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docs/version3.x/algorithm/PP-StructureV3/PP-StructureV3.en.md
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# Introduction to PP-StructureV3
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**PP-StructureV3** pipeline, based on the Layout Parsing v1 pipeline, has strengthened the ability of layout detection, table recognition, and formula recognition. It has also added the ability to understand charts and restore reading order, as well as the ability to convert results into Markdown files. In various document data, it performs excellently and can handle more complex document data. This pipeline also provides flexible service-oriented deployment methods, supporting the use of multiple programming languages on various hardware. Moreover, it also provides the ability for secondary development. You can train and optimize on your own dataset based on this pipeline, and the trained model can be seamlessly integrated.
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-StructureV3/algorithm_ppstructurev3.png" width="600"/>
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</div>
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# Key Metrics
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<table>
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<thead>
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<tr>
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<th rowspan="2">Method Type</th>
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<th rowspan="2">Methods</th>
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<th colspan="2">Overall<sup>Edit</sup>↓</th>
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<th colspan="2">Text<sup>Edit</sup>↓</th>
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<th colspan="2">Formula<sup>Edit</sup>↓</th>
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<th colspan="2">Table<sup>Edit</sup>↓</th>
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<th colspan="2">Read Order<sup>Edit</sup>↓</th>
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</tr>
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<tr>
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<th>EN</th>
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<th>ZH</th>
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<th>EN</th>
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||||
<th>ZH</th>
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<th>EN</th>
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<th>ZH</th>
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<th>EN</th>
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<th>ZH</th>
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<th>EN</th>
|
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<th>ZH</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td rowspan="9">Pipeline Tools</td>
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<td><b>PP-structureV3</b></td>
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<td><b>0.147</b></td>
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<td><b>0.212</b></td>
|
||||
<td>0.059</td>
|
||||
<td><b>0.09</b></td>
|
||||
<td>0.295</td>
|
||||
<td>0.535</td>
|
||||
<td>0.159</td>
|
||||
<td><b>0.109</b></td>
|
||||
<td>0.075</td>
|
||||
<td><b>0.114</b></td>
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||||
</tr>
|
||||
<tr>
|
||||
<td>MinerU-0.9.3</td>
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||||
<td>0.15</td>
|
||||
<td>0.357</td>
|
||||
<td>0.061</td>
|
||||
<td>0.215</td>
|
||||
<td>0.278</td>
|
||||
<td>0.577</td>
|
||||
<td>0.18</td>
|
||||
<td>0.344</td>
|
||||
<td>0.079</td>
|
||||
<td>0.292</td>
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||||
</tr>
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<tr>
|
||||
<td>MinerU-1.3.11</td>
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<td>0.166</td>
|
||||
<td>0.310</td>
|
||||
<td>0.0826</td>
|
||||
<td>0.2000</td>
|
||||
<td>0.3368</td>
|
||||
<td>0.6236</td>
|
||||
<td>0.1613</td>
|
||||
<td>0.1833</td>
|
||||
<td>0.0834</td>
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||||
<td>0.2316</td>
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||||
</tr>
|
||||
<tr>
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<td>Marker-1.2.3</td>
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<td>0.336</td>
|
||||
<td>0.556</td>
|
||||
<td>0.08</td>
|
||||
<td>0.315</td>
|
||||
<td>0.53</td>
|
||||
<td>0.883</td>
|
||||
<td>0.619</td>
|
||||
<td>0.685</td>
|
||||
<td>0.114</td>
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||||
<td>0.34</td>
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||||
</tr>
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||||
<tr>
|
||||
<td>Mathpix</td>
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||||
<td>0.191</td>
|
||||
<td>0.365</td>
|
||||
<td>0.105</td>
|
||||
<td>0.384</td>
|
||||
<td>0.306</td>
|
||||
<td>0.454</td>
|
||||
<td>0.243</td>
|
||||
<td>0.32</td>
|
||||
<td>0.108</td>
|
||||
<td>0.304</td>
|
||||
</tr>
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<tr>
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||||
<td>Docling-2.14.0</td>
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<td>0.589</td>
|
||||
<td>0.909</td>
|
||||
<td>0.416</td>
|
||||
<td>0.987</td>
|
||||
<td>0.999</td>
|
||||
<td>1</td>
|
||||
<td>0.627</td>
|
||||
<td>0.81</td>
|
||||
<td>0.313</td>
|
||||
<td>0.837</td>
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||||
</tr>
|
||||
<tr>
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||||
<td>Pix2Text-1.1.2.3</td>
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<td>0.32</td>
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||||
<td>0.528</td>
|
||||
<td>0.138</td>
|
||||
<td>0.356</td>
|
||||
<td><b>0.276</b></td>
|
||||
<td>0.611</td>
|
||||
<td>0.584</td>
|
||||
<td>0.645</td>
|
||||
<td>0.281</td>
|
||||
<td>0.499</td>
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</tr>
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<tr>
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||||
<td>Unstructured-0.17.2</td>
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<td>0.586</td>
|
||||
<td>0.716</td>
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||||
<td>0.198</td>
|
||||
<td>0.481</td>
|
||||
<td>0.999</td>
|
||||
<td>1</td>
|
||||
<td>1</td>
|
||||
<td>0.998</td>
|
||||
<td>0.145</td>
|
||||
<td>0.387</td>
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||||
</tr>
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<tr>
|
||||
<td>OpenParse-0.7.0</td>
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<td>0.646</td>
|
||||
<td>0.814</td>
|
||||
<td>0.681</td>
|
||||
<td>0.974</td>
|
||||
<td>0.996</td>
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||||
<td>1</td>
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||||
<td>0.284</td>
|
||||
<td>0.639</td>
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||||
<td>0.595</td>
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||||
<td>0.641</td>
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</tr>
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<tr>
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||||
<td rowspan="5">Expert VLMs</td>
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<td>GOT-OCR</td>
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<td>0.287</td>
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||||
<td>0.411</td>
|
||||
<td>0.189</td>
|
||||
<td>0.315</td>
|
||||
<td>0.36</td>
|
||||
<td>0.528</td>
|
||||
<td>0.459</td>
|
||||
<td>0.52</td>
|
||||
<td>0.141</td>
|
||||
<td>0.28</td>
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||||
</tr>
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||||
<tr>
|
||||
<td>Nougat</td>
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||||
<td>0.452</td>
|
||||
<td>0.973</td>
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||||
<td>0.365</td>
|
||||
<td>0.998</td>
|
||||
<td>0.488</td>
|
||||
<td>0.941</td>
|
||||
<td>0.572</td>
|
||||
<td>1</td>
|
||||
<td>0.382</td>
|
||||
<td>0.954</td>
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||||
</tr>
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||||
<tr>
|
||||
<td>Mistral OCR</td>
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||||
<td>0.268</td>
|
||||
<td>0.439</td>
|
||||
<td>0.072</td>
|
||||
<td>0.325</td>
|
||||
<td>0.318</td>
|
||||
<td>0.495</td>
|
||||
<td>0.6</td>
|
||||
<td>0.65</td>
|
||||
<td>0.083</td>
|
||||
<td>0.284</td>
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||||
</tr>
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||||
<tr>
|
||||
<td>OLMOCR-sglang</td>
|
||||
<td>0.326</td>
|
||||
<td>0.469</td>
|
||||
<td>0.097</td>
|
||||
<td>0.293</td>
|
||||
<td>0.455</td>
|
||||
<td>0.655</td>
|
||||
<td>0.608</td>
|
||||
<td>0.652</td>
|
||||
<td>0.145</td>
|
||||
<td>0.277</td>
|
||||
</tr>
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||||
<tr>
|
||||
<td>SmolDocling-256M_transformer</td>
|
||||
<td>0.493</td>
|
||||
<td>0.816</td>
|
||||
<td>0.262</td>
|
||||
<td>0.838</td>
|
||||
<td>0.753</td>
|
||||
<td>0.997</td>
|
||||
<td>0.729</td>
|
||||
<td>0.907</td>
|
||||
<td>0.227</td>
|
||||
<td>0.522</td>
|
||||
</tr>
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||||
<tr>
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||||
<td rowspan="6">General VLMs</td>
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||||
<td>Gemini2.0-flash</td>
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||||
<td>0.191</td>
|
||||
<td>0.264</td>
|
||||
<td>0.091</td>
|
||||
<td>0.139</td>
|
||||
<td>0.389</td>
|
||||
<td>0.584</td>
|
||||
<td>0.193</td>
|
||||
<td>0.206</td>
|
||||
<td>0.092</td>
|
||||
<td>0.128</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Gemini2.5-Pro</td>
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||||
<td>0.148</td>
|
||||
<td><b>0.212</b></td>
|
||||
<td><b>0.055</b></td>
|
||||
<td>0.168</td>
|
||||
<td>0.356</td>
|
||||
<td>0.439</td>
|
||||
<td><b>0.13</b></td>
|
||||
<td>0.119</td>
|
||||
<td><b>0.049</b></td>
|
||||
<td>0.121</td>
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||||
</tr>
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||||
<tr>
|
||||
<td>GPT4o</td>
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||||
<td>0.233</td>
|
||||
<td>0.399</td>
|
||||
<td>0.144</td>
|
||||
<td>0.409</td>
|
||||
<td>0.425</td>
|
||||
<td>0.606</td>
|
||||
<td>0.234</td>
|
||||
<td>0.329</td>
|
||||
<td>0.128</td>
|
||||
<td>0.251</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Qwen2-VL-72B</td>
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||||
<td>0.252</td>
|
||||
<td>0.327</td>
|
||||
<td>0.096</td>
|
||||
<td>0.218</td>
|
||||
<td>0.404</td>
|
||||
<td>0.487</td>
|
||||
<td>0.387</td>
|
||||
<td>0.408</td>
|
||||
<td>0.119</td>
|
||||
<td>0.193</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Qwen2.5-VL-72B</td>
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||||
<td>0.214</td>
|
||||
<td>0.261</td>
|
||||
<td>0.092</td>
|
||||
<td>0.18</td>
|
||||
<td>0.315</td>
|
||||
<td><b>0.434</b></td>
|
||||
<td>0.341</td>
|
||||
<td>0.262</td>
|
||||
<td>0.106</td>
|
||||
<td>0.168</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>InternVL2-76B</td>
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||||
<td>0.44</td>
|
||||
<td>0.443</td>
|
||||
<td>0.353</td>
|
||||
<td>0.29</td>
|
||||
<td>0.543</td>
|
||||
<td>0.701</td>
|
||||
<td>0.547</td>
|
||||
<td>0.555</td>
|
||||
<td>0.317</td>
|
||||
<td>0.228</td>
|
||||
</tr>
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||||
</tbody>
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</table>
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|
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The above data is from:
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* <a href="https://github.com/opendatalab/OmniDocBench">OmniDocBench</a>
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* <a href="https://arxiv.org/abs/2412.07626">OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations</a>
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|
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# End to End Benchmark
|
||||
|
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The performance of PP-StructureV3 and MinerU with different configurations under different GPU environments are as follows.
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|
||||
Requirements:
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||||
* Paddle 3.0
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* PaddleOCR 3.0.0
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||||
* MinerU 1.3.10
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* CUDA 11.8
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* cuDNN 8.9
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|
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## Local inference
|
||||
|
||||
Local inference was tested with both V100 and A100 GPU, evaluating the performance of PP-StructureV3 under 6 different configurations. The test data consists of 15 PDF files, totaling 925 pages, including elements such as tables, formulas, seals, and charts.
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|
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In the following PP-StructureV3 configuration, please refer to [PP-OCRv5](../PP-OCRv5/PP-OCRv5.en.md) for OCR model details, see [Formula Recognition](../../module_usage/formula_recognition.en.md) for formula recognition model details, and refer to [Text Detection](../../module_usage/text_detection.en.md) for the max_side_limit setting of the text detection module.
|
||||
|
||||
### Env: NVIDIA Tesla V100 + Intel Xeon Gold 6271C
|
||||
|
||||
<table border="1">
|
||||
<tr>
|
||||
<td>
|
||||
Methods
|
||||
</td>
|
||||
<td colspan="4">
|
||||
Configurations
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average time per
|
||||
page
|
||||
(s)
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average CPU
|
||||
（%）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Peak RAM Usage
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average RAM
|
||||
Usage
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average GPU
|
||||
（%）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Peak VRAM Usage
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average VRAM
|
||||
Usage
|
||||
（GB）
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td rowspan="7">
|
||||
PP-StructureV3
|
||||
</td>
|
||||
<td>
|
||||
OCR Models
|
||||
</td>
|
||||
<td>
|
||||
Formula Recognition Model
|
||||
</td>
|
||||
<td>
|
||||
Chart Recognition Model
|
||||
</td>
|
||||
<td>
|
||||
text detection module max_side_limit
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.77
|
||||
</td>
|
||||
<td>
|
||||
111.4
|
||||
</td>
|
||||
<td>
|
||||
6.7
|
||||
</td>
|
||||
<td>
|
||||
5.2
|
||||
</td>
|
||||
<td>
|
||||
38.9
|
||||
</td>
|
||||
<td>
|
||||
17.0
|
||||
</td>
|
||||
<td>
|
||||
16.5
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✔
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
4.09
|
||||
</td>
|
||||
<td>
|
||||
105.3
|
||||
</td>
|
||||
<td>
|
||||
5.5
|
||||
</td>
|
||||
<td>
|
||||
4.0
|
||||
</td>
|
||||
<td>
|
||||
24.7
|
||||
</td>
|
||||
<td>
|
||||
17.0
|
||||
</td>
|
||||
<td>
|
||||
16.6
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.56
|
||||
</td>
|
||||
<td>
|
||||
113.7
|
||||
</td>
|
||||
<td>
|
||||
6.6
|
||||
</td>
|
||||
<td>
|
||||
4.9
|
||||
</td>
|
||||
<td>
|
||||
29.1
|
||||
</td>
|
||||
<td>
|
||||
10.7
|
||||
</td>
|
||||
<td>
|
||||
10.6
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.42
|
||||
</td>
|
||||
<td>
|
||||
112.9
|
||||
</td>
|
||||
<td>
|
||||
6.8
|
||||
</td>
|
||||
<td>
|
||||
5.1
|
||||
</td>
|
||||
<td>
|
||||
38
|
||||
</td>
|
||||
<td>
|
||||
16.0
|
||||
</td>
|
||||
<td>
|
||||
15.5
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.15
|
||||
</td>
|
||||
<td>
|
||||
114.8
|
||||
</td>
|
||||
<td>
|
||||
6.5
|
||||
</td>
|
||||
<td>
|
||||
5.0
|
||||
</td>
|
||||
<td>
|
||||
26.1
|
||||
</td>
|
||||
<td>
|
||||
8.4
|
||||
</td>
|
||||
<td>
|
||||
8.3
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
1200
|
||||
</td>
|
||||
<td>
|
||||
0.99
|
||||
</td>
|
||||
<td>
|
||||
113
|
||||
</td>
|
||||
<td>
|
||||
7.0
|
||||
</td>
|
||||
<td>
|
||||
5.6
|
||||
</td>
|
||||
<td>
|
||||
29.2
|
||||
</td>
|
||||
<td>
|
||||
8.6
|
||||
</td>
|
||||
<td>
|
||||
8.5
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
MinerU
|
||||
</td>
|
||||
<td colspan="4">
|
||||
-
|
||||
</td>
|
||||
<td>
|
||||
1.57
|
||||
</td>
|
||||
<td>
|
||||
142.9
|
||||
</td>
|
||||
<td>
|
||||
13.3
|
||||
</td>
|
||||
<td>
|
||||
11.8
|
||||
</td>
|
||||
<td>
|
||||
43.3
|
||||
</td>
|
||||
<td>
|
||||
31.6
|
||||
</td>
|
||||
<td>
|
||||
9.7
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
### NVIDIA A100 + Intel Xeon Platinum 8350C
|
||||
|
||||
<table border="1">
|
||||
<tr>
|
||||
<td>
|
||||
Methods
|
||||
</td>
|
||||
<td colspan="4">
|
||||
Configurations
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average time per
|
||||
page
|
||||
(s)
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average CPU
|
||||
（%）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Peak RAM Usage
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average RAM
|
||||
Usage
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average GPU
|
||||
（%）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Peak VRAM Usage
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
Average VRAM
|
||||
Usage
|
||||
（GB）
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td rowspan="7">
|
||||
PP-StructureV3
|
||||
</td>
|
||||
<td>
|
||||
OCR Models
|
||||
</td>
|
||||
<td>
|
||||
Formula Recognition Model
|
||||
</td>
|
||||
<td>
|
||||
Chart Recognition Model
|
||||
</td>
|
||||
<td>
|
||||
text detection module max_side_limit
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.12
|
||||
</td>
|
||||
<td>
|
||||
109.8
|
||||
</td>
|
||||
<td>
|
||||
9.2
|
||||
</td>
|
||||
<td>
|
||||
7.8
|
||||
</td>
|
||||
<td>
|
||||
29.8
|
||||
</td>
|
||||
<td>
|
||||
21.8
|
||||
</td>
|
||||
<td>
|
||||
21.1
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✔
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
2.76
|
||||
</td>
|
||||
<td>
|
||||
103.7
|
||||
</td>
|
||||
<td>
|
||||
9.0
|
||||
</td>
|
||||
<td>
|
||||
7.7
|
||||
</td>
|
||||
<td>
|
||||
24
|
||||
</td>
|
||||
<td>
|
||||
21.8
|
||||
</td>
|
||||
<td>
|
||||
21.1
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.04
|
||||
</td>
|
||||
<td>
|
||||
110.7
|
||||
</td>
|
||||
<td>
|
||||
9.3
|
||||
</td>
|
||||
<td>
|
||||
7.8
|
||||
</td>
|
||||
<td>
|
||||
22
|
||||
</td>
|
||||
<td>
|
||||
12.2
|
||||
</td>
|
||||
<td>
|
||||
12.1
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
0.95
|
||||
</td>
|
||||
<td>
|
||||
111.4
|
||||
</td>
|
||||
<td>
|
||||
9.1
|
||||
</td>
|
||||
<td>
|
||||
7.8
|
||||
</td>
|
||||
<td>
|
||||
28.1
|
||||
</td>
|
||||
<td>
|
||||
21.8
|
||||
</td>
|
||||
<td>
|
||||
21.0
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
0.89
|
||||
</td>
|
||||
<td>
|
||||
112.1
|
||||
</td>
|
||||
<td>
|
||||
9.2
|
||||
</td>
|
||||
<td>
|
||||
7.8
|
||||
</td>
|
||||
<td>
|
||||
18.5
|
||||
</td>
|
||||
<td>
|
||||
11.4
|
||||
</td>
|
||||
<td>
|
||||
11.2
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
1200
|
||||
</td>
|
||||
<td>
|
||||
0.64
|
||||
</td>
|
||||
<td>
|
||||
113.5
|
||||
</td>
|
||||
<td>
|
||||
10.2
|
||||
</td>
|
||||
<td>
|
||||
8.5
|
||||
</td>
|
||||
<td>
|
||||
23.7
|
||||
</td>
|
||||
<td>
|
||||
11.4
|
||||
</td>
|
||||
<td>
|
||||
11.2
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
MinerU
|
||||
</td>
|
||||
<td colspan="4">
|
||||
-
|
||||
</td>
|
||||
<td>
|
||||
1.06
|
||||
</td>
|
||||
<td>
|
||||
168.3
|
||||
</td>
|
||||
<td>
|
||||
18.3
|
||||
</td>
|
||||
<td>
|
||||
16.8
|
||||
</td>
|
||||
<td>
|
||||
27.5
|
||||
</td>
|
||||
<td>
|
||||
76.9
|
||||
</td>
|
||||
<td>
|
||||
14.8
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
## Serving Inference
|
||||
|
||||
The serving inference test is based on the NVIDIA A100 + Intel Xeon Platinum 8350C environment, with test data consisting of 1500 images, including tables, formulas, seals, charts, and other elements.
|
||||
|
||||
<table>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>Instances Number</td>
|
||||
<td>Concurrent Requests Number</td>
|
||||
<td>Throughput</td>
|
||||
<td>Average Latency (s)</td>
|
||||
<td>Success Number/Total Number</td>
|
||||
</tr>
|
||||
<tr">
|
||||
<td>4 GPUs ✖️ 1 instance/gpu</td>
|
||||
<td>4</td>
|
||||
<td>1.69</td>
|
||||
<td>2.36</td>
|
||||
<td>100%</td>
|
||||
</tr>
|
||||
<tr">
|
||||
<td>4 GPUs ✖️ 4 instances/gpu</td>
|
||||
<td>16</td>
|
||||
<td>4.05</td>
|
||||
<td>3.87</td>
|
||||
<td>100%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
# PP-StructureV3 Demo
|
||||
|
||||
<div align="center">
|
||||
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-StructureV3/algorithm_ppstructurev3_demo.png" width="600"/>
|
||||
</div>
|
||||
|
||||
<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex%2FPaddleX3.0%2Fdoc_images%2FPP-StructureV3%2Falgorithm_ppstructurev3_demo.pdf">More Demos</a>
|
||||
|
||||
|
||||
# FAQ
|
||||
|
||||
1. What is the default configuration? How to get higher accuracy, faster speed, or smaller GPU memory?
|
||||
|
||||
When using mobile OCR models + PP-FormulaNet_plus-M, and max length of text detection set to 1200, if set use_chart_recognition to False and dont not load the chart recognition model, the GPU memory would be reduced.
|
||||
|
||||
On the V100, the peak and average GPU memory would be reduced from 8776.0 MB and 8680.8 MB to 6118.0 MB and 6016.7 MB, respectively; On the A100, the peak and average GPU memory would be reduced from 11716.0 MB and 11453.9 MB to 9850.0 MB and 9593.5 MB, respectively.
|
||||
|
||||
You can using multi-gpus by setting `device` to `gpu:<no.>,<no.>`, such as `gpu:0,1,2,3`. And about multi-process parallel inference, you can refer: [Multi-Process Parallel Inference](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/pipeline_usage/instructions/parallel_inference.en.md#example-of-multi-process-parallel-inference).
|
||||
|
||||
2. About serving deployment
|
||||
|
||||
(1) Can the service handle requests concurrently?
|
||||
|
||||
For the basic serving deployment solution, the service processes only one request at a time. This plan is mainly used for rapid verification, to establish the development chain, or for scenarios where concurrent requests are not required.
|
||||
|
||||
For high-stability serving deployment solution, the service process only one request at a time by default, but you can refer to the related docs to adjust achieve scaling.
|
||||
|
||||
（2）How to reduce latency and improve throughput?
|
||||
|
||||
Use the High-performance inference plugin, and deploy multi instances.
|
||||
971
docs/version3.x/algorithm/PP-StructureV3/PP-StructureV3.md
Normal file
971
docs/version3.x/algorithm/PP-StructureV3/PP-StructureV3.md
Normal file
@@ -0,0 +1,971 @@
|
||||
# 一、PP-StructureV3 简介
|
||||
**PP-StructureV3** 产线在通用版面解析v1产线的基础上，强化了版面区域检测、表格识别、公式识别的能力，增加了图表理解和多栏阅读顺序的恢复能力、结果转换 Markdown 文件的能力，在多种文档数据中，表现优异，可以处理较复杂的文档数据。本产线同时提供了灵活的服务化部署方式，支持在多种硬件上使用多种编程语言调用。不仅如此，本产线也提供了二次开发的能力，您可以基于本产线在您自己的数据集上训练调优，训练后的模型也可以无缝集成。
|
||||
|
||||
<div align="center">
|
||||
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-StructureV3/algorithm_ppstructurev3.png" width="600"/>
|
||||
</div>
|
||||
|
||||
# 二、关键指标
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th rowspan="2">Method Type</th>
|
||||
<th rowspan="2">Methods</th>
|
||||
<th colspan="2">Overall<sup>Edit</sup>↓</th>
|
||||
<th colspan="2">Text<sup>Edit</sup>↓</th>
|
||||
<th colspan="2">Formula<sup>Edit</sup>↓</th>
|
||||
<th colspan="2">Table<sup>Edit</sup>↓</th>
|
||||
<th colspan="2">Read Order<sup>Edit</sup>↓</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>EN</th>
|
||||
<th>ZH</th>
|
||||
<th>EN</th>
|
||||
<th>ZH</th>
|
||||
<th>EN</th>
|
||||
<th>ZH</th>
|
||||
<th>EN</th>
|
||||
<th>ZH</th>
|
||||
<th>EN</th>
|
||||
<th>ZH</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td rowspan="9">Pipeline Tools</td>
|
||||
<td><b>PP-structureV3</b></td>
|
||||
<td><b>0.147</b></td>
|
||||
<td><b>0.212</b></td>
|
||||
<td>0.059</td>
|
||||
<td><b>0.09</b></td>
|
||||
<td>0.295</td>
|
||||
<td>0.535</td>
|
||||
<td>0.159</td>
|
||||
<td><b>0.109</b></td>
|
||||
<td>0.075</td>
|
||||
<td><b>0.114</b></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>MinerU-0.9.3</td>
|
||||
<td>0.15</td>
|
||||
<td>0.357</td>
|
||||
<td>0.061</td>
|
||||
<td>0.215</td>
|
||||
<td>0.278</td>
|
||||
<td>0.577</td>
|
||||
<td>0.18</td>
|
||||
<td>0.344</td>
|
||||
<td>0.079</td>
|
||||
<td>0.292</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>MinerU-1.3.11</td>
|
||||
<td>0.166</td>
|
||||
<td>0.310</td>
|
||||
<td>0.0826</td>
|
||||
<td>0.2000</td>
|
||||
<td>0.3368</td>
|
||||
<td>0.6236</td>
|
||||
<td>0.1613</td>
|
||||
<td>0.1833</td>
|
||||
<td>0.0834</td>
|
||||
<td>0.2316</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Marker-1.2.3</td>
|
||||
<td>0.336</td>
|
||||
<td>0.556</td>
|
||||
<td>0.08</td>
|
||||
<td>0.315</td>
|
||||
<td>0.53</td>
|
||||
<td>0.883</td>
|
||||
<td>0.619</td>
|
||||
<td>0.685</td>
|
||||
<td>0.114</td>
|
||||
<td>0.34</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Mathpix</td>
|
||||
<td>0.191</td>
|
||||
<td>0.365</td>
|
||||
<td>0.105</td>
|
||||
<td>0.384</td>
|
||||
<td>0.306</td>
|
||||
<td>0.454</td>
|
||||
<td>0.243</td>
|
||||
<td>0.32</td>
|
||||
<td>0.108</td>
|
||||
<td>0.304</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Docling-2.14.0</td>
|
||||
<td>0.589</td>
|
||||
<td>0.909</td>
|
||||
<td>0.416</td>
|
||||
<td>0.987</td>
|
||||
<td>0.999</td>
|
||||
<td>1</td>
|
||||
<td>0.627</td>
|
||||
<td>0.81</td>
|
||||
<td>0.313</td>
|
||||
<td>0.837</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Pix2Text-1.1.2.3</td>
|
||||
<td>0.32</td>
|
||||
<td>0.528</td>
|
||||
<td>0.138</td>
|
||||
<td>0.356</td>
|
||||
<td><b>0.276</b></td>
|
||||
<td>0.611</td>
|
||||
<td>0.584</td>
|
||||
<td>0.645</td>
|
||||
<td>0.281</td>
|
||||
<td>0.499</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Unstructured-0.17.2</td>
|
||||
<td>0.586</td>
|
||||
<td>0.716</td>
|
||||
<td>0.198</td>
|
||||
<td>0.481</td>
|
||||
<td>0.999</td>
|
||||
<td>1</td>
|
||||
<td>1</td>
|
||||
<td>0.998</td>
|
||||
<td>0.145</td>
|
||||
<td>0.387</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>OpenParse-0.7.0</td>
|
||||
<td>0.646</td>
|
||||
<td>0.814</td>
|
||||
<td>0.681</td>
|
||||
<td>0.974</td>
|
||||
<td>0.996</td>
|
||||
<td>1</td>
|
||||
<td>0.284</td>
|
||||
<td>0.639</td>
|
||||
<td>0.595</td>
|
||||
<td>0.641</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td rowspan="5">Expert VLMs</td>
|
||||
<td>GOT-OCR</td>
|
||||
<td>0.287</td>
|
||||
<td>0.411</td>
|
||||
<td>0.189</td>
|
||||
<td>0.315</td>
|
||||
<td>0.36</td>
|
||||
<td>0.528</td>
|
||||
<td>0.459</td>
|
||||
<td>0.52</td>
|
||||
<td>0.141</td>
|
||||
<td>0.28</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Nougat</td>
|
||||
<td>0.452</td>
|
||||
<td>0.973</td>
|
||||
<td>0.365</td>
|
||||
<td>0.998</td>
|
||||
<td>0.488</td>
|
||||
<td>0.941</td>
|
||||
<td>0.572</td>
|
||||
<td>1</td>
|
||||
<td>0.382</td>
|
||||
<td>0.954</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Mistral OCR</td>
|
||||
<td>0.268</td>
|
||||
<td>0.439</td>
|
||||
<td>0.072</td>
|
||||
<td>0.325</td>
|
||||
<td>0.318</td>
|
||||
<td>0.495</td>
|
||||
<td>0.6</td>
|
||||
<td>0.65</td>
|
||||
<td>0.083</td>
|
||||
<td>0.284</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>OLMOCR-sglang</td>
|
||||
<td>0.326</td>
|
||||
<td>0.469</td>
|
||||
<td>0.097</td>
|
||||
<td>0.293</td>
|
||||
<td>0.455</td>
|
||||
<td>0.655</td>
|
||||
<td>0.608</td>
|
||||
<td>0.652</td>
|
||||
<td>0.145</td>
|
||||
<td>0.277</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>SmolDocling-256M_transformer</td>
|
||||
<td>0.493</td>
|
||||
<td>0.816</td>
|
||||
<td>0.262</td>
|
||||
<td>0.838</td>
|
||||
<td>0.753</td>
|
||||
<td>0.997</td>
|
||||
<td>0.729</td>
|
||||
<td>0.907</td>
|
||||
<td>0.227</td>
|
||||
<td>0.522</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td rowspan="6">General VLMs</td>
|
||||
<td>Gemini2.0-flash</td>
|
||||
<td>0.191</td>
|
||||
<td>0.264</td>
|
||||
<td>0.091</td>
|
||||
<td>0.139</td>
|
||||
<td>0.389</td>
|
||||
<td>0.584</td>
|
||||
<td>0.193</td>
|
||||
<td>0.206</td>
|
||||
<td>0.092</td>
|
||||
<td>0.128</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Gemini2.5-Pro</td>
|
||||
<td>0.148</td>
|
||||
<td><b>0.212</b></td>
|
||||
<td><b>0.055</b></td>
|
||||
<td>0.168</td>
|
||||
<td>0.356</td>
|
||||
<td>0.439</td>
|
||||
<td><b>0.13</b></td>
|
||||
<td>0.119</td>
|
||||
<td><b>0.049</b></td>
|
||||
<td>0.121</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>GPT4o</td>
|
||||
<td>0.233</td>
|
||||
<td>0.399</td>
|
||||
<td>0.144</td>
|
||||
<td>0.409</td>
|
||||
<td>0.425</td>
|
||||
<td>0.606</td>
|
||||
<td>0.234</td>
|
||||
<td>0.329</td>
|
||||
<td>0.128</td>
|
||||
<td>0.251</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Qwen2-VL-72B</td>
|
||||
<td>0.252</td>
|
||||
<td>0.327</td>
|
||||
<td>0.096</td>
|
||||
<td>0.218</td>
|
||||
<td>0.404</td>
|
||||
<td>0.487</td>
|
||||
<td>0.387</td>
|
||||
<td>0.408</td>
|
||||
<td>0.119</td>
|
||||
<td>0.193</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Qwen2.5-VL-72B</td>
|
||||
<td>0.214</td>
|
||||
<td>0.261</td>
|
||||
<td>0.092</td>
|
||||
<td>0.18</td>
|
||||
<td>0.315</td>
|
||||
<td><b>0.434</b></td>
|
||||
<td>0.341</td>
|
||||
<td>0.262</td>
|
||||
<td>0.106</td>
|
||||
<td>0.168</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>InternVL2-76B</td>
|
||||
<td>0.44</td>
|
||||
<td>0.443</td>
|
||||
<td>0.353</td>
|
||||
<td>0.29</td>
|
||||
<td>0.543</td>
|
||||
<td>0.701</td>
|
||||
<td>0.547</td>
|
||||
<td>0.555</td>
|
||||
<td>0.317</td>
|
||||
<td>0.228</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
以上部分数据出自：
|
||||
* <a href="https://github.com/opendatalab/OmniDocBench">OmniDocBench</a>
|
||||
* <a href="https://arxiv.org/abs/2412.07626">OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations</a>
|
||||
|
||||
|
||||
# 三、推理 Benchmark
|
||||
|
||||
在不同GPU环境下，不同配置的 PP-StructureV3 和 MinerU 对比的性能指标如下。
|
||||
|
||||
基本测试环境：
|
||||
* Paddle 3.0正式版
|
||||
* PaddleOCR 3.0.0正式版
|
||||
* MinerU 1.3.10
|
||||
* CUDA 11.8
|
||||
* cuDNN 8.9
|
||||
|
||||
## 3.1 本地推理
|
||||
|
||||
本地推理分别在 V100 和 A100 两种 GPU机器上，测试了 6 种不同配置下 PP-StructureV3 的性能，测试数据为15个PDF文件，共925页，包含表格、公式、印章、图表等元素。
|
||||
|
||||
下述 PP-StructureV3 配置中，OCR 模型详情请见[PP-OCRv5](../PP-OCRv5/PP-OCRv5.md)，公式识别模型详情请见[公式识别](../../module_usage/formula_recognition.md)，文本检测模块 max_side_limit 设置请见[文本检测](../../module_usage/text_detection.md)。
|
||||
|
||||
### NVIDIA Tesla V100 + Intel Xeon Gold 6271C
|
||||
<table border="1">
|
||||
<tr>
|
||||
<td>
|
||||
方案
|
||||
</td>
|
||||
<td colspan="4">
|
||||
配置
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均每页耗时
|
||||
（s）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均CPU利用率
|
||||
（%）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
峰值RAM用量
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均RAM用量
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均GPU利用率
|
||||
（%）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
峰值VRAM用量
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均VRAM用量
|
||||
（GB）
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td rowspan="7">
|
||||
PP-StructureV3
|
||||
</td>
|
||||
<td>
|
||||
OCR模型
|
||||
</td>
|
||||
<td>
|
||||
公式识别模型
|
||||
</td>
|
||||
<td>
|
||||
是否启用图表识别模块
|
||||
</td>
|
||||
<td>
|
||||
文本检测max_side_limit
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.77
|
||||
</td>
|
||||
<td>
|
||||
111.4
|
||||
</td>
|
||||
<td>
|
||||
6.7
|
||||
</td>
|
||||
<td>
|
||||
5.2
|
||||
</td>
|
||||
<td>
|
||||
38.9
|
||||
</td>
|
||||
<td>
|
||||
17.0
|
||||
</td>
|
||||
<td>
|
||||
16.5
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✔
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
4.09
|
||||
</td>
|
||||
<td>
|
||||
105.3
|
||||
</td>
|
||||
<td>
|
||||
5.5
|
||||
</td>
|
||||
<td>
|
||||
4.0
|
||||
</td>
|
||||
<td>
|
||||
24.7
|
||||
</td>
|
||||
<td>
|
||||
17.0
|
||||
</td>
|
||||
<td>
|
||||
16.6
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.56
|
||||
</td>
|
||||
<td>
|
||||
113.7
|
||||
</td>
|
||||
<td>
|
||||
6.6
|
||||
</td>
|
||||
<td>
|
||||
4.9
|
||||
</td>
|
||||
<td>
|
||||
29.1
|
||||
</td>
|
||||
<td>
|
||||
10.7
|
||||
</td>
|
||||
<td>
|
||||
10.6
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.42
|
||||
</td>
|
||||
<td>
|
||||
112.9
|
||||
</td>
|
||||
<td>
|
||||
6.8
|
||||
</td>
|
||||
<td>
|
||||
5.1
|
||||
</td>
|
||||
<td>
|
||||
38
|
||||
</td>
|
||||
<td>
|
||||
16.0
|
||||
</td>
|
||||
<td>
|
||||
15.5
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.15
|
||||
</td>
|
||||
<td>
|
||||
114.8
|
||||
</td>
|
||||
<td>
|
||||
6.5
|
||||
</td>
|
||||
<td>
|
||||
5.0
|
||||
</td>
|
||||
<td>
|
||||
26.1
|
||||
</td>
|
||||
<td>
|
||||
8.4
|
||||
</td>
|
||||
<td>
|
||||
8.3
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
1200
|
||||
</td>
|
||||
<td>
|
||||
0.99
|
||||
</td>
|
||||
<td>
|
||||
113
|
||||
</td>
|
||||
<td>
|
||||
7.0
|
||||
</td>
|
||||
<td>
|
||||
5.6
|
||||
</td>
|
||||
<td>
|
||||
29.2
|
||||
</td>
|
||||
<td>
|
||||
8.6
|
||||
</td>
|
||||
<td>
|
||||
8.5
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
MinerU
|
||||
</td>
|
||||
<td colspan="4">
|
||||
-
|
||||
</td>
|
||||
<td>
|
||||
1.57
|
||||
</td>
|
||||
<td>
|
||||
142.9
|
||||
</td>
|
||||
<td>
|
||||
13.3
|
||||
</td>
|
||||
<td>
|
||||
11.8
|
||||
</td>
|
||||
<td>
|
||||
43.3
|
||||
</td>
|
||||
<td>
|
||||
31.6
|
||||
</td>
|
||||
<td>
|
||||
9.7
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
### NVIDIA A100 + Intel Xeon Platinum 8350C
|
||||
<table border="1">
|
||||
<tr>
|
||||
<td>
|
||||
方案
|
||||
</td>
|
||||
<td colspan="4">
|
||||
配置
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均每页耗时
|
||||
（s）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均CPU利用率
|
||||
（%）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
峰值RAM用量
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均RAM用量
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均GPU利用率
|
||||
（%）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
峰值VRAM用量
|
||||
（GB）
|
||||
</td>
|
||||
<td rowspan="2">
|
||||
平均VRAM用量
|
||||
（GB）
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td rowspan="7">
|
||||
PP-StructureV3
|
||||
</td>
|
||||
<td>
|
||||
OCR模型
|
||||
</td>
|
||||
<td>
|
||||
公式识别模型
|
||||
</td>
|
||||
<td>
|
||||
是否启用图表识别模块
|
||||
</td>
|
||||
<td>
|
||||
文本检测max_side_limit
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.12
|
||||
</td>
|
||||
<td>
|
||||
109.8
|
||||
</td>
|
||||
<td>
|
||||
9.2
|
||||
</td>
|
||||
<td>
|
||||
7.8
|
||||
</td>
|
||||
<td>
|
||||
29.8
|
||||
</td>
|
||||
<td>
|
||||
21.8
|
||||
</td>
|
||||
<td>
|
||||
21.1
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✔
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
2.76
|
||||
</td>
|
||||
<td>
|
||||
103.7
|
||||
</td>
|
||||
<td>
|
||||
9.0
|
||||
</td>
|
||||
<td>
|
||||
7.7
|
||||
</td>
|
||||
<td>
|
||||
24
|
||||
</td>
|
||||
<td>
|
||||
21.8
|
||||
</td>
|
||||
<td>
|
||||
21.1
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-L
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
1.04
|
||||
</td>
|
||||
<td>
|
||||
110.7
|
||||
</td>
|
||||
<td>
|
||||
9.3
|
||||
</td>
|
||||
<td>
|
||||
7.8
|
||||
</td>
|
||||
<td>
|
||||
22
|
||||
</td>
|
||||
<td>
|
||||
12.2
|
||||
</td>
|
||||
<td>
|
||||
12.1
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Server系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
0.95
|
||||
</td>
|
||||
<td>
|
||||
111.4
|
||||
</td>
|
||||
<td>
|
||||
9.1
|
||||
</td>
|
||||
<td>
|
||||
7.8
|
||||
</td>
|
||||
<td>
|
||||
28.1
|
||||
</td>
|
||||
<td>
|
||||
21.8
|
||||
</td>
|
||||
<td>
|
||||
21.0
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
4096
|
||||
</td>
|
||||
<td>
|
||||
0.89
|
||||
</td>
|
||||
<td>
|
||||
112.1
|
||||
</td>
|
||||
<td>
|
||||
9.2
|
||||
</td>
|
||||
<td>
|
||||
7.8
|
||||
</td>
|
||||
<td>
|
||||
18.5
|
||||
</td>
|
||||
<td>
|
||||
11.4
|
||||
</td>
|
||||
<td>
|
||||
11.2
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
Mobile系列
|
||||
</td>
|
||||
<td>
|
||||
PP-FormulaNet-M
|
||||
</td>
|
||||
<td>
|
||||
✗
|
||||
</td>
|
||||
<td>
|
||||
1200
|
||||
</td>
|
||||
<td>
|
||||
0.64
|
||||
</td>
|
||||
<td>
|
||||
113.5
|
||||
</td>
|
||||
<td>
|
||||
10.2
|
||||
</td>
|
||||
<td>
|
||||
8.5
|
||||
</td>
|
||||
<td>
|
||||
23.7
|
||||
</td>
|
||||
<td>
|
||||
11.4
|
||||
</td>
|
||||
<td>
|
||||
11.2
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
MinerU
|
||||
</td>
|
||||
<td colspan="4">
|
||||
-
|
||||
</td>
|
||||
<td>
|
||||
1.06
|
||||
</td>
|
||||
<td>
|
||||
168.3
|
||||
</td>
|
||||
<td>
|
||||
18.3
|
||||
</td>
|
||||
<td>
|
||||
16.8
|
||||
</td>
|
||||
<td>
|
||||
27.5
|
||||
</td>
|
||||
<td>
|
||||
76.9
|
||||
</td>
|
||||
<td>
|
||||
14.8
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
## 3.2 服务化部署
|
||||
|
||||
服务化部署测试基于 NVIDIA A100 + Intel Xeon Platinum 8350C 环境，测试数据为 1500 张图像，包含表格、公式、印章、图表等元素。
|
||||
|
||||
<table>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>实例数</td>
|
||||
<td>并发请求数</td>
|
||||
<td>吞吐</td>
|
||||
<td>平均时延（s）</td>
|
||||
<td>成功请求数/总请求数</td>
|
||||
</tr>
|
||||
<tr">
|
||||
<td>4卡 ✖️ 1实例/卡</td>
|
||||
<td>4</td>
|
||||
<td>1.69</td>
|
||||
<td>2.36</td>
|
||||
<td>100%</td>
|
||||
</tr>
|
||||
<tr">
|
||||
<td>4卡 ✖️ 4实例/卡</td>
|
||||
<td>16</td>
|
||||
<td>4.05</td>
|
||||
<td>3.87</td>
|
||||
<td>100%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
# 四、PP-StructureV3 Demo示例
|
||||
|
||||
<div align="center">
|
||||
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr/PP-StructureV3/algorithm_ppstructurev3_demo.png" width="600"/>
|
||||
</div>
|
||||
|
||||
<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex%2FPaddleX3.0%2Fdoc_images%2FPP-StructureV3%2Falgorithm_ppstructurev3_demo.pdf">更多示例</a>
|
||||
|
||||
# 五、使用方法和常见问题
|
||||
|
||||
1. 默认模型是什么配置，如果需要更高精度、更快速度、或者更小显存，应该调哪些参数或者更换哪些模型，对结果影响大概有多大？
|
||||
|
||||
在“使用轻量OCR模型+轻量公式模型，文本检测max 1200”的基础上，将产线配置文件中的use_chart_recognition设置为False，不加载图表识别模型，可以进一步减少显存用量。在V100测试环境中，峰值和平均显存用量分别从8776.0 MB和8680.8 MB降低到6118.0 MB和6016.7 MB；在A100测试环境中，峰值和平均显存用量分别从11716.0 MB和11453.9 MB降低到9850.0 MB和9593.5 MB。
|
||||
在Python API或CLI设置device为<设备类型>:<设备编号1>,<设备编号2>...（例如gpu:0,1,2,3）可实现多卡并行推理。如果内置的多卡并行推理功能提速效果仍不满足预期，可参考多进程并行推理示例代码，结合具体场景进行进一步优化：[多进程并行推理](https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/pipeline_usage/instructions/parallel_inference.md#%E5%A4%9A%E8%BF%9B%E7%A8%8B%E5%B9%B6%E8%A1%8C%E6%8E%A8%E7%90%86%E7%A4%BA%E4%BE%8B)。
|
||||
|
||||
2. 服务化部署的常见问题
|
||||
|
||||
（1）服务可以并发处理请求吗？
|
||||
|
||||
对于基础服务化部署方案，服务同一时间只处理一个请求，该方案主要用于快速验证、打通开发链路，或者用在不需要并发请求的场景；
|
||||
|
||||
对于高稳定性服务化部署方案，服务默认在同一时间只处理一个请求，但用户可以参考服务化部署指南，通过调整配置实现水平扩展，以使服务同时处理多个请求。
|
||||
|
||||
（2）如何降低时延、提升吞吐？
|
||||
|
||||
无论使用哪一种服务化部署方案，都可以通过启用高性能推理插件提升模型推理速度，从而降低处理时延。
|
||||
|
||||
此外，对于高稳定性服务化部署方案，通过调整服务配置，设置多个实例，也可以充分利用部署机器的资源，有效提升吞吐。
|
||||
Reference in New Issue
Block a user