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# PaddleX模型列表（燧原 GCU）

PaddleX 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，如您更考虑模型存储大小，请选择存储大小较小的模型。

## [图像分类模块](../module_usage/tutorials/cv_modules/image_classification.md)
<table>
<thead>
<tr>
<th>模型名称</th>
<th>Top1 Acc（%）</th>
<th>模型存储大小（M）</th>
<th>模型下载链接</th></tr>
</thead>
<tbody>
<tr>
<td>ConvNeXt_base_224</td>
<td>83.84</td>
<td>313.9 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ConvNeXt_base_224_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_base_224_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ConvNeXt_base_384</td>
<td>84.90</td>
<td>313.9 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ConvNeXt_base_384_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_base_384_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ConvNeXt_large_224</td>
<td>84.26</td>
<td>700.7 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ConvNeXt_large_224_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_large_224_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ConvNeXt_large_384</td>
<td>85.27</td>
<td>700.7 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ConvNeXt_large_384_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_large_384_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ConvNeXt_small</td>
<td>83.13</td>
<td>178.0 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ConvNeXt_small_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_small_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ConvNeXt_tiny</td>
<td>82.03</td>
<td>101.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ConvNeXt_tiny_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_tiny_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>FasterNet-L</td>
<td>83.5</td>
<td>357.1 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/FasterNet-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-L_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>FasterNet-M</td>
<td>82.9</td>
<td>204.6 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/FasterNet-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-M_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>FasterNet-S</td>
<td>81.3</td>
<td>119.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/FasterNet-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-S_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>FasterNet-T0</td>
<td>71.8</td>
<td>15.1 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/FasterNet-T0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-T0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>FasterNet-T1</td>
<td>76.2</td>
<td>29.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/FasterNet-T1_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-T1_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>FasterNet-T2</td>
<td>78.8</td>
<td>57.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/FasterNet-T2_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-T2_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV1_x0_25</td>
<td>51.4</td>
<td>1.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV1_x0_25_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV1_x0_25_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV1_x0_5</td>
<td>63.5</td>
<td>4.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV1_x0_5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV1_x0_5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV1_x0_75</td>
<td>68.8</td>
<td>9.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV1_x0_75_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV1_x0_75_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV1_x1_0</td>
<td>71.0</td>
<td>15.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV1_x1_0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV1_x1_0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV2_x0_25</td>
<td>53.2</td>
<td>5.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV2_x0_25_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x0_25_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV2_x0_5</td>
<td>65.0</td>
<td>7.1 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV2_x0_5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x0_5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV2_x1_0</td>
<td>72.2</td>
<td>12.6 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV2_x1_0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x1_0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV2_x1_5</td>
<td>74.1</td>
<td>25.0 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV2_x1_5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x1_5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV2_x2_0</td>
<td>75.2</td>
<td>41.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV2_x2_0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x2_0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_large_x0_35</td>
<td>64.3</td>
<td>7.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_large_x0_35_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x0_35_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_large_x0_5</td>
<td>69.2</td>
<td>9.6 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_large_x0_5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x0_5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_large_x0_75</td>
<td>73.1</td>
<td>14.0 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_large_x0_75_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x0_75_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_large_x1_0</td>
<td>75.3</td>
<td>19.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_large_x1_0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x1_0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_large_x1_25</td>
<td>76.4</td>
<td>26.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_large_x1_25_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x1_25_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_small_x0_35</td>
<td>53.0</td>
<td>6.0 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_small_x0_35_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x0_35_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_small_x0_5</td>
<td>59.2</td>
<td>6.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_small_x0_5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x0_5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_small_x0_75</td>
<td>66.0</td>
<td>8.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_small_x0_75_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x0_75_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_small_x1_0</td>
<td>68.2</td>
<td>10.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_small_x1_0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x1_0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV3_small_x1_25</td>
<td>70.7</td>
<td>13.0 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV3_small_x1_25_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x1_25_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV4_conv_large</td>
<td>83.4</td>
<td>125.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV4_conv_large_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_conv_large_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV4_conv_medium</td>
<td>80.9</td>
<td>37.6 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV4_conv_medium_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_conv_medium_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>MobileNetV4_conv_small</td>
<td>74.4</td>
<td>14.7 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/MobileNetV4_conv_small_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_conv_small_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNet_base</td>
<td>85.0</td>
<td>249.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNet_base_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNet_base_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNet_small</td>
<td>81.51</td>
<td>86.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNet_small_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNet_small_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNet_tiny</td>
<td>79.83</td>
<td>52.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNet_tiny_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNet_tiny_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNetV2-B0</td>
<td>77.77</td>
<td>21.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNetV2-B0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNetV2-B1</td>
<td>78.90</td>
<td>22.6 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNetV2-B1_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B1_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNetV2-B2</td>
<td>81.57</td>
<td>39.9 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNetV2-B2_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B2_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNetV2-B3</td>
<td>82.92</td>
<td>57.9 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNetV2-B3_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B3_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNetV2-B4</td>
<td>83.68</td>
<td>70.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNetV2-B4_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B4_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNetV2-B5</td>
<td>84.75</td>
<td>140.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNetV2-B5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-HGNetV2-B6</td>
<td>86.20</td>
<td>268.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-HGNetV2-B6_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B6_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNet_x0_25</td>
<td>51.86</td>
<td>5.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x0_25_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_25_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNet_x0_35</td>
<td>58.10</td>
<td>5.9 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x0_35_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_35_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNet_x0_5</td>
<td>63.14</td>
<td>6.7 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x0_5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNet_x0_75</td>
<td>68.18</td>
<td>8.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x0_75_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_75_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNet_x1_0</td>
<td>71.32</td>
<td>10.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNet_x1_5</td>
<td>73.71</td>
<td>16.0 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNet_x2_0</td>
<td>75.18</td>
<td>23.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x2_0_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x2_0_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNet_x2_5</td>
<td>76.60</td>
<td>32.1 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x2_5_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x2_5_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNetV2_base</td>
<td>77.04</td>
<td>23.7 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNetV2_base_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNetV2_base_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNetV2_large</td>
<td>78.51</td>
<td>37.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNetV2_large_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNetV2_large_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-LCNetV2_small</td>
<td>73.96</td>
<td>14.6 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNetV2_small_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNetV2_small_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet18_vd</td>
<td>72.3</td>
<td>41.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet18_vd_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet18_vd_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet18</td>
<td>71.0</td>
<td>41.5 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet18_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet18_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet34_vd</td>
<td>76.0</td>
<td>77.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet34_vd_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet34_vd_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet34</td>
<td>74.6</td>
<td>77.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet34_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet34_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet50_vd</td>
<td>79.1</td>
<td>90.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet50_vd_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet50_vd_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet50</td>
<td>76.5</td>
<td>90.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet50_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet50_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet101_vd</td>
<td>80.2</td>
<td>158.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet101_vd_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet101_vd_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet101</td>
<td>77.6</td>
<td>158.7 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet101_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet101_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet152_vd</td>
<td>80.6</td>
<td>214.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet152_vd_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet152_vd_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet152</td>
<td>78.3</td>
<td>214.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet152_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet152_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>ResNet200_vd</td>
<td>80.7</td>
<td>266.0 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/ResNet200_vd_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet200_vd_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>StarNet-S1</td>
<td>73.5</td>
<td>11.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/StarNet-S1_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/StarNet-S1_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>StarNet-S2</td>
<td>74.7</td>
<td>14.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/StarNet-S2_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/StarNet-S2_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>StarNet-S3</td>
<td>77.4</td>
<td>22.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/StarNet-S3_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/StarNet-S3_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>StarNet-S4</td>
<td>78.8</td>
<td>28.9 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/StarNet-S4_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/StarNet-S4_pretrained.pdparams">训练模型</a></td></tr>
</tbody>
</table>
<b>注：以上精度指标为</b>[ImageNet-1k](https://www.image-net.org/index.php)<b>验证集 Top1 Acc。</b>

## [目标检测模块](../module_usage/tutorials/cv_modules/object_detection.md)
<table>
<thead>
<tr>
<th>模型名称</th>
<th>mAP（%）</th>
<th>模型存储大小（M）</th>
<th>模型下载链接</th></tr>
</thead>
<tbody>
<tr>
<td>FCOS-ResNet50</td>
<td>39.6</td>
<td>124.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/FCOS-ResNet50_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FCOS-ResNet50_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PicoDet-L</td>
<td>42.5</td>
<td>20.9 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PicoDet-M</td>
<td>37.4</td>
<td>16.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-M_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PicoDet-S</td>
<td>29.0</td>
<td>4.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PicoDet-XS</td>
<td>26.2</td>
<td>5.7M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PicoDet-XS_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-XS_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-YOLOE_plus-L</td>
<td>52.8</td>
<td>185.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE_plus-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-L_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-YOLOE_plus-M</td>
<td>49.7</td>
<td>83.2 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE_plus-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-M_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-YOLOE_plus-S</td>
<td>43.6</td>
<td>28.3 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE_plus-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-S_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-YOLOE_plus-X</td>
<td>54.7</td>
<td>349.4 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE_plus-X_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-X_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>RT-DETR-H</td>
<td>56.3</td>
<td>435.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-H_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-H_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>RT-DETR-L</td>
<td>53.0</td>
<td>113.7 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-L_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>RT-DETR-R18</td>
<td>46.5</td>
<td>70.7 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-R18_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-R18_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>RT-DETR-R50</td>
<td>53.1</td>
<td>149.1 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-R50_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-R50_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>RT-DETR-X</td>
<td>54.8</td>
<td>232.9 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/RT-DETR-X_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/RT-DETR-X_pretrained.pdparams">训练模型</a></td></tr>
</tbody>
</table>
<b>注：以上精度指标为</b>[COCO2017](https://cocodataset.org/#home)<b>验证集 mAP(0.5:0.95)。</b>

## [行人检测模块](../module_usage/tutorials/cv_modules/human_detection.md)
<table>
<thead>
<tr>
<th>模型名称</th>
<th>mAP（%）</th>
<th>模型存储大小</th>
<th>模型下载链接</th></tr>
</thead>
<tbody>
<tr>
<td>PP-YOLOE-L_human</td>
<td>48.0</td>
<td>196.1 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE-L_human_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE-L_human_pretrained.pdparams">训练模型</a></td></tr>
<tr>
<td>PP-YOLOE-S_human</td>
<td>42.5</td>
<td>28.8 M</td>
<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-YOLOE-S_human_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE-S_human_pretrained.pdparams">训练模型</a></td></tr>
</tbody>
</table>
<b>注：以上精度指标为</b>[CrowdHuman](https://bj.bcebos.com/v1/paddledet/data/crowdhuman.zip)<b>验证集 mAP(0.5:0.95)。</b>

## [文本检测模块](../module_usage/tutorials/ocr_modules/text_detection.md)
<table>
<thead>
<tr>
<th>模型名称</th>
<th>检测Hmean（%）</th>
<th>模型存储大小（M）</th>
<th>模型下载链接</th></tr>
</thead>
<tbody>
<tr>
<td>PP-OCRv4_mobile_det</td>
<td>77.79</td>
<td>4.2 M</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-OCRv4_server_det</td>
<td>82.69</td>
<td>100.1 M</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>
</tbody>
</table>
<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，其中检测包含 500 张图片。</b>

## [文本识别模块](../module_usage/tutorials/ocr_modules/text_recognition.md)
<table>
<thead>
<tr>
<th>模型名称</th>
<th>识别Avg Accuracy(%)</th>
<th>模型存储大小（M）</th>
<th>模型下载链接</th></tr>
</thead>
<tbody>
<tr>
<td>PP-OCRv4_mobile_rec</td>
<td>78.20</td>
<td>10.6 M</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>79.20</td>
<td>71.2 M</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>
</tbody>
</table>
<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 1.1w 张图片。</b>
