PaddleOCR模型列表（CPU/GPU）

PaddleOCR 内置了多条产线，每条产线都包含了若干模块，每个模块包含若干模型，具体使用哪些模型，您可以根据下边的 benchmark 数据来选择。如您更考虑模型精度，请选择精度较高的模型，如您更考虑模型推理速度，请选择推理速度较快的模型，如您更考虑模型存储大小，请选择存储大小较小的模型。

文本检测模块

<table> <thead> <tr> <th>模型</th> <th>检测Hmean（%）</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th> </tr> </thead> <tbody> <tr> <td>PP-OCRv5_server_det</td> <td>83.8</td> <td>89.55 / 70.19</td> <td>383.15 / 383.15</td> <td>101</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv5_server_det.yaml">PP-OCRv5_server_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_det_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv5_mobile_det</td> <td>79.0</td> <td>10.67 / 6.36</td> <td>57.77 / 28.15</td> <td>4.7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml">PP-OCRv5_mobile_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_det_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv4_server_det</td> <td>82.56</td> <td>127.82 / 98.87</td> <td>585.95 / 489.77</td> <td>109</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv4_server_det.yaml">PP-OCRv4_server_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_det_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv4_mobile_det</td> <td>63.8</td> <td>9.87 / 4.17</td> <td>56.60 / 20.79</td> <td>4.7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv4_mobile_det.yaml">PP-OCRv4_mobile_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_det_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv3_mobile_det</td> <td>78.68</td> <td>9.90 / 3.60</td> <td>41.93 / 20.76</td> <td>2.1</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv3_mobile_det.yaml">PP-OCRv3_mobile_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_mobile_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_mobile_det_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv3_server_det</td> <td>80.11</td> <td>119.50 / 75.00</td> <td>379.35 / 318.35</td> <td>102.1</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_detection/PP-OCRv3_server_det.yaml">PP-OCRv3_server_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_server_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_server_det_pretrained.pdparams">训练模型</a></td> </tr> </tbody> </table> <b>注：以上精度指标的评估集是 PaddleOCR 自建的中英文数据集，覆盖街景、网图、文档、手写多个场景，其中文本识别包含 593 张图片。</b>

印章文本检测模块

<table> <thead> <tr> <th>模型名称</th> <th>检测Hmean（%）</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th></tr> </thead> <tbody> <tr> <td>PP-OCRv4_mobile_seal_det</td> <td>96.36</td> <td>9.70 / 3.56</td> <td>50.38 / 19.64</td> <td>4.7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/seal_text_detection/PP-OCRv4_mobile_seal_det.yaml">PP-OCRv4_mobile_seal_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_seal_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_seal_det_pretrained.pdparams">训练模型</a></td></tr> <tr> <td>PP-OCRv4_server_seal_det</td> <td>98.40</td> <td>124.64 / 91.57</td> <td>545.68 / 439.86</td> <td>109</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/seal_text_detection/PP-OCRv4_server_seal_det.yaml">PP-OCRv4_server_seal_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_seal_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_seal_det_pretrained.pdparams">训练模型</a></td></tr> </tbody> </table> <b>注：以上精度指标的评估集是 PaddleOCR 自建的印章数据集，包含500印章图像。</b>

文本识别模块

<b>中文识别模型</b>

<table> <tr> <th>模型</th> <th>识别 Avg Accuracy(%)</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th> </tr> <tr> <td>PP-OCRv5_server_rec</td> <td>86.38</td> <td>8.46 / 2.36</td> <td>31.21 / 31.21</td> <td>81</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv5_server_rec.yaml">PP-OCRv5_server_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_rec_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv5_mobile_rec</td> <td>81.29</td> <td>5.43 / 1.46</td> <td>21.20 / 5.32</td> <td>16</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv5_mobile_rec.yaml">PP-OCRv5_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_rec_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv4_server_rec_doc</td> <td>86.58</td> <td>8.69 / 2.78</td> <td>37.93 / 37.93</td> <td>182</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv4_server_rec_doc.yaml">PP-OCRv4_server_rec_doc.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ PP-OCRv4_server_rec_doc_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>PP-OCRv4_mobile_rec</td> <td>78.74</td> <td>5.26 / 1.12</td> <td>17.48 / 3.61</td> <td>10.5</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv4_mobile_rec.yaml">PP-OCRv4_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv4_server_rec</td> <td>85.19</td> <td>8.75 / 2.49</td> <td>36.93 / 36.93</td> <td>173</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv4_server_rec.yaml">PP-OCRv4_server_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-OCRv3_mobile_rec</td> <td>72.96</td> <td>3.89 / 1.16</td> <td>8.72 / 3.56</td> <td>10.3</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/PP-OCRv3_mobile_rec.yaml">PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> </table> <p><b>注：以上精度指标的评估集是 PaddleOCR 自建的中文数据集，覆盖街景、网图、文档、手写多个场景，其中文本识别包含 8367 张图片。</b></p> <table> <tr> <th>模型</th> <th>识别 Avg Accuracy(%)</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th> </tr> <tr> <td>ch_SVTRv2_rec</td> <td>68.81</td> <td>10.38 / 8.31</td> <td>66.52 / 30.83</td> <td>80.5</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/ch_SVTRv2_rec.yaml">ch_SVTRv2_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_SVTRv2_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_SVTRv2_rec_pretrained.pdparams">训练模型</a></td> </tr> </table> <p><b>注：以上精度指标的评估集是 <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务</a>A榜。 </b></p> <table> <tr> <th>模型</th> <th>识别 Avg Accuracy(%)</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th> </tr> <tr> <td>ch_RepSVTR_rec</td> <td>65.07</td> <td>6.29 / 1.57</td> <td>20.64 / 5.40</td> <td>48.8</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/ch_RepSVTR_rec.yaml">ch_RepSVTR_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ch_RepSVTR_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ch_RepSVTR_rec_pretrained.pdparams">训练模型</a></td> </tr> </table> <p><b>注：以上精度指标的评估集是 <a href="https://aistudio.baidu.com/competition/detail/1131/0/introduction">PaddleOCR算法模型挑战赛 - 赛题一：OCR端到端识别任务</a>B榜。 </b></p>

<b>英文识别模型</b>

<b>多语言识别模型</b>

<table> <tr> <th>模型</th> <th>识别 Avg Accuracy(%)</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th> </tr> <tr> <td>korean_PP-OCRv3_mobile_rec</td> <td>60.21</td> <td>3.73 / 0.98</td> <td>8.76 / 2.91</td> <td>9.6</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/korean_PP-OCRv3_mobile_rec.yaml">korean_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ korean_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>japan_PP-OCRv3_mobile_rec</td> <td>45.69</td> <td>3.86 / 1.01</td> <td>8.62 / 2.92</td> <td>9.8</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/japan_PP-OCRv3_mobile_rec.yaml">japan_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ japan_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>chinese_cht_PP-OCRv3_mobile_rec</td> <td>82.06</td> <td>3.90 / 1.16</td> <td>9.24 / 3.18</td> <td>10.8</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/chinese_cht_PP-OCRv3_mobile_rec.yaml">chinese_cht_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ chinese_cht_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>te_PP-OCRv3_mobile_rec</td> <td>95.88</td> <td>3.59 / 0.81</td> <td>8.28 / 6.21</td> <td>8.7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/te_PP-OCRv3_mobile_rec.yaml">te_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ te_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>ka_PP-OCRv3_mobile_rec</td> <td>96.96</td> <td>3.49 / 0.89</td> <td>8.63 / 2.77</td> <td>17.4</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/ka_PP-OCRv3_mobile_rec.yaml">ka_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ ka_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>ta_PP-OCRv3_mobile_rec</td> <td>76.83</td> <td>3.49 / 0.86</td> <td>8.35 / 3.41</td> <td>8.7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/ta_PP-OCRv3_mobile_rec.yaml">ta_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ ta_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>latin_PP-OCRv3_mobile_rec</td> <td>76.93</td> <td>3.53 / 0.78</td> <td>8.50 / 6.83</td> <td>8.7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/latin_PP-OCRv3_mobile_rec.yaml">latin_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ latin_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>arabic_PP-OCRv3_mobile_rec</td> <td>73.55</td> <td>3.60 / 0.83</td> <td>8.44 / 4.69</td> <td>17.3</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/arabic_PP-OCRv3_mobile_rec.yaml">arabic_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ arabic_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>cyrillic_PP-OCRv3_mobile_rec</td> <td>94.28</td> <td>3.56 / 0.79</td> <td>8.22 / 2.76</td> <td>8.7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/cyrillic_PP-OCRv3_mobile_rec.yaml">cyrillic_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ cyrillic_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> <tr> <td>devanagari_PP-OCRv3_mobile_rec</td> <td>96.44</td> <td>3.60 / 0.78</td> <td>6.95 / 2.87</td> <td>8.7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/text_recognition/devanagari_PP-OCRv3_mobile_rec.yaml">devanagari_PP-OCRv3_mobile_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/\ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模型</a></td> </tr> </table> <p><b>注：以上精度指标的评估集是 PaddleOCR 自建的多语种数据集。</b></p>

公式识别模块

<table> <tr> <th>模型</th> <th>En-BLEU(%)</th> <th>Zh-BLEU(%)</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th> </tr> <tr> <td>UniMERNet</td> <td>85.91</td> <td>43.50</td> <td>1311.84 / 1311.84</td> <td>- / 8288.07</td> <td>1530</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/UniMERNet.yaml">UniMERNet.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UniMERNet_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UniMERNet_pretrained.pdparams">训练模型</a></td> </tr> <td>PP-FormulaNet-S</td> <td>87.00</td> <td>45.71</td> <td>182.25 / 182.25</td> <td>- / 254.39</td> <td>224</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml">PP-FormulaNet-S.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-S_pretrained.pdparams">训练模型</a></td> </tr> <td>PP-FormulaNet-L</td> <td>90.36</td> <td>45.78</td> <td>1482.03 / 1482.03</td> <td>- / 3131.54</td> <td>695</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet-L.yaml">PP-FormulaNet-L.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet-L_pretrained.pdparams">训练模型</a></td> </tr> <td>PP-FormulaNet_plus-S</td> <td>88.71</td> <td>53.32</td> <td>179.20 / 179.20</td> <td>- / 260.99</td> <td>248</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-S.yaml">PP-FormulaNet_plus-S.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-S_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-FormulaNet_plus-M</td> <td>91.45</td> <td>89.76</td> <td>1040.27 / 1040.27</td> <td>- / 1615.80</td> <td>592</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-M.yaml">PP-FormulaNet_plus-M.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-M_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-FormulaNet_plus-L</td> <td>92.22</td> <td>90.64</td> <td>1476.07 / 1476.07</td> <td>- / 3125.58</td> <td>698</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/PP-FormulaNet_plus-L.yaml">PP-FormulaNet_plus-L.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-FormulaNet_plus-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-FormulaNet_plus-L_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>LaTeX_OCR_rec</td> <td>74.55</td> <td>39.96</td> <td>1088.89 / 1088.89</td> <td>- / -</td> <td>99</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/formula_recognition/LaTeX_OCR_rec.yaml">LaTeX_OCR_rec.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/LaTeX_OCR_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/LaTeX_OCR_rec_pretrained.pdparams">训练模型</a></td> </tr> </table> <b>注：以上精度指标测量自 PaddleX 内部自建公式识别测试集。LaTeX_OCR_rec在LaTeX-OCR公式识别测试集的BLEU score为 0.8821。</b>

表格结构识别模块

<table> <tr> <th>模型</th> <th>精度（%）</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th> </tr> <tr> <td>SLANet</td> <td>59.52</td> <td>23.96 / 21.75</td> <td>- / 43.12</td> <td>6.9</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_structure_recognition/SLANet.yaml">SLANet.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SLANet_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANet_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>SLANet_plus</td> <td>63.69</td> <td>23.43 / 22.16</td> <td>- / 41.80</td> <td>6.9</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_structure_recognition/SLANet_plus.yaml">SLANet_plus.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SLANet_plus_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANet_plus_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>SLANeXt_wired</td> <td rowspan="2">69.65</td> <td rowspan="2">85.92 / 85.92</td> <td rowspan="2">- / 501.66</td> <td rowspan="2">351</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_structure_recognition/SLANeXt_wired.yaml">SLANeXt_wired.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SLANeXt_wired_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANeXt_wired_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>SLANeXt_wireless</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_structure_recognition/SLANeXt_wireless.yaml">SLANeXt_wireless.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/SLANeXt_wireless_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANeXt_wireless_pretrained.pdparams">训练模型</a></td> </tr> </table> <b>注：以上精度指标测量自 PaddleX 内部自建高难度中文表格识别数据集。</b>

表格单元格检测模块

<table> <tr> <th>模型</th> <th>mAP(%)</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th> </tr> <tr> <td>RT-DETR-L_wired_table_cell_det</td> <td rowspan="2">82.7</td> <td rowspan="2">33.47 / 27.02</td> <td rowspan="2">402.55 / 256.56</td> <td rowspan="2">124</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_cells_detection/RT-DETR-L_wired_table_cell_det.yaml">RT-DETR-L_wired_table_cell_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wired_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wired_table_cell_det_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>RT-DETR-L_wireless_table_cell_det</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_cells_detection/RT-DETR-L_wireless_table_cell_det.yaml">RT-DETR-L_wireless_table_cell_det.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_wireless_table_cell_det_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_wireless_table_cell_det_pretrained.pdparams">训练模型</a></td> </tr> </table> <p><b>注：以上精度指标测量自 PaddleX 内部自建表格单元格检测数据集。</b></p>

表格分类模块

<table> <tr> <th>模型</th> <th>Top1 Acc(%)</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml文件</th> <th>模型下载链接</th> </tr> <tr> <td>PP-LCNet_x1_0_table_cls</td> <td>94.2</td> <td>2.62 / 0.60</td> <td>3.17 / 1.14</td> <td>6.6</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/table_classification/PP-LCNet_x1_0_table_cls.yaml">PP-LCNet_x1_0_table_cls.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_table_cls_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_table_cls_pretrained.pdparams">训练模型</a></td> </tr> </table> <p><b>注：以上精度指标测量自 PaddleX 内部自建表格分类数据集。</b></p>

文本图像矫正模块

<table> <thead> <tr> <th>模型名称</th> <th>CER</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml 文件</th> <th>模型下载链接</th></tr> </thead> <tbody> <tr> <td>UVDoc</td> <td>0.179</td> <td>19.05 / 19.05</td> <td>- / 869.82</td> <td>30.3</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/image_unwarping/UVDoc.yaml">UVDoc.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/UVDoc_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UVDoc_pretrained.pdparams">训练模型</a></td></tr> </tbody> </table> <b>注：以上精度指标测量自 </b><b>PaddleX自建的图像矫正数据集</b><b>。</b>

版面区域检测模块

<b>版面检测模型，包含20个常见的类别：文档标题、段落标题、文本、页码、摘要、目录、参考文献、脚注、页眉、页脚、算法、公式、公式编号、图像、表格、图和表标题（图标题、表格标题和图表标题）、印章、图表、侧栏文本和参考文献内容</b>

<b>注：以上精度指标的评估集是自建的版面区域检测数据集，包含中英文论文、杂志、报纸、研报、PPT、试卷、课本等 1300 张文档类型图片。</b>

<b>文档图像版面子模块检测，包含1个版面区域类别，能检测多栏的报纸、杂志的每个子文章的文本区域:</b>

<b>注：以上精度指标的评估集是自建的版面子区域检测数据集，包含中英文论文、杂志、报纸、研报、PPT、试卷、课本等 1000 张文档类型图片。</b>

<b>版面检测模型，包含23个常见的类别：文档标题、段落标题、文本、页码、摘要、目录、参考文献、脚注、页眉、页脚、算法、公式、公式编号、图像、图表标题、表格、表格标题、印章、图表标题、图表、页眉图像、页脚图像、侧栏文本</b>

<table> <thead> <tr> <th>模型</th> <th>mAP(0.5)（%）</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml文件</th> <th>模型下载链接</th> </tr> </thead> <tbody> <tr> <td>PP-DocLayout-L</td> <td>90.4</td> <td>33.59 / 33.59</td> <td>503.01 / 251.08</td> <td>123.76 </td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocLayout-L.yaml">PP-DocLayout-L.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-L_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-DocLayout-M</td> <td>75.2</td> <td>13.03 / 4.72</td> <td>43.39 / 24.44</td> <td>22.578</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocLayout-M.yaml">PP-DocLayout-M.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-M_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PP-DocLayout-S</td> <td>70.9</td> <td>11.54 / 3.86</td> <td>18.53 / 6.29</td> <td>4.834</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PP-DocLayout-S.yaml">PP-DocLayout-S.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocLayout-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-DocLayout-S_pretrained.pdparams">训练模型</a></td> </tr> </tbody> </table>

<b>注：以上精度指标的评估集是自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 500 张文档类型图片。</b>

<b>表格版面检测模型</b>

<b>3类版面检测模型，包含表格、图像、印章</b>

<table> <thead> <tr> <th>模型</th> <th>mAP(0.5)（%）</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml文件</th> <th>模型下载链接</th> </tr> </thead> <tbody> <tr> <td>PicoDet-S_layout_3cls</td> <td>88.2</td> <td>8.43 / 3.44</td> <td>17.60 / 6.51</td> <td>4.8</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PicoDet-S_layout_3cls.yaml">PicoDet-S_layout_3cls.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-S_layout_3cls_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_3cls_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PicoDet-L_layout_3cls</td> <td>89.0</td> <td>12.80 / 9.57</td> <td>45.04 / 23.86</td> <td>22.6</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PicoDet-L_layout_3cls.yaml">PicoDet-L_layout_3cls.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-L_layout_3cls_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_3cls_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>RT-DETR-H_layout_3cls</td> <td>95.8</td> <td>114.80 / 25.65</td> <td>924.38 / 924.38</td> <td>470.1</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/RT-DETR-H_layout_3cls.yaml">RT-DETR-H_layout_3cls.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-H_layout_3cls_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_3cls_pretrained.pdparams">训练模型</a></td> </tr> </tbody></table> <b>注：以上精度指标的评估集是 PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 1154 张文档类型图片。</b>

<b>5类英文文档区域检测模型，包含文字、标题、表格、图片以及列表</b>

<b>17类区域检测模型，包含17个版面常见类别，分别是：段落标题、图片、文本、数字、摘要、内容、图表标题、公式、表格、表格标题、参考文献、文档标题、脚注、页眉、算法、页脚、印章</b>

<table> <thead> <tr> <th>模型</th> <th>mAP(0.5)（%）</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml文件</th> <th>模型下载链接</th> </tr> </thead> <tbody> <tr> <td>PicoDet-S_layout_17cls</td> <td>87.4</td> <td>8.80 / 3.62</td> <td>17.51 / 6.35</td> <td>4.8</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PicoDet-S_layout_17cls.yaml">PicoDet-S_layout_17cls.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-S_layout_17cls_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_layout_17cls_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>PicoDet-L_layout_17cls</td> <td>89.0</td> <td>12.60 / 10.27</td> <td>43.70 / 24.42</td> <td>22.6</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/PicoDet-L_layout_17cls.yaml">PicoDet-L_layout_17cls.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-L_layout_17cls_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_layout_17cls_pretrained.pdparams">训练模型</a></td> </tr> <tr> <td>RT-DETR-H_layout_17cls</td> <td>98.3</td> <td>115.29 / 101.18</td> <td>964.75 / 964.75</td> <td>470.2</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/layout_detection/RT-DETR-H_layout_17cls.yaml">RT-DETR-H_layout_17cls.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-H_layout_17cls_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_layout_17cls_pretrained.pdparams">训练模型</a></td> </tr> </tbody> </table> <b>注：以上精度指标的评估集是 PaddleOCR 自建的版面区域检测数据集，包含中英文论文、杂志和研报等常见的 892 张文档类型图片。</b>

文档图像方向分类模块

<table> <thead> <tr> <th>模型</th> <th>Top-1 Acc（%）</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml文件</th> <th>模型下载链接</th> </tr> </thead> <tbody> <tr> <td>PP-LCNet_x1_0_doc_ori</td> <td>99.06</td> <td>2.62 / 0.59</td> <td>3.24 / 1.19</td> <td>7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/doc_text_orientation/PP-LCNet_x1_0_doc_ori.yaml">PP-LCNet_x1_0_doc_ori.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_doc_ori_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_doc_ori_pretrained.pdparams">训练模型</a></td> </tr> </tbody> </table> <b>注：以上精度指标的评估集是自建的数据集，覆盖证件和文档等多个场景，包含 1000 张图片。</b>

文本行方向分类模块

<table> <thead> <tr> <th>模型</th> <th>Top-1 Acc（%）</th> <th>GPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>CPU推理耗时（ms） [常规模式 / 高性能模式]</th> <th>模型存储大小（MB）</th> <th>yaml文件</th> <th>模型下载链接</th> </tr> </thead> <tbody> <tr> <td>PP-LCNet_x1_0_doc_ori</td> <td>99.06</td> <td>2.62 / 0.59</td> <td>3.24 / 1.19</td> <td>7</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/textline_orientation/PP-LCNet_x0_25_textline_ori.yaml">PP-LCNet_x0_25_textline_ori.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_doc_ori_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_doc_ori_pretrained.pdparams">训练模型</a></td> </tr> </tbody> </table>

<b>注：以上精度指标的评估集是自建的数据集，覆盖证件和文档等多个场景，包含 1000 张图片。</b>

文档类视觉语言模型模块

<table> <tr> <th>模型</th> <th>模型参数尺寸（B）</th> <th>模型存储大小（GB）</th> <th>yaml文件</th> <th>模型下载链接</th> </tr> <tr> <td>PP-DocBee-2B</td> <td>2</td> <td>4.2</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/doc_vlm/PP-DocBee-2B.yaml">PP-DocBee-2B.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee-2B_infer.tar">推理模型</a></td> </tr> <tr> <td>PP-DocBee-7B</td> <td>7</td> <td>15.8</td> <td><a href="https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/modules/doc_vlm/PP-DocBee-7B.yaml">PP-DocBee-7B.yaml</a></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee-7B_infer.tar">推理模型</a></td> </tr> <tr> <td>PP-DocBee2-3B</td> <td>3</td> <td>7.6</td> <td></td> <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-DocBee2-3B_infer.tar">推理模型</a></td> </tr> </table>