli.com -i https://pypi.tuna.tsinghua.edu.cn/simple
```
#### 2. 下载模型权重文件
详细参考 [如何下载模型文件](docs/how_to_download_models_zh_cn.md)
> ❗️模型下载后请务必检查模型文件是否下载完整
>
> 请检查目录下的模型文件大小与网页上描述是否一致，如果可以的话，最好通过sha256校验模型是否下载完整
#### 3. 拷贝配置文件并进行配置
在仓库根目录可以获得 [magic-pdf.template.json](magic-pdf.template.json) 配置模版文件
> ❗️务必执行以下命令将配置文件拷贝到【用户目录】下，否则程序将无法运行
>
> windows的用户目录为 "C:\\Users\\用户名", linux用户目录为 "/home/用户名", macOS用户目录为 "/Users/用户名"
```bash
cp magic-pdf.template.json ~/magic-pdf.json
```
在用户目录中找到magic-pdf.json文件并配置"models-dir"为[2. 下载模型权重文件](#2-下载模型权重文件)中下载的模型权重文件所在目录
> ❗️务必正确配置模型权重文件所在目录的【绝对路径】，否则会因为找不到模型文件而导致程序无法运行
>
> windows系统中此路径应包含盘符，且需把路径中所有的`"\"`替换为`"/"`,否则会因为转义原因导致json文件语法错误。
>
> 例如：模型放在D盘根目录的models目录，则model-dir的值应为"D:/models"
```json
{
// other config
"models-dir": "D:/models",
"table-config": {
"is_table_recog_enable": false, // 表格识别功能默认是关闭的，如果需要修改此处的值
"max_time": 400
}
}
```
### 使用GPU
如果您的设备支持CUDA，且满足主线环境中的显卡要求，则可以使用GPU加速，请根据自己的系统选择适合的教程：
- [Ubuntu22.04LTS + GPU](docs/README_Ubuntu_CUDA_Acceleration_zh_CN.md)
- [Windows10/11 + GPU](docs/README_Windows_CUDA_Acceleration_zh_CN.md)
- 使用Docker快速部署
> Docker 需设备gpu显存大于等于16GB，默认开启所有加速功能
```bash
wget https://github.com/opendatalab/MinerU/raw/master/Dockerfile
docker build -t mineru:0.7.0b1 .
docker run --rm -it --gpus=all mineru:0.7.0b1 /bin/bash
magic-pdf --help
```
## 使用
### 命令行
```bash
magic-pdf --help
Usage: magic-pdf [OPTIONS]
Options:
-v, --version display the version and exit
-p, --path PATH local pdf filepath or directory [required]
-o, --output-dir TEXT output local directory
-m, --method [ocr|txt|auto] the method for parsing pdf.
ocr: using ocr technique to extract information from pdf,
txt: suitable for the text-based pdf only and outperform ocr,
auto: automatically choose the best method for parsing pdf
from ocr and txt.
without method specified, auto will be used by default.
--help Show this message and exit.
## show version
magic-pdf -v
## command line example
magic-pdf -p {some_pdf} -o {some_output_dir} -m auto
```
其中 `{some_pdf}` 可以是单个pdf文件，也可以是一个包含多个pdf文件的目录。
运行完命令后输出的结果会保存在`{some_output_dir}`目录下, 输出的文件列表如下
```text
├── some_pdf.md # markdown 文件
├── images # 存放图片目录
├── some_pdf_layout.pdf # layout 绘图
├── some_pdf_middle.json # minerU 中间处理结果
├── some_pdf_model.json # 模型推理结果
├── some_pdf_origin.pdf # 原 pdf 文件
└── some_pdf_spans.pdf # 最小粒度的bbox位置信息绘图
```
更多有关输出文件的信息，请参考[输出文件说明](docs/output_file_zh_cn.md)
### API
处理本地磁盘上的文件
```python
image_writer = DiskReaderWriter(local_image_dir)
image_dir = str(os.path.basename(local_image_dir))
jso_useful_key = {"_pdf_type": "", "model_list": []}
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
pipe.pipe_classify()
pipe.pipe_analyze()
pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
```
处理对象存储上的文件
```python
s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
image_dir = "s3://img_bucket/"
s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
jso_useful_key = {"_pdf_type": "", "model_list": []}
pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
pipe.pipe_classify()
pipe.pipe_analyze()
pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
```
详细实现可参考
- [demo.py 最简单的处理方式](demo/demo.py)
- [magic_pdf_parse_main.py 能够更清晰看到处理流程](demo/magic_pdf_parse_main.py)
### 二次开发
TODO
# TODO
- [ ] 基于语义的阅读顺序
- [ ] 正文中列表识别
- [ ] 正文中代码块识别
- [ ] 目录识别
- [x] 表格识别
- [ ] [化学式识别](docs/chemical_knowledge_introduction/introduction.pdf)
- [ ] 几何图形识别
# Known Issues
- 阅读顺序基于规则的分割，在一些情况下会乱序
- 不支持竖排文字
- 列表、代码块、目录在layout模型里还没有支持
- 漫画书、艺术图册、小学教材、习题尚不能很好解析
- 在一些公式密集的PDF上强制启用OCR效果会更好
- 如果您要处理包含大量公式的pdf,强烈建议开启OCR功能。使用pymuPDF提取文字的时候会出现文本行互相重叠的情况导致公式插入位置不准确。
- **表格识别**目前处于测试阶段，识别速度较慢，识别准确度有待提升。以下是我们在Ubuntu 22.04 LTS + Intel(R) Xeon(R) Platinum 8352V CPU @ 2.10GHz + NVIDIA GeForce RTX 4090环境下的一些性能测试结果，可供参考。
| 表格大小 | 解析耗时 |
| ------------ | -------- |
| 6\*5 55kb | 37s |
| 16\*12 284kb | 3m18s |
| 44\*7 559kb | 4m12s |
# FAQ
[常见问题](docs/FAQ_zh_cn.md)
<<<<<<< HEAD
=======
[FAQ](docs/FAQ_en_us.md)
>>>>>>> 7f0fe20004af7416db886f4b75c116bcc1c986b4
[FAQ](docs/FAQ_en_us.md)
# All Thanks To Our Contributors
<a href="https://github.com/opendatalab/MinerU/graphs/contributors">
<img src="https://contrib.rocks/image?repo=opendatalab/MinerU" />
</a>
# License Information
[LICENSE.md](LICENSE.md)
本项目目前采用PyMuPDF以实现高级功能，但因其遵循AGPL协议，可能对某些使用场景构成限制。未来版本迭代中，我们计划探索并替换为许可条款更为宽松的PDF处理库，以提升用户友好度及灵活性。
# Acknowledgments
- [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit)
- [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy)
- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
- [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)
# Citation
```bibtex
@article{he2024opendatalab,
title={Opendatalab: Empowering general artificial intelligence with open datasets},
author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
journal={arXiv preprint arXiv:2407.13773},
year={2024}
}
@misc{2024mineru,
title={MinerU: A One-stop, Open-source, High-quality Data Extraction Tool},
author={MinerU Contributors},
howpublished = {\url{https://github.com/opendatalab/MinerU}},
year={2024}
}
```
# Star History
<a>
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
</picture>
</a>
# Magic-doc
[Magic-Doc](https://github.com/InternLM/magic-doc) Fast speed ppt/pptx/doc/docx/pdf extraction tool
# Magic-html
[Magic-HTML](https://github.com/opendatalab/magic-html) Mixed web page extraction tool
# Links
- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)
- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)
docs/README_Ubuntu_CUDA_Acceleration_en_US.md
View file @ 62aa1cbd
......@@ -58,28 +58,12 @@
### 6. Download Models
Refer to detailed instructions on [how to download model files](how_to_download_models_en.md).
After downloading, move the `models` directory to an SSD with more space.
❗ After downloading the models, ensure they are complete:
- Check that the file sizes match the description on the website.
- If possible, verify the integrity using SHA256.
## 7. Understand the Location of the Configuration File
### 7. Configuration Before First Run
Obtain the configuration template file `magic-pdf.template.json` from the root directory of the repository.
❗ Execute the following command to copy the configuration file to your home directory, otherwise the program will not run:
```sh
wget https://github.com/opendatalab/MinerU/raw/master/magic-pdf.template.json
cp magic-pdf.template.json ~/magic-pdf.json
```
Find the `magic-pdf.json` file in your home directory and configure `"models-dir"` to be the directory where the model weights from Step 6 were downloaded.
❗ Correctly specify the absolute path of the directory containing the model weights; otherwise, the program will fail due to missing model files.
```json
{
"models-dir": "/tmp/models"
}
```
After completing the [6. Download Models](#6-download-models) step, the script will automatically generate a `magic-pdf.json` file in the user directory and configure the default model path.
You can find the `magic-pdf.json` file in your user directory.
> The user directory for Linux is "/home/username".
### 8. First Run
Download a sample file from the repository and test it.
......
docs/README_Ubuntu_CUDA_Acceleration_zh_CN.md
View file @ 62aa1cbd
......@@ -54,29 +54,11 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com -i h
## 6. 下载模型
详细参考 [如何下载模型文件](how_to_download_models_zh_cn.md)
下载后请将models目录移动到空间较大的ssd磁盘目录
> ❗️模型下载后请务必检查模型文件是否下载完整
>
> 请检查目录下的模型文件大小与网页上描述是否一致，如果可以的话，最好通过sha256校验模型是否下载完整
>
## 7. 第一次运行前的配置
在仓库根目录可以获得 [magic-pdf.template.json](../magic-pdf.template.json) 配置模版文件
> ❗️务必执行以下命令将配置文件拷贝到【用户目录】下，否则程序将无法运行
>
> linux用户目录为 "/home/用户名"
```bash
wget https://gitee.com/myhloli/MinerU/raw/master/magic-pdf.template.json
cp magic-pdf.template.json ~/magic-pdf.json
```
在用户目录中找到magic-pdf.json文件并配置"models-dir"为[6. 下载模型](#6-下载模型)中下载的模型权重文件所在目录
> ❗️务必正确配置模型权重文件所在目录的【绝对路径】，否则会因为找不到模型文件而导致程序无法运行
>
```json
{
"models-dir": "/tmp/models"
}
```
## 7. 了解配置文件存放的位置
完成[6.下载模型](#6-下载模型)步骤后，脚本会自动生成用户目录下的magic-pdf.json文件，并自动配置默认模型路径。
您可在【用户目录】下找到magic-pdf.json文件。
> linux用户目录为 "/home/用户名"
## 8. 第一次运行
从仓库中下载样本文件，并测试
......
docs/README_Windows_CUDA_Acceleration_en_US.md
View file @ 62aa1cbd
......@@ -29,37 +29,12 @@ Download link: https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Windows-x86
### 5. Download Models
Refer to detailed instructions on [how to download model files](how_to_download_models_en.md).
After downloading, move the `models` directory to an SSD with more space.
>❗ After downloading the models, ensure they are complete:
>- Check that the file sizes match the description on the website.
>- If possible, verify the integrity using SHA256.
### 6. Understand the Location of the Configuration File
### 6. Configuration Before the First Run
Obtain the configuration template file `magic-pdf.template.json` from the repository root directory.
>❗️Execute the following command to copy the configuration file to your user directory, or the program will not run.
>
> In Windows, user directory is "C:\Users\username"
```powershell
(New-Object System.Net.WebClient).DownloadFile('https://github.com/opendatalab/MinerU/raw/master/magic-pdf.template.json', 'magic-pdf.template.json')
cp magic-pdf.template.json ~/magic-pdf.json
```
Find the `magic-pdf.json` file in your user directory and configure `"models-dir"` to point to the directory where the model weights from step 5 were downloaded.
> ❗️Ensure the absolute path of the model weights directory is correctly configured, or the program will fail to run due to not finding the model files.
>
> In Windows, this path should include the drive letter and replace all `"\"` to `"/"`.
>
> Example: If the models are placed in the root directory of drive D, the value for `model-dir` should be `"D:/models"`.
```json
{
"models-dir": "/tmp/models"
}
```
After completing the [5. Download Models](#5-download-models) step, the script will automatically generate a `magic-pdf.json` file in the user directory and configure the default model path.
You can find the `magic-pdf.json` file in your 【user directory】 .
> The user directory for Windows is "C:/Users/username".
### 7. First Run
Download a sample file from the repository and test it.
......
docs/README_Windows_CUDA_Acceleration_zh_CN.md
View file @ 62aa1cbd
......@@ -31,32 +31,11 @@ pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com -i h
## 5. 下载模型
详细参考 [如何下载模型文件](how_to_download_models_zh_cn.md)
下载后请将models目录移动到空间较大的ssd磁盘目录
> ❗️模型下载后请务必检查模型文件是否下载完整
>
> 请检查目录下的模型文件大小与网页上描述是否一致，如果可以的话，最好通过sha256校验模型是否下载完整
## 6. 第一次运行前的配置
在仓库根目录可以获得 [magic-pdf.template.json](../magic-pdf.template.json) 配置模版文件
> ❗️务必执行以下命令将配置文件拷贝到【用户目录】下，否则程序将无法运行
>
> windows用户目录为 "C:\Users\用户名"
```powershell
(New-Object System.Net.WebClient).DownloadFile('https://gitee.com/myhloli/MinerU/raw/master/magic-pdf.template.json', 'magic-pdf.template.json')
cp magic-pdf.template.json ~/magic-pdf.json
```
在用户目录中找到magic-pdf.json文件并配置"models-dir"为[5. 下载模型](#5-下载模型)中下载的模型权重文件所在目录
> ❗️务必正确配置模型权重文件所在目录的【绝对路径】，否则会因为找不到模型文件而导致程序无法运行
>
> windows系统中此路径应包含盘符，且需把路径中所有的`"\"`替换为`"/"`,否则会因为转义原因导致json文件语法错误。
>
> 例如：模型放在D盘根目录的models目录，则model-dir的值应为"D:/models"
```json
{
"models-dir": "/tmp/models"
}
```
## 6. 了解配置文件存放的位置
完成[5.下载模型](#5-下载模型)步骤后，脚本会自动生成用户目录下的magic-pdf.json文件，并自动配置默认模型路径。
您可在【用户目录】下找到magic-pdf.json文件。
> windows用户目录为 "C:/Users/用户名"
## 7. 第一次运行
从仓库中下载样本文件，并测试
......
docs/download_models.py
View file @ 62aa1cbd
# use modelscope sdk download models
import os
import requests
import json
from modelscope import snapshot_download
model_dir = snapshot_download('opendatalab/PDF-Extract-Kit')
print(f"model dir is: {model_dir}/models")
def download_and_modify_json(url, local_filename, modifications):
if os.path.exists(local_filename):
data = json.load(open(local_filename))
else:
# 下载JSON文件
response = requests.get(url)
response.raise_for_status() # 检查请求是否成功
# 解析JSON内容
data = response.json()
# 修改内容
for key, value in modifications.items():
data[key] = value
# 保存修改后的内容
with open(local_filename, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=4)
if __name__ == '__main__':
model_dir = snapshot_download('opendatalab/PDF-Extract-Kit')
layoutreader_model_dir = snapshot_download('ppaanngggg/layoutreader')
model_dir = model_dir + "/models"
print(f"model_dir is: {model_dir}")
print(f"layoutreader_model_dir is: {layoutreader_model_dir}")
json_url = 'https://gitee.com/myhloli/MinerU/raw/master/magic-pdf.template.json'
config_file_name = "magic-pdf.json"
home_dir = os.path.expanduser("~")
config_file = os.path.join(home_dir, config_file_name)
json_mods = {
'models-dir': model_dir,
'layoutreader-model-dir': layoutreader_model_dir,
}
download_and_modify_json(json_url, config_file, json_mods)
print(f"The configuration file has been configured successfully, the path is: {config_file}")
\ No newline at end of file
docs/download_models_hf.py
View file @ 62aa1cbd
import os
import requests
import json
from huggingface_hub import snapshot_download
model_dir = snapshot_download('opendatalab/PDF-Extract-Kit')
print(f"model dir is: {model_dir}/models")
def download_and_modify_json(url, local_filename, modifications):
if os.path.exists(local_filename):
data = json.load(open(local_filename))
else:
# 下载JSON文件
response = requests.get(url)
response.raise_for_status() # 检查请求是否成功
# 解析JSON内容
data = response.json()
# 修改内容
for key, value in modifications.items():
data[key] = value
# 保存修改后的内容
with open(local_filename, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=4)
if __name__ == '__main__':
model_dir = snapshot_download('opendatalab/PDF-Extract-Kit')
layoutreader_model_dir = snapshot_download('hantian/layoutreader')
model_dir = model_dir + "/models"
print(f"model_dir is: {model_dir}")
print(f"layoutreader_model_dir is: {layoutreader_model_dir}")
json_url = 'https://github.com/opendatalab/MinerU/raw/master/magic-pdf.template.json'
config_file_name = "magic-pdf.json"
home_dir = os.path.expanduser("~")
config_file = os.path.join(home_dir, config_file_name)
json_mods = {
'models-dir': model_dir,
'layoutreader-model-dir': layoutreader_model_dir,
}
download_and_modify_json(json_url, config_file, json_mods)
print(f"The configuration file has been configured successfully, the path is: {config_file}")
\ No newline at end of file
docs/how_to_download_models_en.md
View file @ 62aa1cbd
### 1. Install Git LFS
Before you begin, make sure Git Large File Storage (Git LFS) is installed on your system. Install it using the following command:
Model downloads are divided into initial downloads and updates to the model directory. Please refer to the corresponding documentation for instructions on how to proceed.
```bash
git lfs install
```
### 2. Download the Model from Hugging Face
To download the `PDF-Extract-Kit` model from Hugging Face, use the following command:
# Initial download of model files
### 1. Download the Model from Hugging Face
Use a Python Script to Download Model Files from Hugging Face
```bash
git lfs clone https://huggingface.co/opendatalab/PDF-Extract-Kit
pip install huggingface_hub
wget https://github.com/opendatalab/MinerU/raw/master/docs/download_models_hf.py -O download_models_hf.py
python download_models_hf.py
```
The Python script will automatically download the model files and configure the model directory in the configuration file.
Ensure that Git LFS is enabled during the clone to properly download all large files.
The configuration file can be found in the user directory, with the filename `magic-pdf.json`.
### 3. Additional steps
# How to update models previously downloaded
#### 1. Check whether the model directory is downloaded completely.
## 1. Models downloaded via Git LFS
The structure of the model folder is as follows, including configuration files and weight files of different components:
```
../
├── Layout
│ ├── config.json
│ └── model_final.pth
├── MFD
│ └── weights.pt
├── MFR
│ └── UniMERNet
│ ├── config.json
│ ├── preprocessor_config.json
│ ├── pytorch_model.bin
│ ├── README.md
│ ├── tokenizer_config.json
│ └── tokenizer.json
│── TabRec
│ └─StructEqTable
│ ├── config.json
│ ├── generation_config.json
│ ├── model.safetensors
│ ├── preprocessor_config.json
│ ├── special_tokens_map.json
│ ├── spiece.model
│ ├── tokenizer.json
│ └── tokenizer_config.json
│ └─ TableMaster
│ └─ ch_PP-OCRv3_det_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
│ └─ ch_PP-OCRv3_rec_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
│ └─ table_structure_tablemaster_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
│ ├── ppocr_keys_v1.txt
│ └── table_master_structure_dict.txt
└── README.md
```
#### 2. Check whether the model file is fully downloaded.
>Due to feedback from some users that downloading model files using git lfs was incomplete or resulted in corrupted model files, this method is no longer recommended.
Please check whether the size of the model file in the directory is consistent with the description on the web page. If possible, it is best to check whether the model is downloaded completely through sha256.
If you previously downloaded model files via git lfs, you can navigate to the previous download directory and use the `git pull` command to update the model.
#### 3. Move the model to the solid-state drive
## 2. Models downloaded via Hugging Face or Model Scope
Move the 'models' directory to a directory with large disk space, preferably on a solid-state drive (SSD). In addition, modify the model directory in `~/magic-pdf.json` to point to the final model storage location, otherwise the model cannot be loaded.
\ No newline at end of file
If you previously downloaded models via Hugging Face or Model Scope, you can rerun the Python script used for the initial download. This will automatically update the model directory to the latest version.
\ No newline at end of file
docs/how_to_download_models_zh_cn.md
View file @ 62aa1cbd
# 如何下载模型文件
模型下载分为首次下载和更新模型目录，请参考对应的文档内容进行操作
模型文件可以从Hugging Face 或 Model Scope 下载，由于网络原因，国内用户访问HF 可能会失败，请使用 ModelScope。
# 首次下载模型文件
模型文件可以从 Hugging Face 或 Model Scope 下载，由于网络原因，国内用户访问HF可能会失败，请使用 ModelScope。
方法一：[从 Hugging Face 下载模型](#方法一从-hugging-face-下载模型)
<details>
<summary>方法一：从 Hugging Face 下载模型</summary>
<p>使用python脚本 从Hugging Face下载模型文件</p>
<pre><code>pip install huggingface_hub
wget https://gitee.com/myhloli/MinerU/raw/master/docs/download_models_hf.py -O download_models_hf.py
python download_models_hf.py</code></pre>
<p>python脚本执行完毕后，会输出模型下载目录</p>
</details>
方法二：[从 ModelScope 下载模型](#方法二从-modelscope-下载模型)
## 方法一：从 Hugging Face 下载模型
## 方法二：从 ModelScope 下载模型
使用Git LFS 从Hugging Face下载模型文件
### 使用python脚本 从ModelScope下载模型文件
```bash
git lfs install # 安装 Git 大文件存储插件 (Git LFS)
git lfs clone https://huggingface.co/opendatalab/PDF-Extract-Kit # 从 Hugging Face 下载 PDF-Extract-Kit 模型
pip install modelscope
wget https://gitee.com/myhloli/MinerU/raw/master/docs/download_models.py -O download_models.py
python download_models.py
```
python脚本会自动下载模型文件并配置好配置文件中的模型目录
配置文件可以在用户目录中找到，文件名为`magic-pdf.json`
> windows的用户目录为 "C:\\Users\\用户名", linux用户目录为 "/home/用户名", macOS用户目录为 "/Users/用户名"
## 方法二：从 ModelScope 下载模型
ModelScope 支持SDK或模型下载，任选一个即可。
[Git lsf下载](#1利用git-lsf下载)
[SDK下载](#2利用sdk下载)
### 1）利用Git lsf下载
```bash
git lfs install
git lfs clone https://www.modelscope.cn/opendatalab/PDF-Extract-Kit.git
```
### 2）利用SDK下载
# 此前下载过模型，如何更新
```bash
# 首先安装modelscope
pip install modelscope
```
## 1. 通过git lfs下载过模型
```python
# 使用modelscope sdk下载模型
from modelscope import snapshot_download
model_dir = snapshot_download('opendatalab/PDF-Extract-Kit')
print(f"模型文件下载路径为：{model_dir}/models")
```
>由于部分用户反馈通过git lfs下载模型文件遇到下载不全和模型文件损坏情况，现已不推荐使用该方式下载。
## 【❗️必须要做❗️】的额外步骤（模型下载完成后请务必完成以下操作）
如此前通过 git lfs 下载过模型文件，可以进入到之前的下载目录中，通过`git pull`命令更新模型。
### 1.检查模型目录是否下载完整
模型文件夹的结构如下，包含了不同组件的配置文件和权重文件：
```
./
├── Layout # 布局检测模型
│ ├── config.json
│ └── model_final.pth
├── MFD # 公式检测
│ └── weights.pt
├── MFR # 公式识别模型
│ └── UniMERNet
│ ├── config.json
│ ├── preprocessor_config.json
│ ├── pytorch_model.bin
│ ├── README.md
│ ├── tokenizer_config.json
│ └── tokenizer.json
│── TabRec # 表格识别模型
│ └─StructEqTable
│ ├── config.json
│ ├── generation_config.json
│ ├── model.safetensors
│ ├── preprocessor_config.json
│ ├── special_tokens_map.json
│ ├── spiece.model
│ ├── tokenizer.json
│ └── tokenizer_config.json
│ └─ TableMaster
│ └─ ch_PP-OCRv3_det_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
│ └─ ch_PP-OCRv3_rec_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
│ └─ table_structure_tablemaster_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
│ ├── ppocr_keys_v1.txt
│ └── table_master_structure_dict.txt
└── README.md
```
> 0.9.x及以后版本由于新增layout排序模型，且该模型和此前的模型不在同一仓库，不能通过`git pull`命令更新，需要单独下载。
>
>```
>from modelscope import snapshot_download
>snapshot_download('ppaanngggg/layoutreader')
>```
### 2.检查模型文件是否下载完整
请检查目录下的模型文件大小与网页上描述是否一致，如果可以的话，最好通过sha256校验模型是否下载完整
## 2. 通过 Hugging Face 或 Model Scope 下载过模型
### 3.移动模型到固态硬盘
将 'models' 目录移动到具有较大磁盘空间的目录中，最好是在固态硬盘(SSD)上。
此外在 `~/magic-pdf.json`里修改模型的目录指向最终的模型存放位置，否则会报模型无法加载的错误。
如此前通过 HuggingFace 或 Model Scope 下载过模型，可以重复执行此前的模型下载python脚本，将会自动将模型目录更新到最新版本。
\ No newline at end of file
magic-pdf.template.json
View file @ 62aa1cbd
......@@ -4,6 +4,7 @@
"bucket-name-2":["ak", "sk", "endpoint"]
},
"models-dir":"/tmp/models",
"layoutreader-model-dir":"/tmp/layoutreader",
"device-mode":"cpu",
"table-config": {
"model": "TableMaster",
......
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