fix_doc (#3387)

* fix_doc

* fix_doc

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

@@ -82,7 +82,7 @@ After running, the result is:
 
 The meanings of the parameters are as follows:
 - `input_path`: represents the path of the input image to be predicted
-- `dt_polys`: represents the predicted text detection boxes, where each text detection box contains multiple vertices of a polygon. Each vertex is a tuple of two elements, representing the x and y coordinates of the vertex respectively
+- `dt_polys`: represents the predicted text detection boxes, where each text detection box contains multiple vertices of a polygon. Each vertex is a list of two elements, representing the x and y coordinates of the vertex respectively
 - `dt_scores`: represents the confidence scores of the predicted text detection boxes
 
 The visualization image is as follows:

docs/module_usage/tutorials/ocr_modules/seal_text_detection.md

@@ -81,7 +81,7 @@ for res in output:
 
 运行结果参数含义如下：
 - `input_path`：表示输入待预测图像的路径
-- `dt_polys`：表示预测的文本检测框，其中每个文本检测框包含一个多边形的多个顶点。其中每个顶点都是一个二元组，分别表示该顶点的x坐标和y坐标
+- `dt_polys`：表示预测的文本检测框，其中每个文本检测框包含一个多边形的多个顶点。其中每个顶点都是一个列表，分别表示该顶点的x坐标和y坐标
 - `dt_scores`：表示预测的文本检测框的置信度
 
 

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

@@ -79,7 +79,7 @@ After running, the result obtained is:
 The meanings of the running result parameters are as follows:
 - `input_path`: Indicates the path of the input image to be predicted.
 - `page_index`: If the input is a PDF file, it indicates which page of the PDF it is; otherwise, it is `None`.
-- `dt_polys`: Indicates the predicted text detection boxes, where each text detection box contains four vertices of a quadrilateral. Each vertex is a tuple representing the x and y coordinates of the vertex.
+- `dt_polys`: Indicates the predicted text detection boxes, where each text detection box contains four vertices of a quadrilateral. Each vertex is a list representing the x and y coordinates of the vertex.
 - `dt_scores`: Indicates the confidence scores of the predicted text detection boxes.
 
 The visualization image is as follows:

docs/module_usage/tutorials/ocr_modules/text_detection.md

@@ -97,7 +97,7 @@ for res in output:
 运行结果参数含义如下：
 - `input_path`：表示输入待预测图像的路径
 - `page_index`：如果输入是PDF文件，则表示当前是PDF的第几页，否则为 `None`
-- `dt_polys`：表示预测的文本检测框，其中每个文本检测框包含一个四边形的四个顶点。其中每个顶点都是一个二元组，分别表示该顶点的x坐标和y坐标
+- `dt_polys`：表示预测的文本检测框，其中每个文本检测框包含一个四边形的四个顶点。其中每个顶点都是一个列表，分别表示该顶点的x坐标和y坐标
 - `dt_scores`：表示预测的文本检测框的置信度
 
 可视化图片如下：

docs/pipeline_usage/tutorials/video_pipelines/video_detection.en.md

@@ -8,7 +8,7 @@ comments: true
 
 Video detection is a technology that identifies and locates specific objects or events in video content. It is widely used in fields such as security surveillance, traffic management, and behavior analysis. This technology can capture and analyze dynamic changes in videos in real-time, such as human activities, vehicle movements, and abnormal events. Through deep learning models, video detection can efficiently extract spatial and temporal features from videos, achieving accurate recognition and localization. Video detection not only enhances the intelligence of surveillance systems but also provides important support for improving safety and operational efficiency. With the development of technology, video detection will play a key role in more scenarios.
 
-<img src="https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/images/yowo.jpg">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/video_detection/yowo.jpg">
 
 <b>The video detection pipeline</b><b> includes a video detection module</b> with the following models.
 
@@ -32,21 +32,8 @@ YOWO is a single-stage network with two branches. One branch extracts spatial fe
 
 </table>
 
-**Test Environment Description**:
+**Test Dataset**: <a href="http://www.thumos.info/download.html">UCF101-24</a> test dataset.
 
-- **Performance Test Environment**
-  - **Test Dataset**: <a href="http://www.thumos.info/download.html">UCF101-24</a> test dataset.
-  - **Hardware Configuration**:
-    - GPU: NVIDIA Tesla T4
-    - CPU: Intel Xeon Gold 6271C @ 2.60GHz
-    - Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
-
-- **Inference Mode Description**
-
-| Mode        | GPU Configuration                        | CPU Configuration | Acceleration Technology Combination                   |
-|-------------|----------------------------------------|-------------------|---------------------------------------------------|
-| Normal Mode | FP32 Precision / No TRT Acceleration   | FP32 Precision / 8 Threads | PaddleInference                                 |
-| High-Performance Mode | Optimal combination of pre-selected precision types and acceleration strategies | FP32 Precision / 8 Threads | Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.) |
 
 ## 2. Quick Start
 

docs/pipeline_usage/tutorials/video_pipelines/video_detection.md

@@ -8,7 +8,7 @@ comments: true
 
 视频检测是一种识别和定位视频内容中特定对象或事件的技术，广泛应用于安防监控、交通管理和行为分析等领域。该技术能够实时捕捉和分析视频中的动态变化，如人员活动、车辆流动及异常事件等。通过深度学习模型，视频检测能够高效提取视频中的空间和时间特征，实现精准识别和定位。视频检测不仅提升了监控系统的智能化程度，还为提高安全性和运营效率提供了重要支撑。随着技术的发展，视频检测将在更多场景中发挥关键作用。
 
-<img src="https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/images/yowo.jpg">
+<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipelines/video_detection/yowo.jpg">
 
 <b>视频检测</b><b>产线中包含视频检测模块</b>包含的模型如下。
 
@@ -33,21 +33,9 @@ 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
 
 </table>
 
-**测试环境说明：**
+**测试数据集**：<a href="http://www.thumos.info/download.html">UCF101-24</a> 测试数据集。
 
-- **性能测试环境**
-  - **测试数据集**：<a href="http://www.thumos.info/download.html">UCF101-24</a> test数据集。
-  - **硬件配置**：
-    - GPU：NVIDIA Tesla T4
-    - CPU：Intel Xeon Gold 6271C @ 2.60GHz
-    - 其他环境：Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
 
-- **推理模式说明**
-
-| 模式        | GPU配置                          | CPU配置          | 加速技术组合                                |
-|-------------|----------------------------------|------------------|---------------------------------------------|
-| 常规模式    | FP32精度 / 无TRT加速             | FP32精度 / 8线程       | PaddleInference                             |
-| 高性能模式  | 选择先验精度类型和加速策略的最优组合         | FP32精度 / 8线程       | 选择先验最优后端（Paddle/OpenVINO/TRT等） |
 
 ## 2. 快速开始