add model url for docs

Author: cuicheng01

docs/module_usage/tutorials/cv_modules/anomaly_detection.en.md

@@ -13,15 +13,15 @@ Unsupervised anomaly detection is a technology that automatically identifies and
 <table>
 <thead>
 <tr>
-<th>Model</th>
+<th>Model</th><th>Model Download Link</th>
 <th>ROCAUC（Avg）</th>
 <th>Model Size (M)</th>
 <th>Description</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td>STFPM</td>
+<td>STFPM</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>
 <td>0.962</td>
 <td>22.5</td>
 <td>An unsupervised anomaly detection algorithm based on representation consists of a pre-trained teacher network and a student network with the same structure. The student network detects anomalies by matching its own features with the corresponding features in the teacher network.</td>

docs/module_usage/tutorials/cv_modules/anomaly_detection.md

@@ -13,15 +13,15 @@ comments: true
 <table>
 <thead>
 <tr>
-<th>模型</th>
+<th>模型</th><th>模型下载链接</th>
 <th>ROCAUC（Avg）</th>
 <th>模型存储大小（M)</th>
 <th>介绍</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td>STFPM</td>
+<td>STFPM</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>
 <td>0.962</td>
 <td>22.5</td>
 <td>一种基于表示的图像异常检测算法，由预训练的教师网络和结构相同的学生网络组成。学生网络通过将自身特征与教师网络中的对应特征相匹配来检测异常。</td>

docs/module_usage/tutorials/cv_modules/face_detection.en.md

@@ -13,8 +13,8 @@ Face detection is a fundamental task in object detection, aiming to automaticall
 <table>
 <thead>
 <tr>
-<th style="text-align: center;">Model</th>
-<th style="text-align: center;">AP (%)<br>Easy/Medium/Hard</th>
+<th style="text-align: center;">Model</th><th>Model Download Link</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
 <th style="text-align: center;">GPU Inference Time (ms)</th>
 <th style="text-align: center;">CPU Inference Time (ms)</th>
 <th style="text-align: center;">Model Size (M)</th>
@@ -23,31 +23,31 @@ Face detection is a fundamental task in object detection, aiming to automaticall
 </thead>
 <tbody>
 <tr>
-<td style="text-align: center;">BlazeFace</td>
+<td style="text-align: center;">BlazeFace</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/BlazeFace_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/BlazeFace_pretrained.pdparams">Trained Model</a></td>
 <td style="text-align: center;">77.7/73.4/49.5</td>
 <td style="text-align: center;"></td>
 <td style="text-align: center;"></td>
 <td style="text-align: center;">0.447</td>
 <td style="text-align: center;">A lightweight and efficient face detection model</td>
 </tr>
 <tr>
-<td style="text-align: center;">BlazeFace-FPN-SSH</td>
+<td style="text-align: center;">BlazeFace-FPN-SSH</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/BlazeFace-FPN-SSH_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/BlazeFace-FPN-SSH_pretrained.pdparams">Trained Model</a></td>
 <td style="text-align: center;">83.2/80.5/60.5</td>
 <td style="text-align: center;"></td>
 <td style="text-align: center;"></td>
 <td style="text-align: center;">0.606</td>
 <td style="text-align: center;">An improved model of BlazeFace, incorporating FPN and SSH structures</td>
 </tr>
 <tr>
-<td style="text-align: center;">PicoDet_LCNet_x2_5_face</td>
+<td style="text-align: center;">PicoDet_LCNet_x2_5_face</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>
 <td style="text-align: center;">93.7/90.7/68.1</td>
 <td style="text-align: center;"></td>
 <td style="text-align: center;"></td>
 <td style="text-align: center;">28.9</td>
 <td style="text-align: center;">Face Detection model based on PicoDet_LCNet_x2_5</td>
 </tr>
 <tr>
-<td style="text-align: center;">PP-YOLOE_plus-S_face</td>
+<td style="text-align: center;">PP-YOLOE_plus-S_face</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus-S_face_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-S_face_pretrained.pdparams">Trained Model</a></td>
 <td style="text-align: center;">93.9/91.8/79.8</td>
 <td style="text-align: center;"></td>
 <td style="text-align: center;"></td>

docs/module_usage/tutorials/cv_modules/face_detection.md

@@ -12,8 +12,8 @@ comments: true
 <table>
 <thead>
 <tr>
-<th>模型</th>
-<th style="text-align: center;">AP (%)<br>Easy/Medium/Hard</th>
+<th>模型</th><th>模型下载链接</th>
+<th style="text-align: center;">AP (%)<br/>Easy/Medium/Hard</th>
 <th>GPU推理耗时 (ms)</th>
 <th>CPU推理耗时</th>
 <th>模型存储大小 (M)</th>
@@ -22,31 +22,31 @@ comments: true
 </thead>
 <tbody>
 <tr>
-<td>BlazeFace</td>
+<td>BlazeFace</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/BlazeFace_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/BlazeFace_pretrained.pdparams">训练模型</a></td>
 <td style="text-align: center;">77.7/73.4/49.5</td>
 <td></td>
 <td></td>
 <td>0.447</td>
 <td>轻量高效的人脸检测模型</td>
 </tr>
 <tr>
-<td>BlazeFace-FPN-SSH</td>
+<td>BlazeFace-FPN-SSH</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/BlazeFace-FPN-SSH_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/BlazeFace-FPN-SSH_pretrained.pdparams">训练模型</a></td>
 <td style="text-align: center;">83.2/80.5/60.5</td>
 <td></td>
 <td></td>
 <td>0.606</td>
 <td>BlazeFace的改进模型，增加FPN和SSH结构</td>
 </tr>
 <tr>
-<td>PicoDet_LCNet_x2_5_face</td>
+<td>PicoDet_LCNet_x2_5_face</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>
 <td style="text-align: center;">93.7/90.7/68.1</td>
 <td></td>
 <td></td>
 <td>28.9</td>
 <td>基于PicoDet_LCNet_x2_5的人脸检测模型</td>
 </tr>
 <tr>
-<td>PP-YOLOE_plus-S_face</td>
+<td>PP-YOLOE_plus-S_face</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_plus-S_face_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-S_face_pretrained.pdparams">训练模型</a></td>
 <td style="text-align: center;">93.9/91.8/79.8</td>
 <td></td>
 <td></td>

docs/module_usage/tutorials/cv_modules/face_feature.en.md

@@ -14,9 +14,9 @@ Face feature models typically take standardized face images processed through de
 <table>
 <thead>
 <tr>
-<th>Model</th>
+<th>Model</th><th>Model Download Link</th>
 <th>Output Feature Dimension</th>
-<th>Acc (%)<br>AgeDB-30/CFP-FP/LFW</th>
+<th>Acc (%)<br/>AgeDB-30/CFP-FP/LFW</th>
 <th>GPU Inference Time (ms)</th>
 <th>CPU Inference Time</th>
 <th>Model Size (M)</th>
@@ -25,7 +25,7 @@ Face feature models typically take standardized face images processed through de
 </thead>
 <tbody>
 <tr>
-<td>MobileFaceNet</td>
+<td>MobileFaceNet</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MobileFaceNet_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileFaceNet_pretrained.pdparams">Trained Model</a></td>
 <td>128</td>
 <td>96.28/96.71/99.58</td>
 <td></td>
@@ -34,7 +34,7 @@ Face feature models typically take standardized face images processed through de
 <td>Face feature model trained on MobileFaceNet with MS1Mv3 dataset</td>
 </tr>
 <tr>
-<td>ResNet50_face</td>
+<td>ResNet50_face</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ResNet50_face_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet50_face_pretrained.pdparams">Trained Model</a></td>
 <td>512</td>
 <td>98.12/98.56/99.77</td>
 <td></td>

docs/module_usage/tutorials/cv_modules/face_feature.md

@@ -14,9 +14,9 @@ comments: true
 <table>
 <thead>
 <tr>
-<th>模型</th>
+<th>模型</th><th>模型下载链接</th>
 <th>输出特征维度</th>
-<th>AP (%)<br>AgeDB-30/CFP-FP/LFW</th>
+<th>AP (%)<br/>AgeDB-30/CFP-FP/LFW</th>
 <th>GPU推理耗时 (ms)</th>
 <th>CPU推理耗时</th>
 <th>模型存储大小 (M)</th>
@@ -25,7 +25,7 @@ comments: true
 </thead>
 <tbody>
 <tr>
-<td>MobileFaceNet</td>
+<td>MobileFaceNet</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MobileFaceNet_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MobileFaceNet_pretrained.pdparams">训练模型</a></td>
 <td>128</td>
 <td>96.28/96.71/99.58</td>
 <td></td>
@@ -34,7 +34,7 @@ comments: true
 <td>基于MobileFaceNet在MS1Mv3数据集上训练的人脸特征提取模型</td>
 </tr>
 <tr>
-<td>ResNet50_face</td>
+<td>ResNet50_face</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/ResNet50_face_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/ResNet50_face_pretrained.pdparams">训练模型</a></td>
 <td>512</td>
 <td>98.12/98.56/99.77</td>
 <td></td>

docs/module_usage/tutorials/cv_modules/human_detection.en.md

@@ -11,32 +11,32 @@ Human detection is a subtask of object detection, which utilizes computer vision
 
 
 <table>
-  <tr>
-    <th >Model</th>
-    <th >mAP(0.5:0.95)</th>
-    <th >mAP(0.5)</th>
-    <th >GPU Inference Time (ms)</th>
-    <th >CPU Inference Time (ms)</th>
-    <th >Model Size (M)</th>
-    <th >Description</th>
-  </tr>
-  <tr>
-    <td>PP-YOLOE-L_human</td>
-    <td>48.0</td>
-    <td>81.9</td>
-    <td>32.8</td>
-    <td>777.7</td>
-    <td>196.02</td>
-    <td rowspan="2">Human detection model based on PP-YOLOE</td>
-  </tr>
-  <tr>
-    <td>PP-YOLOE-S_human</td>
-    <td>42.5</td>
-    <td>77.9</td>
-    <td>15.0</td>
-    <td>179.3</td>
-    <td>28.79</td>
-  </tr>
+<tr>
+<th>Model</th><th>Model Download Link</th>
+<th>mAP(0.5:0.95)</th>
+<th>mAP(0.5)</th>
+<th>GPU Inference Time (ms)</th>
+<th>CPU Inference Time (ms)</th>
+<th>Model Size (M)</th>
+<th>Description</th>
+</tr>
+<tr>
+<td>PP-YOLOE-L_human</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>
+<td>48.0</td>
+<td>81.9</td>
+<td>32.8</td>
+<td>777.7</td>
+<td>196.02</td>
+<td rowspan="2">Human detection model based on PP-YOLOE</td>
+</tr>
+<tr>
+<td>PP-YOLOE-S_human</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>
+<td>42.5</td>
+<td>77.9</td>
+<td>15.0</td>
+<td>179.3</td>
+<td>28.79</td>
+</tr>
 </table>
 
 <b>Note: The evaluation set for the above accuracy metrics is CrowdHuman dataset mAP(0.5:0.95). GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b>

docs/module_usage/tutorials/cv_modules/human_detection.md

@@ -10,32 +10,32 @@ comments: true
 ## 二、支持模型列表
 
 <table>
-  <tr>
-    <th >模型</th>
-    <th >mAP(0.5:0.95)</th>
-    <th >mAP(0.5)</th>
-    <th >GPU推理耗时（ms）</th>
-    <th >CPU推理耗时 (ms)</th>
-    <th >模型存储大小（M）</th>
-    <th >介绍</th>
-  </tr>
-  <tr>
-    <td>PP-YOLOE-L_human</td>
-    <td>48.0</td>
-    <td>81.9</td>
-    <td>32.8</td>
-    <td>777.7</td>
-    <td>196.02</td>
-    <td rowspan="2">基于PP-YOLOE的行人检测模型</td>
-  </tr>
-  <tr>
-    <td>PP-YOLOE-S_human</td>
-    <td>42.5</td>
-    <td>77.9</td>
-    <td>15.0</td>
-    <td>179.3</td>
-    <td>28.79</td>
-  </tr>
+<tr>
+<th>模型</th><th>模型下载链接</th>
+<th>mAP(0.5:0.95)</th>
+<th>mAP(0.5)</th>
+<th>GPU推理耗时（ms）</th>
+<th>CPU推理耗时 (ms)</th>
+<th>模型存储大小（M）</th>
+<th>介绍</th>
+</tr>
+<tr>
+<td>PP-YOLOE-L_human</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>
+<td>48.0</td>
+<td>81.9</td>
+<td>32.8</td>
+<td>777.7</td>
+<td>196.02</td>
+<td rowspan="2">基于PP-YOLOE的行人检测模型</td>
+</tr>
+<tr>
+<td>PP-YOLOE-S_human</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>
+<td>42.5</td>
+<td>77.9</td>
+<td>15.0</td>
+<td>179.3</td>
+<td>28.79</td>
+</tr>
 </table>
 
 <b>注：以上精度指标为CrowdHuman数据集 mAP(0.5:0.95)。所有模型 GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为8，精度类型为 FP32。</b>