update docs

Author: AmberC0209

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).

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。

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).

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。

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).

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。

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).

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。

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).

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。