[docs] Modify test environment (#4302)

* modify test environment

* modify test environment

docs/module_usage/tutorials/cv_modules/3d_bev_detection.en.md

@@ -32,13 +32,19 @@ The 3D multimodal fusion detection module is a key component in the fields of co
       <li><b>Performance Test Environment</b>
           <ul>
           <li><strong>Test Dataset：</strong>The above accuracy metrics are based on the <a href="https://www.nuscenes.org/nuscenes">nuscenes</a> validation set with mAP(0.5:0.95) and NDS 60.9, and the precision type is FP32.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/3d_bev_detection.md

@@ -40,7 +40,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -35,13 +35,19 @@ Unsupervised anomaly detection is a technology that automatically identifies and
       <li><b>Performance Test Environment</b>
           <ul>
               <li><strong>Test Dataset：</strong>The above model accuracy indicators are measured from the MVTec_AD dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/anomaly_detection.md

@@ -40,7 +40,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -67,13 +67,19 @@ Face detection is a fundamental task in object detection, aiming to automaticall
       <li><b>Performance Test Environment</b>
           <ul>
                     <li><strong>Test Dataset：</strong>The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/face_detection.md

@@ -70,7 +70,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -51,13 +51,19 @@ Face feature models typically take standardized face images processed through de
       <li><b>Performance Test Environment</b>
           <ul>
                <li><strong>Test Dataset：</strong>The above accuracy metrics are Accuracy scores measured on the AgeDB-30, CFP-FP, and LFW datasets.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/face_feature.md

@@ -56,7 +56,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -47,13 +47,19 @@ Human detection is a subtask of object detection, which utilizes computer vision
       <li><b>Performance Test Environment</b>
           <ul>
                 <li><strong>Test Dataset：</strong>The evaluation set for the above accuracy metrics is CrowdHuman dataset mAP(0.5:0.95).</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/human_detection.md

@@ -50,7 +50,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -51,13 +51,19 @@ Keypoint detection algorithms mainly include two approaches: Top-Down and Bottom
       <li><b>Performance Test Environment</b>
           <ul>
            <li><strong>Test Dataset：</strong>The above accuracy metrics are based on the COCO dataset AP(0.5:0.95) using ground truth annotations for bounding boxes.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/human_keypoint_detection.md

@@ -55,7 +55,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -773,13 +773,19 @@ The image classification module is a crucial component in computer vision system
       <li><b>Performance Test Environment</b>
           <ul>
                 <li><strong>Test Dataset：</strong><a href="https://www.image-net.org/index.php">ImageNet-1k</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/image_classification.md

@@ -772,7 +772,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -52,13 +52,19 @@ The image feature module is one of the important tasks in computer vision, prima
       <li><b>Performance Test Environment</b>
           <ul>
               <li><strong>Test Dataset：</strong>PaddleX Custom Dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/image_feature.md

@@ -56,7 +56,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -79,13 +79,19 @@ The image multi-label classification module is a crucial component in computer v
       <li><b>Performance Test Environment</b>
           <ul>
           <li><strong>Test Dataset：</strong>multi-label classification task on  <a href="https://cocodataset.org/#home">COCO2017</a></li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/image_multilabel_classification.md

@@ -83,7 +83,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -182,13 +182,19 @@ The instance segmentation module is a crucial component in computer vision syste
       <li><b>Performance Test Environment</b>
           <ul>
            <li><strong>Test Dataset：</strong><a href="https://cocodataset.org/#home">COCO2017</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/instance_segmentation.md

@@ -186,7 +186,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -38,13 +38,19 @@ Mainbody detection is a fundamental task in object detection, aiming to identify
       <li><b>Performance Test Environment</b>
           <ul>
               <li><strong>Test Dataset：</strong>PaddleClas mainbody detection dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/mainbody_detection.md

@@ -42,7 +42,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -430,13 +430,19 @@ The object detection module is a crucial component in computer vision systems, r
       <li><b>Performance Test Environment</b>
           <ul>
             <li><strong>Test Dataset：</strong> <a href="https://cocodataset.org/#home">COCO2017</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/object_detection.md

@@ -434,7 +434,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -47,13 +47,19 @@ Open-vocabulary object detection is an advanced object detection technology aime
       <li><b>Performance Test Environment</b>
           <ul>
               <li><strong>Test Dataset：</strong>  Based on the open vocabulary object detection model trained on the three datasets: O365, GoldG, and Cap4M.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/open_vocabulary_detection.md

@@ -52,7 +52,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -39,13 +39,19 @@ Open-vocabulary segmentation is an image segmentation task that aims to segment
   <ul>
       <li><b>Performance Test Environment</b>
           <ul>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/open_vocabulary_segmentation.md

@@ -44,7 +44,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -40,13 +40,19 @@ Pedestrian attribute recognition is a crucial component in computer vision syste
       <li><b>Performance Test Environment</b>
           <ul>
                 <li><strong>Test Dataset：</strong> PaddleX's internal self-built dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.md

@@ -44,7 +44,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -35,13 +35,19 @@ Rotated object detection is a derivative of the object detection module, specifi
       <li><b>Performance Test Environment</b>
           <ul>
                  <li><strong>Test Dataset：</strong>  <a href="https://captain-whu.github.io/DOTA/">DOTA</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/rotated_object_detection.md

@@ -39,7 +39,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -229,13 +229,19 @@ Semantic segmentation is a technique in computer vision that classifies each pix
       <li><b>Performance Test Environment</b>
           <ul>
             <li><strong>Test Dataset：</strong>  <a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a> dataset and <a href="https://www.cityscapes-dataset.com/">Cityscapes</a> dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/semantic_segmentation.md

@@ -234,7 +234,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -55,13 +55,19 @@ Small object detection typically refers to accurately detecting and locating sma
       <li><b>Performance Test Environment</b>
           <ul>
                 <li><strong>Test Dataset：</strong>  VisDrone-DET dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/small_object_detection.md

@@ -60,7 +60,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -40,13 +40,19 @@ Vehicle attribute recognition is a crucial component in computer vision systems.
       <li><b>Performance Test Environment</b>
           <ul>
            <li><strong>Test Dataset：</strong>  VeRi dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.md

@@ -44,7 +44,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

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

@@ -42,13 +42,19 @@ Vehicle detection is a subtask of object detection, specifically referring to th
       <li><b>Performance Test Environment</b>
           <ul>
            <li><strong>Test Dataset：</strong>  PPVehicle dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/cv_modules/vehicle_detection.md

@@ -48,7 +48,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md

@@ -40,13 +40,19 @@ The document image orientation classification module is aim to distinguish the o
       <li><b>Performance Test Environment</b>
           <ul>
              <li><strong>Test Dataset：</strong> Self-built multi-scene dataset (1000 images, including ID cards/documents, etc.)</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.md

@@ -44,7 +44,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/formula_recognition.en.md

@@ -99,13 +99,19 @@ The formula recognition module is a crucial component of OCR (Optical Character
       <li><b>Performance Test Environment</b>
           <ul>
               <li><strong>Test Dataset：</strong>PaddleX Internal Self-built Formula Recognition Test Set</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/formula_recognition.md

@@ -101,7 +101,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/layout_detection.en.md

@@ -257,13 +257,19 @@ The core task of structure analysis is to parse and segment the content of input
                    <li>17-Class Area Detection Model: A self-built layout area detection dataset by PaddleOCR, including 892 common document type images such as Chinese and English papers, magazines, and research reports.</li>
                  </ul>
              </li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/layout_detection.md

@@ -266,7 +266,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/seal_text_detection.en.md

@@ -49,13 +49,19 @@ The seal text detection module typically outputs multi-point bounding boxes arou
       <li><b>Performance Test Environment</b>
           <ul>
                <li><strong>Test Dataset：</strong> PaddleX Custom Dataset, Containing 500 Images of Circular Stamps.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/seal_text_detection.md

@@ -52,7 +52,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/table_cells_detection.en.md

@@ -40,13 +40,19 @@ The table cell detection module is a key component of table recognition tasks, r
       <li><b>Performance Test Environment</b>
           <ul>
              <li><strong>Test Dataset：</strong> PaddleX Internal Self-built Evaluation Dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/table_cells_detection.md

@@ -44,7 +44,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/table_classification.en.md

@@ -33,13 +33,19 @@ The table classification module is a key component of a computer vision system,
       <li><b>Performance Test Environment</b>
           <ul>
            <li><strong>Test Dataset：</strong> PaddleX Internal Self-built Evaluation Dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/table_classification.md

@@ -38,7 +38,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/table_structure_recognition.en.md

@@ -60,13 +60,19 @@ SLANet_plus is an enhanced version of SLANet, a table structure recognition mode
       <li><b>Performance Test Environment</b>
           <ul>
           <li><strong>Test Dataset：</strong> PaddleX Internal Self-built Evaluation Dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/table_structure_recognition.md

@@ -62,7 +62,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/text_detection.en.md

@@ -83,13 +83,19 @@ The text detection module is a crucial component in OCR (Optical Character Recog
       <li><b>Performance Test Environment</b>
           <ul>
            <li><strong>Test Dataset：</strong>PaddleOCR3.0 newly constructed multilingual dataset (including Chinese, Traditional Chinese, English, Japanese), covering street scenes, web images, documents, handwriting, blur, rotation, distortion, etc., totaling 2677 images.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/text_detection.md

@@ -89,7 +89,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/text_image_unwarping.en.md

@@ -40,13 +40,19 @@ The primary purpose of Text Image Unwarping is to perform geometric transformati
           <ul>
           <li><strong>Test Dataset：</strong>
           <a href="https://www3.cs.stonybrook.edu/~cvl/docunet.html">DocUNet benchmark</a> dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/text_image_unwarping.md

@@ -45,7 +45,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/text_recognition.en.md

@@ -384,13 +384,19 @@ The ultra-lightweight cyrillic alphabet recognition model trained based on the P
                  <li>Multilingual Recognition Model: A self-built multilingual dataset using PaddleX.</li>
                </ul>
              </li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/text_recognition.md

@@ -417,7 +417,12 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://padd
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.en.md

@@ -51,13 +51,19 @@ The text line orientation classification module primarily distinguishes the orie
       <li><b>Performance Test Environment</b>
           <ul>
              <li><strong>Test Dataset：</strong> PaddleX Self-built Dataset, Covering Multiple Scenarios Such as Documents and Certificates, Containing 1000 Images.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/ocr_modules/textline_orientation_classification.md

@@ -56,7 +56,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.en.md

@@ -65,13 +65,19 @@ Time series anomaly detection focuses on identifying abnormal points or periods
       <li><b>Performance Test Environment</b>
           <ul>
             <li><strong>Test Dataset：</strong> <b>PSM</b> dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.md

@@ -72,7 +72,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/time_series_modules/time_series_classification.en.md

@@ -35,13 +35,19 @@ Time series classification involves identifying and categorizing different patte
       <li><b>Performance Test Environment</b>
           <ul>
            <li><strong>Test Dataset：</strong> UWaveGestureLibrary.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/time_series_modules/time_series_classification.md

@@ -40,7 +40,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/time_series_modules/time_series_forecasting.en.md

@@ -97,13 +97,19 @@ Time series forecasting aims to predict the possible values or states at a futur
       <li><b>Performance Test Environment</b>
           <ul>
             <li><strong>Test Dataset：</strong> <a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/data/Etth1.tar">ETTH1</a> test dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/time_series_modules/time_series_forecasting.md

@@ -105,7 +105,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/video_modules/video_classification.en.md

@@ -48,13 +48,19 @@ PP-TSM is a video classification model developed by Baidu PaddlePaddle's Vision
       <li><b>Performance Test Environment</b>
           <ul>
                <li><strong>Test Dataset：</strong> <a href="https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/dataset/k400.md">K400</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/video_modules/video_classification.md

@@ -53,7 +53,12 @@ PP-TSM是一种百度飞桨视觉团队自研的视频分类模型。该模型
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/module_usage/tutorials/video_modules/video_detection.en.md

@@ -36,13 +36,19 @@ YOWO is a single-stage network with two branches. One branch extracts spatial fe
       <li><b>Performance Test Environment</b>
           <ul>
                <li><strong>Test Dataset：</strong> <a href="http://www.thumos.info/download.html">UCF101-24</a> test set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/module_usage/tutorials/video_modules/video_detection.md

@@ -40,7 +40,12 @@ YOWO是具有两个分支的单阶段网络。一个分支通过2D-CNN提取关
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.en.md

@@ -41,13 +41,19 @@ Please note that the 3D multi-modal fusion detection pipeline currently does not
       <li><b>Performance Test Environment</b>
           <ul>
               <li><strong>Test Dataset：</strong><a href="https://www.nuscenes.org/nuscenes">nuscenes</a> validation set</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.md

@@ -45,7 +45,12 @@ BEVFusion 是一种多模态 3D 目标检测模型，通过将环视摄像头图
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.en.md

@@ -113,13 +113,19 @@ The face recognition pipeline is an end-to-end system dedicated to solving face
                 <li>Face Feature Model: Evaluated on the AgeDB-30, CFP-FP, and LFW datasets, respectively.</li>
               </ul>
             </li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.md

@@ -118,7 +118,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.en.md

@@ -82,13 +82,19 @@ PP-ShiTuV2 is a practical general image recognition system mainly composed of th
                 <li>Image Feature Model: AliProducts Dataset.</li>
               </ul>
             </li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.md

@@ -86,7 +86,12 @@ PP-ShiTuV2 是一个实用的通用图像识别系统，主要由主体检测、
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.en.md

@@ -85,13 +85,19 @@ PaddleX's Human Keypoint Detection Pipeline is a Top-Down solution consisting of
                 <li>Human Keypoint Detection Model: COCO Dataset AP(0.5:0.95), with detection boxes obtained from ground truth annotations.</li>
               </ul>
             </li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.md

@@ -89,7 +89,12 @@ PaddleX 的人体关键点检测产线是一个 Top-Down 方案，由行人检
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.en.md

@@ -36,13 +36,19 @@ This pipeline integrates the high-precision anomaly detection model STFPM, which
       <li><b>Performance Test Environment</b>
           <ul>
             <li><strong>Test Dataset：</strong>The above accuracy metrics are the average anomaly scores on the <b><a href="https://www.mvtec.com/company/research/datasets/mvtec-ad">MVTec AD</a></b> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.md

@@ -44,7 +44,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/image_classification.en.md

@@ -780,13 +780,19 @@ Image classification is a technique that assigns images to predefined categories
       <li><b>Performance Test Environment</b>
           <ul>
              <li><strong>Test Dataset：</strong><a href="https://www.image-net.org/index.php">ImageNet-1k</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/image_classification.md

@@ -783,7 +783,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.en.md

@@ -65,13 +65,19 @@ Image multi-label classification is a technique that assigns multiple relevant c
       <li><b>Performance Test Environment</b>
           <ul>
              <li><strong>Test Dataset：</strong>multi-label classification task on <b><a href="https://cocodataset.org/#home">COCO2017</a></b>.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.md

@@ -72,7 +72,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.en.md

@@ -182,13 +182,19 @@ Instance segmentation is a computer vision task that not only identifies the obj
       <li><b>Performance Test Environment</b>
           <ul>
            <li><strong>Test Dataset：</strong><a href="https://cocodataset.org/#home">COCO2017</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.md

@@ -192,7 +192,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/object_detection.en.md

@@ -423,13 +423,19 @@ Object detection aims to identify the categories and locations of multiple objec
       <li><b>Performance Test Environment</b>
           <ul>
                <li><strong>Test Dataset：</strong><a href="https://cocodataset.org/#home">COCO2017</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/object_detection.md

@@ -438,7 +438,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.en.md

@@ -42,13 +42,19 @@ Open vocabulary object detection is an advanced object detection technology that
       <li><b>Performance Test Environment</b>
           <ul>
             <li><strong>Test Dataset：</strong>COCO val2017 validation set</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.md

@@ -50,7 +50,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.en.md

@@ -45,13 +45,19 @@ Open vocabulary segmentation is an image segmentation task that aims to segment
   <ul>
       <li><b>Performance Test Environment</b>
           <ul>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.md

@@ -49,7 +49,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.en.md

@@ -78,13 +78,19 @@ Pedestrian attribute recognition is a key function in computer vision systems, u
                    <li>Pedestrian Attribute Recognition Model: PaddleX Internal Self-built Dataset.</li>
                   </ul>
                </li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.md

@@ -84,7 +84,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.en.md

@@ -41,13 +41,19 @@ Rotated object detection is a variant of the object detection module, specifical
       <li><b>Performance Test Environment</b>
           <ul>
                <li><strong>Test Dataset：</strong> <a href="https://captain-whu.github.io/DOTA/">DOTA</a> validation set.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.md

@@ -48,7 +48,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.en.md

@@ -209,13 +209,19 @@ Semantic segmentation is a computer vision technique that aims to assign each pi
       <li><b>Performance Test Environment</b>
           <ul>
                <li><strong>Test Dataset：</strong> <a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a> dataset and <a href="https://www.cityscapes-dataset.com/"> Cityscapes</a> dataset.</li>
-              <li><strong>Hardware Configuration：</strong>
+              <li><strong>Hardware Configuration:</strong>
                   <ul>
                       <li>GPU: NVIDIA Tesla T4</li>
                       <li>CPU: Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>Other Environments: Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
                   </ul>
               </li>
+              <li><strong>Software Environment:</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
+                  </ul>
+              </li>
+              </li>
           </ul>
       </li>
       <li><b>Inference Mode Description</b></li>

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.md

@@ -221,7 +221,12 @@ comments: true
                   <ul>
                       <li>GPU：NVIDIA Tesla T4</li>
                       <li>CPU：Intel Xeon Gold 6271C @ 2.60GHz</li>
-                      <li>其他环境：Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                  </ul>
+              </li>
+              <li><strong>软件环境：</strong>
+                  <ul>
+                      <li>Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6</li>
+                      <li>paddlepaddle 3.0.0 / paddlex 3.0.3</li>
                   </ul>
               </li>
           </ul>