[GCU] Update model list (#2737)

* [GCU] Update model list

* [GCU] Update model list

docs/support_list/model_list_gcu.en.md

@@ -6,7 +6,7 @@ comments: true
 
 PaddleX incorporates multiple pipelines, each containing several modules, and each module encompasses various models. You can select the appropriate models based on the benchmark data below. If you prioritize model accuracy, choose models with higher accuracy. If you prioritize model size, select models with smaller storage requirements.
 
-## Image Classification Module
+## [Image Classification Module](../module_usage/tutorials/cv_modules/image_classification.en.md)
 <table>
 <thead>
 <tr>
@@ -17,15 +17,360 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </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/paddle3.0b2/ConvNeXt_base_224_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_base_224_pretrained.pdparams">Trained Model</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/paddle3.0b2/ConvNeXt_base_384_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_base_384_pretrained.pdparams">Trained Model</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/paddle3.0b2/ConvNeXt_large_224_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_large_224_pretrained.pdparams">Trained Model</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/paddle3.0b2/ConvNeXt_large_384_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_large_384_pretrained.pdparams">Trained Model</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/paddle3.0b2/ConvNeXt_small_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_small_pretrained.pdparams">Trained Model</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/paddle3.0b2/ConvNeXt_tiny_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ConvNeXt_tiny_pretrained.pdparams">Trained Model</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/paddle3.0b2/FasterNet-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-L_pretrained.pdparams">Trained Model</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/paddle3.0b2/FasterNet-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-M_pretrained.pdparams">Trained Model</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/paddle3.0b2/FasterNet-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-S_pretrained.pdparams">Trained Model</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/paddle3.0b2/FasterNet-T0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-T0_pretrained.pdparams">Trained Model</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/paddle3.0b2/FasterNet-T1_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-T1_pretrained.pdparams">Trained Model</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/paddle3.0b2/FasterNet-T2_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FasterNet-T2_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV1_x0_25_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV1_x0_25_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV1_x0_5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV1_x0_5_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV1_x0_75_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV1_x0_75_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV1_x1_0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV1_x1_0_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV2_x0_25_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x0_25_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV2_x0_5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x0_5_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV2_x1_0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x1_0_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV2_x1_5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x1_5_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV2_x2_0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV2_x2_0_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_large_x0_35_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x0_35_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_large_x0_5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x0_5_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_large_x0_75_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x0_75_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_large_x1_0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x1_0_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_large_x1_25_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_large_x1_25_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_small_x0_35_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x0_35_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_small_x0_5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x0_5_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_small_x0_75_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x0_75_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_small_x1_0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x1_0_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV3_small_x1_25_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV3_small_x1_25_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV4_conv_large_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_conv_large_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV4_conv_medium_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_conv_medium_pretrained.pdparams">Trained Model</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/paddle3.0b2/MobileNetV4_conv_small_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_conv_small_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNet_base_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNet_base_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNet_small_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNet_small_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNet_tiny_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNet_tiny_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNetV2-B0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B0_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNetV2-B1_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B1_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNetV2-B2_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B2_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNetV2-B3_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B3_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNetV2-B4_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B4_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNetV2-B5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B5_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-HGNetV2-B6_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B6_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNet_x0_25_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_25_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNet_x0_35_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_35_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNet_x0_5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_5_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNet_x0_75_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x0_75_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNet_x1_0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNet_x1_5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_5_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNet_x2_0_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x2_0_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNet_x2_5_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x2_5_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNetV2_base_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNetV2_base_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNetV2_large_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNetV2_large_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-LCNetV2_small_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNetV2_small_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet18_vd_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet18_vd_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet18_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet18_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet34_vd_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet34_vd_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet34_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet34_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet50_vd_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet50_vd_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
 <td>ResNet50</td>
-<td>76.96</td>
+<td>76.5</td>
 <td>90.8 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ResNet50_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet50_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet101_vd_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet101_vd_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet101_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet101_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet152_vd_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet152_vd_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet152_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet152_pretrained.pdparams">Trained Model</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/paddle3.0b2/ResNet200_vd_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet200_vd_pretrained.pdparams">Trained Model</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/paddle3.0b2/StarNet-S1_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/StarNet-S1_pretrained.pdparams">Trained Model</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/paddle3.0b2/StarNet-S2_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/StarNet-S2_pretrained.pdparams">Trained Model</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/paddle3.0b2/StarNet-S3_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/StarNet-S3_pretrained.pdparams">Trained Model</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/paddle3.0b2/StarNet-S4_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/StarNet-S4_pretrained.pdparams">Trained Model</a></td></tr>
 </tbody>
 </table>
 <b>Note: The above accuracy metrics refer to Top-1 Accuracy on the [ImageNet-1k](https://www.image-net.org/index.php) validation set.</b>
 
-## Object Detection Module
+## [Object Detection Module](../module_usage/tutorials/cv_modules/object_detection.en.md)
 <table>
 <thead>
 <tr>
@@ -36,6 +381,31 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </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/paddle3.0b2/FCOS-ResNet50_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/FCOS-ResNet50_pretrained.pdparams">Trained Model</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/paddle3.0b2/PicoDet-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-L_pretrained.pdparams">Trained Model</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/paddle3.0b2/PicoDet-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-M_pretrained.pdparams">Trained Model</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/paddle3.0b2/PicoDet-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-S_pretrained.pdparams">Trained Model</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/paddle3.0b2/PicoDet-XS_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet-XS_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
 <td>PP-YOLOE_plus-L</td>
 <td>52.8</td>
 <td>185.3 M</td>
@@ -84,7 +454,31 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are for</b> [COCO2017](https://cocodataset.org/#home) <b>validation set mAP(0.5:0.95).</b>
 
-## Text Detection Module
+## [Pedestrian Detection Module](../module_usage/tutorials/cv_modules/human_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mAP（%）</th>
+<th>Model Size (M)</th>
+<th>Model Download Link</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/paddle3.0b2/PP-YOLOE-L_human_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE-L_human_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-YOLOE-S_human_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE-S_human_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are mAP(0.5:0.95) on the [CrowdHuman](https://bj.bcebos.com/v1/paddledet/data/crowdhuman.zip) validation set.</b>
+
+## [Text Detection Module](../module_usage/tutorials/ocr_modules/text_detection.en.md)
 <table>
 <thead>
 <tr>
@@ -108,7 +502,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are evaluated on PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, and handwritten scenarios, with 500 images for detection.</b>
 
-## Text Recognition Module
+## [Text Recognition Module](../module_usage/tutorials/ocr_modules/text_recognition.en.md)
 <table>
 <thead>
 <tr>

docs/support_list/model_list_gcu.md

@@ -6,36 +6,406 @@ comments: true
 
 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
+## [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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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>76.96</td>
+<td>76.5</td>
 <td>90.8 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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
+## [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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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/paddle3.0b2/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>
@@ -84,13 +454,37 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 </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>
 
-## 文本检测模块
+## [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/paddle3.0b2/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/paddle3.0b2/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>模型存储大小（M）</th>
 <th>模型下载链接</th></tr>
 </thead>
 <tbody>
@@ -102,19 +496,19 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <tr>
 <td>PP-OCRv4_server_det</td>
 <td>82.69</td>
-<td>100.1M</td>
+<td>100.1 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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
+## [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>模型存储大小（M）</th>
 <th>模型下载链接</th></tr>
 </thead>
 <tbody>