update model list (#2940)

docs/support_list/model_list_dcu.en.md

@@ -45,6 +45,142 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are Top-1 Accuracy on the [ImageNet-1k](https://www.image-net.org/index.php) validation set.</b>
 
+## [Image Multi-label Classification Module](../module_usage/tutorials/cv_modules/image_multilabel_classification.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mAP (%)</th>
+<th>Model Storage Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>CLIP_vit_base_patch16_448_ML</td>
+<td>89.15</td>
+<td>325.6 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/CLIP_vit_base_patch16_448_ML_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/CLIP_vit_base_patch16_448_ML_pretrained.pdparams">Training Model</a></td></tr>
+<tr>
+<td>PP-HGNetV2-B0_ML</td>
+<td>80.98</td>
+<td>39.6 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-HGNetV2-B0_ML_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B0_ML_pretrained.pdparams">Training Model</a></td>
+<tr>
+<td>PP-HGNetV2-B4_ML</td>
+<td>87.96</td>
+<td>88.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-HGNetV2-B4_ML_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B4_ML_pretrained.pdparams">Training Model</a></td></tr>
+<tr>
+<td>PP-HGNetV2-B6_ML</td>
+<td>91.06</td>
+<td>286.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-HGNetV2-B6_ML_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B6_ML_pretrained.pdparams">Training Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are for the multi-label classification task mAP of [COCO2017](https://cocodataset.org/#home).</b>
+
+## [Image Feature Module](../module_usage/tutorials/cv_modules/image_feature.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>recall@1（%）</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-ShiTuV2_rec_CLIP_vit_base</td>
+<td>88.69</td>
+<td>306.6 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-ShiTuV2_rec_CLIP_vit_base_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-ShiTuV2_rec_CLIP_vit_base_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are for AliProducts recall@1。</b>
+
+## Object Detection Module
+<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>PicoDet-L</td>
+<td>42.6</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>37.5</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>29.1</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>52.9</td>
+<td>185.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-L_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>PP-YOLOE_plus-M</td>
+<td>49.8</td>
+<td>83.2 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-M_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>PP-YOLOE_plus-S</td>
+<td>43.7</td>
+<td>28.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-S_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-YOLOE_plus-X_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-X_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are mAP(0.5:0.95) on the [COCO2017](https://cocodataset.org/#home) validation set.</b>
+
+## [Small Object Detection Module](../module_usage/tutorials/cv_modules/small_object_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mAP（%）</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-YOLOE_plus_SOD-S</td>
+<td>25.1</td>
+<td>77.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus_SOD-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-S_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>PP-YOLOE_plus_SOD-L</td>
+<td>31.9</td>
+<td>325.0 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus_SOD-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-L_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>PP-YOLOE_plus_SOD-largesize-L</td>
+<td>42.7</td>
+<td>340.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus_SOD-largesize-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-largesize-L_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are for </b>[VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset)<b> validation set mAP(0.5:0.95)。</b>
+
 ## Semantic Segmentation Module
 <table>
 <thead>
@@ -68,3 +204,89 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </tbody>
 </table>
 <b>Note: The above accuracy metrics are mIoU on the [Cityscapes](https://www.cityscapes-dataset.com/) dataset.</b>
+
+## [Abnormality Detection Module](../module_usage/tutorials/cv_modules/anomaly_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>Avg（%）</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>STFPM</td>
+<td>96.2</td>
+<td>21.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/STFPM_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/STFPM_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on the </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> dataset using the average anomaly score.</b>
+
+## [Face Detection Module](../module_usage/tutorials/cv_modules/face_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">93.7/90.7/68.1</td>
+<td>28.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PicoDet_LCNet_x2_5_face_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_LCNet_x2_5_face_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640.</b>
+
+## Text Detection Module
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>Detection Hmean (%)</th>
+<th>Model Size (M)</th>
+<th>Model Download Link</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/paddle3.0b2/PP-OCRv4_mobile_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_det_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>PP-OCRv4_server_det</td>
+<td>82.69</td>
+<td>100.1M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_server_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_det_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, handwriting, and more scenarios, with 500 images for detection.</b>
+
+## Text Recognition Module
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>Recognition Avg Accuracy (%)</th>
+<th>Model Size (M)</th>
+<th>Model Download Link</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/paddle3.0b2/PP-OCRv4_mobile_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_rec_pretrained.pdparams">Trained Model</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/paddle3.0b2/PP-OCRv4_server_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_rec_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, handwriting, and more scenarios, with 11,000 images for text recognition.</b>

docs/support_list/model_list_dcu.md

@@ -45,6 +45,142 @@ 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>[ImageNet-1k](https://www.image-net.org/index.php)<b>验证集 Top1 Acc。</b>
 
+## [图像多标签分类模块](../module_usage/tutorials/cv_modules/image_multilabel_classification.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>CLIP_vit_base_patch16_448_ML</td>
+<td>89.15</td>
+<td>325.6 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/CLIP_vit_base_patch16_448_ML_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/CLIP_vit_base_patch16_448_ML_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>PP-HGNetV2-B0_ML</td>
+<td>80.98</td>
+<td>39.6 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-HGNetV2-B0_ML_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B0_ML_pretrained.pdparams">训练模型</a></td>
+<tr>
+<td>PP-HGNetV2-B4_ML</td>
+<td>87.96</td>
+<td>88.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-HGNetV2-B4_ML_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B4_ML_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>PP-HGNetV2-B6_ML</td>
+<td>91.06</td>
+<td>286.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-HGNetV2-B6_ML_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B6_ML_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 [COCO2017](https://cocodataset.org/#home) 的多标签分类任务mAP。</b>
+
+## [图像特征模块](../module_usage/tutorials/cv_modules/image_feature.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>recall@1（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-ShiTuV2_rec_CLIP_vit_base</td>
+<td>88.69</td>
+<td>306.6 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-ShiTuV2_rec_CLIP_vit_base_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-ShiTuV2_rec_CLIP_vit_base_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 AliProducts recall@1。</b>
+
+## 目标检测模块
+<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>PicoDet-L</td>
+<td>42.6</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>37.5</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>29.1</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>52.9</td>
+<td>185.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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>49.8</td>
+<td>83.2 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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>43.7</td>
+<td>28.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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/paddle3.0b2/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>
+</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>
+
+## [小目标检测模块](../module_usage/tutorials/cv_modules/small_object_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_plus_SOD-S</td>
+<td>25.1</td>
+<td>77.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus_SOD-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-S_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>PP-YOLOE_plus_SOD-L</td>
+<td>31.9</td>
+<td>325.0 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus_SOD-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-L_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>PP-YOLOE_plus_SOD-largesize-L</td>
+<td>42.7</td>
+<td>340.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus_SOD-largesize-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-largesize-L_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 </b>[VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset)<b> 验证集 mAP(0.5:0.95)。</b>
+
 ## 语义分割模块
 <table>
 <thead>
@@ -68,3 +204,90 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 </tbody>
 </table>
 <b>注：以上精度指标为</b>[Cityscapes](https://www.cityscapes-dataset.com/)<b>数据集 mloU。</b>
+
+## [异常检测模块](../module_usage/tutorials/cv_modules/anomaly_detection.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>Avg（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>STFPM</td>
+<td>96.2</td>
+<td>21.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/STFPM_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/STFPM_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> 验证集 平均异常分数。</b>
+
+## [人脸检测模块](../module_usage/tutorials/cv_modules/face_detection.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">93.7/90.7/68.1</td>
+<td>28.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PicoDet_LCNet_x2_5_face_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_LCNet_x2_5_face_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+**注：以上精度指标是在WIDER-FACE验证集上，以640
+\*640作为输入尺寸评估得到的。**
+
+## 文本检测模块
+<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/paddle3.0b2/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>100.1M</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
+<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/paddle3.0b2/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/paddle3.0b2/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>

docs/support_list/model_list_mlu.en.md

@@ -203,6 +203,44 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are based on the mIoU of the [Cityscapes](https://www.cityscapes-dataset.com/) dataset.</b>
 
+## [Abnormality Detection Module](../module_usage/tutorials/cv_modules/anomaly_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>Avg（%）</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>STFPM</td>
+<td>96.2</td>
+<td>21.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/STFPM_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/STFPM_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on the </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> dataset using the average anomaly score.</b>
+
+## [Face Detection Module](../module_usage/tutorials/cv_modules/face_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">93.7/90.7/68.1</td>
+<td>28.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PicoDet_LCNet_x2_5_face_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_LCNet_x2_5_face_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640.</b>
+
 ## Text Detection Module
 <table>
 <thead>

docs/support_list/model_list_mlu.md

@@ -203,6 +203,45 @@ 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>[Cityscapes](https://www.cityscapes-dataset.com/)<b>数据集 mloU。</b>
 
+## [异常检测模块](../module_usage/tutorials/cv_modules/anomaly_detection.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>Avg（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>STFPM</td>
+<td>96.2</td>
+<td>21.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/STFPM_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/STFPM_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> 验证集 平均异常分数。</b>
+
+## [人脸检测模块](../module_usage/tutorials/cv_modules/face_detection.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">93.7/90.7/68.1</td>
+<td>28.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PicoDet_LCNet_x2_5_face_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_LCNet_x2_5_face_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+**注：以上精度指标是在WIDER-FACE验证集上，以640
+\*640作为输入尺寸评估得到的。**
+
 ## 文本检测模块
 <table>
 <thead>

docs/support_list/model_list_npu.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>
@@ -167,6 +167,16 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <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>MobileNetV4_hybrid_large</td>
+<td>83.8</td>
+<td>145.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MobileNetV4_hybrid_large_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_hybrid_large_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>MobileNetV4_hybrid_medium</td>
+<td>80.5</td>
+<td>42.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MobileNetV4_hybrid_medium_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_hybrid_medium_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
 <td>PP-HGNet_base</td>
 <td>85.0</td>
 <td>249.4 M</td>
@@ -356,6 +366,56 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td>81.10</td>
 <td>100.1 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SwinTransformer_tiny_patch4_window7_224_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>StarNet-S1</td>
+<td>73.6</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>74.8</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>77.0</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>79.0</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>
+<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>83.0</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>71.9</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>75.9</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>79.1</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>
 </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>
@@ -390,12 +450,59 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td>91.06</td>
 <td>286.5 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-HGNetV2-B6_ML_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B6_ML_pretrained.pdparams">Training Model</a></td></tr>
-</tr></tbody>
+<tr>
+<td>PP-LCNet_x1_0_ML</td>
+<td>77.96</td>
+<td>29.4 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LCNet_x1_0_ML_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_ML_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>ResNet50_ML</td>
+<td>83.42</td>
+<td>108.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ResNet50_ML_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet50_ML_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
 </table>
 <b>Note: The above accuracy metrics are for the multi-label classification task mAP of [COCO2017](https://cocodataset.org/#home).</b>
 
+## [Pedestrian Attribute Module](../module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mA (%)</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-LCNet_x1_0_pedestrian_attribute</td>
+<td>92.2</td>
+<td>6.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LCNet_x1_0_pedestrian_attribute_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_pedestrian_attribute_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are mA on PaddleX's internal self-built dataset.</b>
 
-## Object Detection Module
+## [Vehicle Attribute Module](../module_usage/tutorials/cv_modules/vehicle_attribute_recognition.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mA (%)</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-LCNet_x1_0_vehicle_attribute</td>
+<td>91.7</td>
+<td>6.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LCNet_x1_0_vehicle_attribute_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_vehicle_attribute_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are mA on the VeRi dataset.</b>
+
+## [Object Detection Module](../module_usage/tutorials/cv_modules/object_detection.en.md)
 <table>
 <thead>
 <tr>
@@ -564,13 +671,13 @@ 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>
 
-## [小目标检测模块](../module_usage/tutorials/cv_modules/small_object_detection.md)
+## [Small Object Detection Module](../module_usage/tutorials/cv_modules/small_object_detection.en.md)
 <table>
 <thead>
 <tr>
-<th>模型名称</th>
+<th>Model Name</th>
 <th>mAP（%）</th>
-<th>模型存储大小</th>
+<th>Model Size</th>
 <th>Model Download Link</th></tr>
 </thead>
 <tbody>
@@ -589,17 +696,32 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td>42.7</td>
 <td>340.5 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus_SOD-largesize-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-largesize-L_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>YOLOX-S</td>
+<td>40.4</td>
+<td>32.0 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/YOLOX-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/YOLOX-S_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>YOLOX-T</td>
+<td>32.9</td>
+<td>18.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/YOLOX-T_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/YOLOX-T_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>YOLOX-M</td>
+<td>46.9</td>
+<td>90.0 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/YOLOX-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/YOLOX-M_pretrained.pdparams">Trained Model</a></td></tr>
 </tbody>
 </table>
-<b>注：以上精度指标为 </b>[VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset)<b> 验证集 mAP(0.5:0.95)。</b>
+<b>Note: The above accuracy metrics are for </b>[VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset)<b> validation set mAP(0.5:0.95)。</b>
 
-## [行人检测模块](../module_usage/tutorials/cv_modules/human_detection.md)
+## [Pedestrian Detection Module](../module_usage/tutorials/cv_modules/human_detection.en.md)
 <table>
 <thead>
 <tr>
-<th>模型名称</th>
+<th>Model Name</th>
 <th>mAP（%）</th>
-<th>模型存储大小</th>
+<th>Model Size</th>
 <th>Model Download Link</th></tr>
 </thead>
 <tbody>
@@ -615,9 +737,9 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <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>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>
+<b>Note: The above accuracy metrics are for </b>[CrowdHuman](https://bj.bcebos.com/v1/paddledet/data/crowdhuman.zip)<b> validation set mAP(0.5:0.95)。</b>
 
-## Semantic Segmentation Module
+## [Semantic Segmentation Module](../module_usage/tutorials/cv_modules/semantic_segmentation.en.md)
 <table>
 <thead>
 <tr>
@@ -648,6 +770,11 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td>205.9 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Deeplabv3-R101_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Deeplabv3-R101_pretrained.pdparams">Trained Model</a></td></tr>
 <tr>
+<td>OCRNet_HRNet-W18</td>
+<td>80.67</td>
+<td>43.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/OCRNet_HRNet-W18_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/OCRNet_HRNet-W18_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
 <td>OCRNet_HRNet-W48</td>
 <td>82.15</td>
 <td>249.8 M</td>
@@ -657,11 +784,73 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td>73.10</td>
 <td>28.5 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LiteSeg-T_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LiteSeg-T_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SegFormer-B0 (slice)</td>
+<td>76.73</td>
+<td>13.2 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B0 (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B0 (slice)_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SegFormer-B1 (slice)</td>
+<td>78.35</td>
+<td>48.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B1 (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B1 (slice)_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SegFormer-B2 (slice)</td>
+<td>81.60</td>
+<td>96.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B2 (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B2 (slice)_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SegFormer-B3 (slice)</td>
+<td>82.47</td>
+<td>167.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B3 (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B3 (slice)_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SegFormer-B4 (slice)</td>
+<td>82.38</td>
+<td>226.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B4 (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B4 (slice)_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SegFormer-B5 (slice)</td>
+<td>82.58</td>
+<td>229.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B5 (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B5 (slice)_pretrained.pdparams">Trained Model</a></td></tr>
 </tbody>
 </table>
 <b>Note: The above accuracy metrics are for</b> [Cityscapes](https://www.cityscapes-dataset.com/) <b>dataset mIoU.</b>
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>mIoU (%)</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>SeaFormer_base(slice)</td>
+<td>40.92</td>
+<td>30.8 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SeaFormer_base(slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_base(slice)_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SeaFormer_large (slice)</td>
+<td>43.66</td>
+<td>49.8 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SeaFormer_large (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_large (slice)_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SeaFormer_small (slice)</td>
+<td>38.73</td>
+<td>14.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SeaFormer_small (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_small (slice)_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SeaFormer_tiny (slice)</td>
+<td>34.58</td>
+<td>6.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SeaFormer_tiny (slice)_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_tiny (slice)_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on the </b>[ADE20k](https://groups.csail.mit.edu/vision/datasets/ADE20K/)<b> dataset. "slice" indicates that the input image has been cropped.</b>
 
-## Instance Segmentation Module
+## [Instance Segmentation Module](../module_usage/tutorials/cv_modules/instance_segmentation.en.md)
 <table>
 <thead>
 <tr>
@@ -745,17 +934,22 @@ 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 Mask AP(0.5:0.95).</b>
 
-## [图像特征模块](../module_usage/tutorials/cv_modules/image_feature.md)
+## [Image Feature Module](../module_usage/tutorials/cv_modules/image_feature.en.md)
 <table>
 <thead>
 <tr>
-<th>模型名称</th>
+<th>Model Name</th>
 <th>recall@1（%）</th>
-<th>模型存储大小</th>
+<th>Model Size</th>
 <th>Model Download Link</th></tr>
 </thead>
 <tbody>
 <tr>
+<td>PP-ShiTuV2_rec</td>
+<td>84.2</td>
+<td>16.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-ShiTuV2_rec_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-ShiTuV2_rec_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
 <td>PP-ShiTuV2_rec_CLIP_vit_base</td>
 <td>88.69</td>
 <td>306.6 M</td>
@@ -767,15 +961,15 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-ShiTuV2_rec_CLIP_vit_large_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-ShiTuV2_rec_CLIP_vit_large_pretrained.pdparams">Trained Model</a></td></tr>
 </tbody>
 </table>
-<b>注：以上精度指标为 AliProducts recall@1。</b>
+<b>Note: The above accuracy metrics are for AliProducts recall@1。</b>
 
-## [主体检测模块](../module_usage/tutorials/cv_modules/mainbody_detection.md)
+## [Main Body Detection Module](../module_usage/tutorials/cv_modules/mainbody_detection.en.md)
 <table>
 <thead>
 <tr>
-<th>模型名称</th>
+<th>Model Name</th>
 <th>mAP（%）</th>
-<th>模型存储大小</th>
+<th>Model Size</th>
 <th>Model Download Link</th></tr>
 </thead>
 <tbody>
@@ -786,15 +980,15 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-ShiTuV2_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-ShiTuV2_det_pretrained.pdparams">Trained Model</a></td></tr>
 </tbody>
 </table>
-<b>注：以上精度指标为 [PaddleClas主体检测数据集](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/training/PP-ShiTu/mainbody_detection.md) mAP(0.5:0.95)。</b>
+<b>Note: The above accuracy metrics are for [PaddleClas主体检测数据集](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/training/PP-ShiTu/mainbody_detection.md) mAP(0.5:0.95)。</b>
 
-## [车辆检测模块](../module_usage/tutorials/cv_modules/vehicle_detection.md)
+## [Vehicle Detection Module](../module_usage/tutorials/cv_modules/vehicle_detection.en.md)
 <table>
 <thead>
 <tr>
-<th>模型名称</th>
+<th>Model Name</th>
 <th>mAP（%）</th>
-<th>模型存储大小</th>
+<th>Model Size</th>
 <th>Model Download Link</th></tr>
 </thead>
 <tbody>
@@ -810,15 +1004,34 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE-S_vehicle_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE-S_vehicle_pretrained.pdparams">Trained Model</a></td></tr>
 </tbody>
 </table>
-<b>注：以上精度指标为 </b>[PPVehicle](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppvehicle)<b> 验证集 mAP(0.5:0.95)。</b>
+<b>Note: The above accuracy metrics are for </b>[PPVehicle](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppvehicle)<b> validation set mAP(0.5:0.95)。</b>
 
-## [异常检测模块](../module_usage/tutorials/cv_modules/anomaly_detection.md)
+## [Face Detection Module](../module_usage/tutorials/cv_modules/face_detection.en.md)
 <table>
 <thead>
 <tr>
-<th>模型名称</th>
+<th>Model Name</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">93.7/90.7/68.1</td>
+<td>28.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PicoDet_LCNet_x2_5_face_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_LCNet_x2_5_face_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640.</b>
+
+## [Abnormality Detection Module](../module_usage/tutorials/cv_modules/anomaly_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
 <th>Avg（%）</th>
-<th>模型存储大小</th>
+<th>Model Size</th>
 <th>Model Download Link</th></tr>
 </thead>
 <tbody>
@@ -829,9 +1042,9 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/STFPM_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/STFPM_pretrained.pdparams">Trained Model</a></td></tr>
 </tbody>
 </table>
-<b>注：以上精度指标为 </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> 验证集 平均异常分数。</b>
+<b>Note: The above accuracy metrics are evaluated on the </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> dataset using the average anomaly score.</b>
 
-## Text Detection Module
+## [Text Detection Module](../module_usage/tutorials/ocr_modules/text_detection.en.md)
 <table>
 <thead>
 <tr>
@@ -855,7 +1068,7 @@ 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>
@@ -913,7 +1126,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are evaluated on the [PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition](https://aistudio.baidu.com/competition/detail/1131/0/introduction) B-Rank.</b>
 
-## Table Structure Recognition Module
+## [Table Structure Recognition Module](../module_usage/tutorials/ocr_modules/table_structure_recognition.en.md)
 <table>
 <thead>
 <tr>
@@ -928,11 +1141,78 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 <td>76.31</td>
 <td>6.9 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SLANet_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANet_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>SLANet_plus</td>
+<td>63.69</td>
+<td>6.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SLANet_plus_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SLANet_plus_pretrained.pdparams">Trained Model</a></td></tr>
 </tbody>
 </table>
 <b>Note: The above accuracy metrics are measured on the PubtabNet English table recognition dataset.</b>
 
-## Layout Analysis Module
+## [Image Rectification Module](../module_usage/tutorials/ocr_modules/text_image_unwarping.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>MS-SSIM (%)</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>UVDoc</td>
+<td>54.40</td>
+<td>30.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/UVDoc_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/UVDoc_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are measured on a self-built image rectification dataset by PaddleX.</b>
+
+## [Seal Text Detection Module](../module_usage/tutorials/ocr_modules/seal_text_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>Detection Hmean (%)</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-OCRv4_mobile_seal_det</td>
+<td>96.47</td>
+<td>4.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_mobile_seal_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_seal_det_pretrained.pdparams">Trained Model</a></td></tr>
+<tr>
+<td>PP-OCRv4_server_seal_det</td>
+<td>98.21</td>
+<td>108.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-OCRv4_server_seal_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_seal_det_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on a self-built seal dataset by PaddleX, containing 500 seal images.</b>
+
+## [Document Orientation Classification Module](../module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>Top-1 Acc (%)</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-LCNet_x1_0_doc_ori</td>
+<td>99.26</td>
+<td>7.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LCNet_x1_0_doc_ori_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_doc_ori_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are Top-1 Acc on PaddleX's internal self-built dataset.</b>
+
+## [Layout Detection Module](../module_usage/tutorials/ocr_modules/layout_detection.en.md)
 <table>
 <thead>
 <tr>
@@ -966,7 +1246,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built layout analysis dataset, containing 10,000 images.</b>
 
-## Time Series Forecasting Module
+## [Time Series Forecasting Module](../module_usage/tutorials/time_series_modules/time_series_forecasting.en.md)
 <table>
 <thead>
 <tr>
@@ -1023,7 +1303,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are measured on the [ETTH1](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/Etth1.tar) dataset (evaluation results on the test set test.csv).</b>
 
-## Time Series Anomaly Detection Module
+## [Time Series Anomaly Detection Module](../module_usage/tutorials/time_series_modules/time_series_anomaly_detection.en.md)
 <table>
 <thead>
 <tr>
@@ -1074,7 +1354,7 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are measured on the [PSM](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/ts_anomaly_examples.tar) dataset.</b>
 
-## Time Series Classification Module
+## [Time Series Classification Module](../module_usage/tutorials/time_series_modules/time_series_classification.en.md)
 <table>
 <thead>
 <tr>

docs/support_list/model_list_npu.md

@@ -167,6 +167,16 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <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>MobileNetV4_hybrid_large</td>
+<td>83.8</td>
+<td>145.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MobileNetV4_hybrid_large_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_hybrid_large_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>MobileNetV4_hybrid_medium</td>
+<td>80.5</td>
+<td>42.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MobileNetV4_hybrid_medium_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileNetV4_hybrid_medium_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
 <td>PP-HGNet_base</td>
 <td>85.0</td>
 <td>249.4 M</td>
@@ -356,6 +366,56 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>81.10</td>
 <td>100.1 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SwinTransformer_tiny_patch4_window7_224_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>StarNet-S1</td>
+<td>73.6</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>74.8</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>77.0</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>79.0</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>
+<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>83.0</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>71.9</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>75.9</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>79.1</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>
 </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>
@@ -390,10 +450,58 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>91.06</td>
 <td>286.5 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-HGNetV2-B6_ML_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-HGNetV2-B6_ML_pretrained.pdparams">训练模型</a></td></tr>
-</tr></tbody>
+<tr>
+<td>PP-LCNet_x1_0_ML</td>
+<td>77.96</td>
+<td>29.4 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LCNet_x1_0_ML_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_ML_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>ResNet50_ML</td>
+<td>83.42</td>
+<td>108.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ResNet50_ML_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet50_ML_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
 </table>
 <b>注：以上精度指标为 [COCO2017](https://cocodataset.org/#home) 的多标签分类任务mAP。</b>
 
+## [行人属性模块](../module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>mA（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-LCNet_x1_0_pedestrian_attribute</td>
+<td>92.2</td>
+<td>6.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LCNet_x1_0_pedestrian_attribute_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_pedestrian_attribute_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 PaddleX 内部自建数据集mA。</b>
+
+## [车辆属性模块](../module_usage/tutorials/cv_modules/vehicle_attribute_recognition.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>mA（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-LCNet_x1_0_vehicle_attribute</td>
+<td>91.7</td>
+<td>6.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LCNet_x1_0_vehicle_attribute_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_vehicle_attribute_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 VeRi 数据集 mA。</b>
+
 ## 目标检测模块
 <table>
 <thead>
@@ -588,6 +696,21 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>42.7</td>
 <td>340.5 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus_SOD-largesize-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus_SOD-largesize-L_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>YOLOX-S</td>
+<td>40.4</td>
+<td>32.0 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/YOLOX-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/YOLOX-S_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>YOLOX-T</td>
+<td>32.9</td>
+<td>18.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/YOLOX-T_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/YOLOX-T_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>YOLOX-M</td>
+<td>46.9</td>
+<td>90.0 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/YOLOX-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/YOLOX-M_pretrained.pdparams">训练模型</a></td></tr>
 </tbody>
 </table>
 <b>注：以上精度指标为 </b>[VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset)<b> 验证集 mAP(0.5:0.95)。</b>
@@ -647,6 +770,11 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>205.9 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Deeplabv3-R101_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Deeplabv3-R101_pretrained.pdparams">训练模型</a></td></tr>
 <tr>
+<td>OCRNet_HRNet-W18</td>
+<td>80.67</td>
+<td>43.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/OCRNet_HRNet-W18_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/OCRNet_HRNet-W18_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
 <td>OCRNet_HRNet-W48</td>
 <td>82.15</td>
 <td>249.8 M</td>
@@ -656,9 +784,71 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>73.10</td>
 <td>28.5 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-LiteSeg-T_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LiteSeg-T_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SegFormer-B0 (slice)</td>
+<td>76.73</td>
+<td>13.2 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B0 (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B0 (slice)_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SegFormer-B1 (slice)</td>
+<td>78.35</td>
+<td>48.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B1 (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B1 (slice)_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SegFormer-B2 (slice)</td>
+<td>81.60</td>
+<td>96.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B2 (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B2 (slice)_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SegFormer-B3 (slice)</td>
+<td>82.47</td>
+<td>167.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B3 (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B3 (slice)_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SegFormer-B4 (slice)</td>
+<td>82.38</td>
+<td>226.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B4 (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B4 (slice)_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SegFormer-B5 (slice)</td>
+<td>82.58</td>
+<td>229.7 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SegFormer-B5 (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SegFormer-B5 (slice)_pretrained.pdparams">训练模型</a></td></tr>
 </tbody>
 </table>
 <b>注：以上精度指标为</b>[Cityscapes](https://www.cityscapes-dataset.com/)<b>数据集 mloU。</b>
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>mloU（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>SeaFormer_base(slice)</td>
+<td>40.92</td>
+<td>30.8 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SeaFormer_base(slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_base(slice)_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SeaFormer_large (slice)</td>
+<td>43.66</td>
+<td>49.8 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SeaFormer_large (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_large (slice)_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SeaFormer_small (slice)</td>
+<td>38.73</td>
+<td>14.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SeaFormer_small (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_small (slice)_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
+<td>SeaFormer_tiny (slice)</td>
+<td>34.58</td>
+<td>6.1M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SeaFormer_tiny (slice)_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SeaFormer_tiny (slice)_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 </b>[ADE20k](https://groups.csail.mit.edu/vision/datasets/ADE20K/)<b> 数据集, slice 表示对输入图像进行了切图操作。</b>
 
 ## 实例分割模块
 <table>
@@ -755,6 +945,11 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 </thead>
 <tbody>
 <tr>
+<td>PP-ShiTuV2_rec</td>
+<td>84.2</td>
+<td>16.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-ShiTuV2_rec_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-ShiTuV2_rec_pretrained.pdparams">训练模型</a></td></tr>
+<tr>
 <td>PP-ShiTuV2_rec_CLIP_vit_base</td>
 <td>88.69</td>
 <td>306.6 M</td>
@@ -811,6 +1006,26 @@ 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>[PPVehicle](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppvehicle)<b> 验证集 mAP(0.5:0.95)。</b>
 
+## [人脸检测模块](../module_usage/tutorials/cv_modules/face_detection.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">93.7/90.7/68.1</td>
+<td>28.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PicoDet_LCNet_x2_5_face_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_LCNet_x2_5_face_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+**注：以上精度指标是在WIDER-FACE验证集上，以640
+\*640作为输入尺寸评估得到的。**
+
 ## [异常检测模块](../module_usage/tutorials/cv_modules/anomaly_detection.md)
 <table>
 <thead>
@@ -927,10 +1142,77 @@ PaddleX 内置了多条产线，每条产线都包含了若干模块，每个模
 <td>76.31</td>
 <td>6.9 M</td>
 <td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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>6.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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>
 </tbody>
 </table>
 <b>注：以上精度指标测量自PubtabNet英文表格识别数据集。</b>
 
+## [图像矫正模块](../module_usage/tutorials/ocr_modules/text_image_unwarping.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>MS-SSIM （%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>UVDoc</td>
+<td>54.40</td>
+<td>30.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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>
+
+## [印章文本检测模块](../module_usage/tutorials/ocr_modules/seal_text_detection.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>检测Hmean（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-OCRv4_mobile_seal_det</td>
+<td>96.47</td>
+<td>4.7M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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>98.21</td>
+<td>108.3 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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 PaddleX 自建的印章数据集，包含500印章图像。</b>
+
+## [文档方向分类模块](../module_usage/tutorials/ocr_modules/doc_img_orientation_classification.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>Top-1 Acc（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PP-LCNet_x1_0_doc_ori</td>
+<td>99.26</td>
+<td>7.1 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/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：以上精度指标为 PaddleX 内部自建数据集 Top-1 Acc 。</b>
+
 ## 版面区域分析模块
 <table>
 <thead>

docs/support_list/model_list_xpu.en.md

@@ -203,6 +203,44 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
 </table>
 <b>Note: The above accuracy metrics are based on the mIoU of the [Cityscapes](https://www.cityscapes-dataset.com/) dataset.</b>
 
+## [Abnormality Detection Module](../module_usage/tutorials/cv_modules/anomaly_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th>Avg（%）</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>STFPM</td>
+<td>96.2</td>
+<td>21.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/STFPM_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/STFPM_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on the </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> dataset using the average anomaly score.</b>
+
+## [Face Detection Module](../module_usage/tutorials/cv_modules/face_detection.en.md)
+<table>
+<thead>
+<tr>
+<th>Model Name</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
+<th>Model Size</th>
+<th>Model Download Link</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">93.7/90.7/68.1</td>
+<td>28.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PicoDet_LCNet_x2_5_face_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_LCNet_x2_5_face_pretrained.pdparams">Trained Model</a></td></tr>
+</tbody>
+</table>
+<b>Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640.</b>
+
 ## Text Detection Module
 <table>
 <thead>

docs/support_list/model_list_xpu.md

@@ -203,6 +203,45 @@ 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>[Cityscapes](https://www.cityscapes-dataset.com/)<b>数据集 mloU。</b>
 
+## [异常检测模块](../module_usage/tutorials/cv_modules/anomaly_detection.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th>Avg（%）</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>STFPM</td>
+<td>96.2</td>
+<td>21.5 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/STFPM_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/STFPM_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+<b>注：以上精度指标为 </b>[MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)<b> 验证集 平均异常分数。</b>
+
+## [人脸检测模块](../module_usage/tutorials/cv_modules/face_detection.md)
+<table>
+<thead>
+<tr>
+<th>模型名称</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
+<th>模型存储大小</th>
+<th>模型下载链接</th></tr>
+</thead>
+<tbody>
+<tr>
+<td>PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">93.7/90.7/68.1</td>
+<td>28.9 M</td>
+<td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PicoDet_LCNet_x2_5_face_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PicoDet_LCNet_x2_5_face_pretrained.pdparams">训练模型</a></td></tr>
+</tbody>
+</table>
+**注：以上精度指标是在WIDER-FACE验证集上，以640
+\*640作为输入尺寸评估得到的。**
+
 ## 文本检测模块
 <table>
 <thead>