PaddlePaddle · AmberC0209 · Dec 13, 2024
diff --git a/docs/module_usage/tutorials/cv_modules/anomaly_detection.en.md b/docs/module_usage/tutorials/cv_modules/anomaly_detection.en.md
@@ -195,3 +195,6 @@ The unsupervised anomaly detection module can be integrated into PaddleX pipelin
 2. <b>Module Integration</b>
 
 The weights you produce can be directly integrated into the unsupervised anomaly detection module. You can refer to the Python example code in [Quick Integration](#quick), simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/anomaly_detection.md b/docs/module_usage/tutorials/cv_modules/anomaly_detection.md
@@ -192,3 +192,6 @@ python main.py -c paddlex/configs/anomaly_detection/STFPM.yaml \
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到图像异常检测模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/face_detection.en.md b/docs/module_usage/tutorials/cv_modules/face_detection.en.md
@@ -267,3 +267,6 @@ The face detection module can be integrated into PaddleX pipelines such as [<b>F
 2. <b>Module Integration</b>
 
 The weights you produce can be directly integrated into the face detection module. You can refer to the Python example code in [Quick Integration](#quick), simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/face_detection.md b/docs/module_usage/tutorials/cv_modules/face_detection.md
@@ -264,3 +264,6 @@ python main.py -c paddlex/configs/face_detection/PicoDet_LCNet_x2_5_face.yaml \
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到人脸检测模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/face_feature.en.md b/docs/module_usage/tutorials/cv_modules/face_feature.en.md
@@ -263,3 +263,6 @@ The face feature module can be integrated into the PaddleX pipeline for [<b>Face
 2. <b>Module Integration</b>
 
 The weights you produced can be directly integrated into the face feature module. You can refer to the Python example code in [Quick Integration](#III.-Quick-Integration) and only need to replace the model with the path to the model you trained.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/face_feature.md b/docs/module_usage/tutorials/cv_modules/face_feature.md
@@ -261,3 +261,6 @@ python main.py -c paddlex/configs/face_recognition/MobileFaceNet.yaml \
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到人脸特征模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/human_detection.en.md b/docs/module_usage/tutorials/cv_modules/human_detection.en.md
@@ -243,3 +243,6 @@ Other related parameters can be set by modifying the fields under `Global` and `
 
 #### 4.4.2 Model Integration
 The weights you produce can be directly integrated into the human detection module. You can refer to the Python sample code in [Quick Integration](#iii-quick-integration) and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/human_detection.md b/docs/module_usage/tutorials/cv_modules/human_detection.md
@@ -242,3 +242,6 @@ python main.py -c paddlex/configs/human_detection/PP-YOLOE-S_human.yaml \
 
 #### 4.4.2 模型集成
 您产出的权重可以直接集成到行人检测模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/image_classification.en.md b/docs/module_usage/tutorials/cv_modules/image_classification.en.md
@@ -877,3 +877,6 @@ The image classification module can be integrated into the [General Image Classi
 2.<b>Module Integration</b>
 
 The weights you produce can be directly integrated into the image classification module. You can refer to the Python example code in <a href="#lable">Quick Integration</a>  and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/image_classification.md b/docs/module_usage/tutorials/cv_modules/image_classification.md
@@ -876,3 +876,6 @@ python main.py -c paddlex/configs/image_classification/PP-LCNet_x1_0.yaml \
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到图像分类模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/image_feature.en.md b/docs/module_usage/tutorials/cv_modules/image_feature.en.md
@@ -308,3 +308,6 @@ The image feature module can be integrated into the <b>General Image Recognition
 2.<b>Module Integration</b>
 
 The weights you produce can be directly integrated into the image feature module. Refer to the Python example code in [Quick Integration](#iii-quick-integration), and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/image_feature.md b/docs/module_usage/tutorials/cv_modules/image_feature.md
@@ -311,3 +311,6 @@ python main.py -c paddlex/configs/general_recognition/PP-ShiTuV2_rec.yaml  \
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到图像特征模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/image_multilabel_classification.en.md b/docs/module_usage/tutorials/cv_modules/image_multilabel_classification.en.md
@@ -303,3 +303,6 @@ The image multi-label classification module can be integrated into the [General
 2.<b>Module Integration</b>
 
 The weights you produce can be directly integrated into the image multi-label classification module. Refer to the Python example code in [Quick Integration](#iii-quick-integration) and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/image_multilabel_classification.md b/docs/module_usage/tutorials/cv_modules/image_multilabel_classification.md
@@ -304,3 +304,6 @@ python main.py -c paddlex/configs/multilabel_classification/PP-LCNet_x1_0_ML.yam
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到图像多标签分类模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/instance_segmentation.en.md b/docs/module_usage/tutorials/cv_modules/instance_segmentation.en.md
@@ -394,3 +394,6 @@ The instance segmentation module can be integrated into the [General Instance Se
 
 2.<b>Module Integration</b>
 The weights you produce can be directly integrated into the instance segmentation module. Refer to the Python example code in <a href="#lable">Quick Integration</a> , and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/instance_segmentation.md b/docs/module_usage/tutorials/cv_modules/instance_segmentation.md
@@ -393,3 +393,6 @@ python main.py -c paddlex/configs/instance_segmentation/Mask-RT-DETR-L.yaml \
 
 2.<b>模块集成</b>
 您产出的权重可以直接集成到实例分割模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/mainbody_detection.en.md b/docs/module_usage/tutorials/cv_modules/mainbody_detection.en.md
@@ -239,3 +239,6 @@ The main body detection module can be integrated into PaddleX pipelines such as
 2. <b>Module Integration</b>
 
 The weights you produce can be directly integrated into the main body detection module. You can refer to the Python example code in [Quick Integration](#quick), simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/mainbody_detection.md b/docs/module_usage/tutorials/cv_modules/mainbody_detection.md
@@ -238,3 +238,6 @@ python main.py -c paddlex/configs/mainbody_detection/PP-ShiTuV2_det.yaml \
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到主体检测模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/object_detection.en.md b/docs/module_usage/tutorials/cv_modules/object_detection.en.md
@@ -606,3 +606,6 @@ The object detection module can be integrated into the [General Object Detection
 2.<b>Module Integration</b>
 
 The weights you produce can be directly integrated into the object detection module. Refer to the Python example code in [Quick Integration](#iii-quick-integration), and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/object_detection.md b/docs/module_usage/tutorials/cv_modules/object_detection.md
@@ -598,3 +598,6 @@ python main.py -c paddlex/configs/object_detection/PicoDet-S.yaml  \
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到目标检测模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.en.md b/docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.en.md
@@ -272,3 +272,6 @@ The pedestrian attribute recognition module can be integrated into the [General
 2.<b>Module Integration</b>
 
 The weights you produce can be directly integrated into the pedestrian attribute recognition module. Refer to the Python example code in <a href="#lable">Quick Integration</a>  and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.md b/docs/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.md
@@ -271,3 +271,6 @@ python main.py -c paddlex/configs/pedestrian_attribute/PP-LCNet_x1_0_pedestrian_
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到行人属性识别模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/semantic_segmentation.en.md b/docs/module_usage/tutorials/cv_modules/semantic_segmentation.en.md
@@ -445,3 +445,6 @@ The document semantic segmentation module can be integrated into PaddleX pipelin
 2. <b>Module Integration</b>
 
 The weights you produce can be directly integrated into the semantic segmentation module. You can refer to the Python sample code in [Quick Integration](#iii-quick-integration) and just replace the model with the path to the model you trained.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/semantic_segmentation.md b/docs/module_usage/tutorials/cv_modules/semantic_segmentation.md
@@ -428,3 +428,6 @@ python main.py -c paddlex/configs/semantic_segmentation/PP-LiteSeg-T.yaml \
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到语义分割模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/small_object_detection.en.md b/docs/module_usage/tutorials/cv_modules/small_object_detection.en.md
@@ -283,3 +283,6 @@ The small object detection module can be integrated into the [Small Object Detec
 2. <b>Module Integration</b>
 
 The weights you produce can be directly integrated into the small object detection module. You can refer to the Python example code in [Quick Integration](#quick), simply replacing the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/small_object_detection.md b/docs/module_usage/tutorials/cv_modules/small_object_detection.md
@@ -283,3 +283,6 @@ python main.py -c paddlex/configs/small_object_detection/PP-YOLOE_plus_SOD-S.yam
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到小目标检测模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.en.md b/docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.en.md
@@ -258,3 +258,6 @@ The vehicle attribute recognition module can be integrated into the [General Ima
 2.<b>Module Integration</b>
 
 The weights you produce can be directly integrated into the vehicle attribute recognition module. Refer to the Python example code in  <a href="#lable">Quick Integration</a>  and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.md b/docs/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.md
@@ -255,3 +255,6 @@ python main.py -c paddlex/configs/vehicle_attribute/PP-LCNet_x1_0_vehicle_attrib
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到车辆属性识别模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/cv_modules/vehicle_detection.en.md b/docs/module_usage/tutorials/cv_modules/vehicle_detection.en.md
@@ -246,3 +246,6 @@ Other related parameters can be set by modifying the fields under `Global` and `
 
 #### 4.4.2 Model Integration
 The weights you produced can be directly integrated into the vehicle detection module. You can refer to the Python example code in [Quick Integration](#iii-quick-integration), simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/cv_modules/vehicle_detection.md b/docs/module_usage/tutorials/cv_modules/vehicle_detection.md
@@ -245,3 +245,6 @@ python main.py -c paddlex/configs/vehicle_detection/PP-YOLOE-S_vehicle.yaml \
 
 #### 4.4.2 模型集成
 您产出的权重可以直接集成到车辆检测模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md b/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md
@@ -276,3 +276,6 @@ The document image classification module can be integrated into PaddleX pipeline
 2.<b>Module Integration</b>
 
 The weights you produce can be directly integrated into the document image orientation classification module. You can refer to the Python sample code in [Quick Integration](#iii-quick-integration) and just replace the model with the path to the model you trained.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.md b/docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.md
@@ -251,3 +251,6 @@ python main.py -c paddlex/configs/doc_text_orientation/PP-LCNet_x1_0_doc_ori.yam
 2.<b>模块集成</b>
 
 您产出的权重可以直接集成到文档图像方向分类模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/ocr_modules/formula_recognition.en.md b/docs/module_usage/tutorials/ocr_modules/formula_recognition.en.md
@@ -265,3 +265,6 @@ Other related parameters can be set by modifying the `Global` and `Predict` fiel
 #### 4.4.2 Model Integration
 
 The weights you produce can be directly integrated into the formula recognition module. Refer to the Python example code in [Quick Integration](#iii-quick-integration), and simply replace the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).
diff --git a/docs/module_usage/tutorials/ocr_modules/formula_recognition.md b/docs/module_usage/tutorials/ocr_modules/formula_recognition.md
@@ -263,3 +263,6 @@ python main.py -c paddlex/configs/formula_recognition/LaTeX_OCR_rec.yaml \
 #### 4.4.2 模型集成
 
 您产出的权重可以直接集成到公式识别模块中，可以参考[快速集成](#三快速集成)的 Python 示例代码，只需要将模型替换为你训练的到的模型路径即可。
+
+!!! tip
+    关于我们项目的一些常见问题解答，请参考[FAQ](../../../FAQ.md)。如果您的问题没有得到解答，请随时在 [Issues](https://github.com/PaddlePaddle/PaddleX/issues) 中提出。
diff --git a/docs/module_usage/tutorials/ocr_modules/layout_detection.en.md b/docs/module_usage/tutorials/ocr_modules/layout_detection.en.md
@@ -301,3 +301,6 @@ The structure analysis module can be integrated into PaddleX pipelines such as t
 
 1. <b>Module Integration</b>
 The weights you produce can be directly integrated into the layout area localization module. You can refer to the Python example code in the [Quick Integration](#quick) section, simply replacing the model with the path to your trained model.
+
+!!! tip
+    For some frequently asked questions about our project, please refer to [FAQ](../../../FAQ.en.md). If your question has not been answered, feel free to raise it in [Issues](https://github.com/PaddlePaddle/PaddleX/issues).