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Fix en docs (#3309)

* fix OVS doc

* fix modules docs

* fix tutorial docs

* fix sth

* fix sth

* fix_en_doxs
Zhang Zelun 9 月之前
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feecb58778

+ 2 - 2
docs/module_usage/tutorials/cv_modules/instance_segmentation.en.md

@@ -235,7 +235,7 @@ Related methods, parameters, and explanations are as follows:
 </table>
 </table>
 
 
 * The `model_name` must be specified. After specifying `model_name`, the default model parameters built into PaddleX are used. If `model_dir` is specified, the user-defined model is used.
 * The `model_name` must be specified. After specifying `model_name`, the default model parameters built into PaddleX are used. If `model_dir` is specified, the user-defined model is used.
-* `threshold` is the threshold for filtering low-confidence objects. The default is `None`, which means using the settings from the previous layer. The priority of parameter settings from highest to lowest is: `predict parameter &gt; create_model initialization &gt; yaml configuration file`.
+* `threshold` is the threshold for filtering low-confidence objects. The default is `None`, which means using the settings from the lower priority. The priority of parameter settings from highest to lowest is: `predict parameter > create_model initialization > yaml configuration file`.
 
 
 * The `predict()` method of the general instance segmentation model is called for inference prediction. The `predict()` method has parameters `input`, `batch_size`, and `threshold`, which are explained as follows:
 * The `predict()` method of the general instance segmentation model is called for inference prediction. The `predict()` method has parameters `input`, `batch_size`, and `threshold`, which are explained as follows:
 
 
@@ -277,7 +277,7 @@ Related methods, parameters, and explanations are as follows:
 <td><code>float</code>/<code>None</code></td>
 <td><code>float</code>/<code>None</code></td>
 <td>
 <td>
 <ul>
 <ul>
-<li><b>None</b>, indicating the use of settings from the previous layer. The priority of parameter settings from highest to lowest is: <code>predict parameter &gt; create_model initialization &gt; yaml configuration file</code></li>
+<li><b>None</b>, indicating the use of settings from the lower priority. The priority of parameter settings from highest to lowest is: <code>predict parameter &gt; create_model initialization &gt; yaml configuration file</code></li>
 <li><b>float</b>, such as 0.5, indicating the use of <code>0.5</code> as the threshold for filtering low-confidence objects during inference</li>
 <li><b>float</b>, such as 0.5, indicating the use of <code>0.5</code> as the threshold for filtering low-confidence objects during inference</li>
 </ul>
 </ul>
 </td>
 </td>

+ 2 - 2
docs/module_usage/tutorials/cv_modules/mainbody_detection.en.md

@@ -106,7 +106,7 @@ Related methods, parameters, and explanations are as follows:
 </table>
 </table>
 
 
 * The `model_name` must be specified. After specifying `model_name`, the default model parameters built into PaddleX are used. If `model_dir` is specified, the user-defined model is used.
 * The `model_name` must be specified. After specifying `model_name`, the default model parameters built into PaddleX are used. If `model_dir` is specified, the user-defined model is used.
-* `threshold` is the threshold for filtering low-confidence objects. The default is `None`, which means using the settings from the previous layer. The priority of parameter settings from highest to lowest is: `predict parameter > create_model initialization > yaml configuration file`. Currently, two types of threshold settings are supported:
+* `threshold` is the threshold for filtering low-confidence objects. The default is `None`, which means using the settings from the lower priority. The priority of parameter settings from highest to lowest is: `predict parameter > create_model initialization > yaml configuration file`. Currently, two types of threshold settings are supported:
   * `float`, using the same threshold for all classes.
   * `float`, using the same threshold for all classes.
   * `dict`, where the key is the class ID and the value is the threshold, allowing different thresholds for different classes. Since main body detection is a single-class detection, this setting is not required.
   * `dict`, where the key is the class ID and the value is the threshold, allowing different thresholds for different classes. Since main body detection is a single-class detection, this setting is not required.
 
 
@@ -150,7 +150,7 @@ Related methods, parameters, and explanations are as follows:
 <td><code>float</code>/<code>dict</code>/<code>None</code></td>
 <td><code>float</code>/<code>dict</code>/<code>None</code></td>
 <td>
 <td>
 <ul>
 <ul>
-<li><b>None</b>, indicating the use of settings from the previous layer. The priority of parameter settings from highest to lowest is: <code>predict parameter > create_model initialization > yaml configuration file</code></li>
+<li><b>None</b>, indicating the use of settings from the lower priority. The priority of parameter settings from highest to lowest is: <code>predict parameter > create_model initialization > yaml configuration file</code></li>
 <li><b>float</b>, such as 0.5, indicating the use of <code>0.5</code> as the threshold for filtering low-confidence objects during inference</li>
 <li><b>float</b>, such as 0.5, indicating the use of <code>0.5</code> as the threshold for filtering low-confidence objects during inference</li>
 <li><b>dict</b>, such as <code>{0: 0.5, 1: 0.35}</code>, indicating the use of 0.5 as the threshold for class 0 and 0.35 for class 1 during inference. Since main body detection is a single-class detection, this setting is not required.</li>
 <li><b>dict</b>, such as <code>{0: 0.5, 1: 0.35}</code>, indicating the use of 0.5 as the threshold for class 0 and 0.35 for class 1 during inference. Since main body detection is a single-class detection, this setting is not required.</li>
 </ul>
 </ul>

+ 2 - 2
docs/module_usage/tutorials/cv_modules/open_vocabulary_detection.en.md

@@ -105,7 +105,7 @@ Related methods, parameters, and explanations are as follows:
 </table>
 </table>
 
 
 * The `model_name` must be specified. After specifying `model_name`, the model parameters built into PaddleX will be used by default. If `model_dir` is specified, the user-defined model will be used.
 * The `model_name` must be specified. After specifying `model_name`, the model parameters built into PaddleX will be used by default. If `model_dir` is specified, the user-defined model will be used.
-* `thresholds` is the filtering threshold used by the model. The default is None, which means using the settings from the previous layer. The priority of parameter settings from high to low is: `predict parameter input > create_model initialization input > yaml configuration file setting`.
+* `thresholds` is the filtering threshold used by the model. The default is None, which means using the settings from the lower priority. The priority of parameter settings from high to low is: `predict parameter input > create_model initialization input > yaml configuration file setting`.
   * The GroundingDINO series of models require two thresholds during inference: box_threshold (default 0.3) and text_threshold (default 0.25). The parameter input format is `{"box_threshold": 0.3, "text_threshold": 0.25}`.
   * The GroundingDINO series of models require two thresholds during inference: box_threshold (default 0.3) and text_threshold (default 0.25). The parameter input format is `{"box_threshold": 0.3, "text_threshold": 0.25}`.
 
 
 * The `predict()` method of the open-vocabulary object detection model is called for inference prediction. The parameters of the `predict()` method are `input`, `batch_size`, `thresholds`, and `prompt`, with specific explanations as follows:
 * The `predict()` method of the open-vocabulary object detection model is called for inference prediction. The parameters of the `predict()` method are `input`, `batch_size`, `thresholds`, and `prompt`, with specific explanations as follows:
@@ -148,7 +148,7 @@ Related methods, parameters, and explanations are as follows:
 <td><code>dict</code>/<code>None</code></td>
 <td><code>dict</code>/<code>None</code></td>
 <td>
 <td>
 <ul>
 <ul>
-  <li><b>None</b>, indicating the use of the settings from the previous layer. The priority of parameter settings from high to low is: <code>predict parameter input > create_model initialization input > yaml configuration file setting</code></li>
+  <li><b>None</b>, indicating the use of the settings from the lower priority. The priority of parameter settings from high to low is: <code>predict parameter input > create_model initialization input > yaml configuration file setting</code></li>
   <li><b>dict</b>, such as <code>{"box_threshold": 0.3, "text_threshold": 0.25}</code>, indicating that the box_threshold is set to 0.3 and the text_threshold is set to 0.25 during inference</li>
   <li><b>dict</b>, such as <code>{"box_threshold": 0.3, "text_threshold": 0.25}</code>, indicating that the box_threshold is set to 0.3 and the text_threshold is set to 0.25 during inference</li>
 </ul>
 </ul>
 </td>
 </td>

+ 3 - 4
docs/module_usage/tutorials/cv_modules/rotated_object_detection.en.md

@@ -28,7 +28,6 @@ Rotated object detection is a derivative of the object detection module, specifi
 </tr>
 </tr>
 </table>
 </table>
 <p><b>Note: The above accuracy metrics are on the <a href="https://captain-whu.github.io/DOTA/">DOTA</a> validation set mAP(0.5:0.95)。All model GPU inference times are based on an NVIDIA TRX2080 Ti machine, with precision type F16, and CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32.</b></p>
 <p><b>Note: The above accuracy metrics are on the <a href="https://captain-whu.github.io/DOTA/">DOTA</a> validation set mAP(0.5:0.95)。All model GPU inference times are based on an NVIDIA TRX2080 Ti machine, with precision type F16, and CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz, with 8 threads and precision type FP32.</b></p>
-> ❗ The above listed are the rotated object detection models currently supported by paddleX,actually PaddleDetection supports<b>10</b>rotated object detection models, For a detailed model list, please refer to <a href="https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.8/configs/rotate">PaddleDetection</a>
 
 
 
 
 ## III. Quick Integration
 ## III. Quick Integration
@@ -111,11 +110,11 @@ Related methods and parameter explanations are as follows:
 
 
 * The `model_name` must be specified. After specifying `model_name`, the model parameters built into PaddleX will be used by default. If `model_dir` is specified, the user-defined model will be used.
 * The `model_name` must be specified. After specifying `model_name`, the model parameters built into PaddleX will be used by default. If `model_dir` is specified, the user-defined model will be used.
 
 
-* `threshold` is the threshold for filtering low-score objects. The default is `None`, which means using the settings from the previous layer. The priority of parameter settings from high to low is: `predict parameter input > create_model initialization > yaml configuration file setting`. Currently, two threshold setting methods are supported:
+* `threshold` is the threshold for filtering low-score objects. The default is `None`, which means using the settings from the lower priority. The priority of parameter settings from high to low is: `predict parameter input > create_model initialization > yaml configuration file setting`. Currently, two threshold setting methods are supported:
   * `float`: Use the same threshold for all classes.
   * `float`: Use the same threshold for all classes.
   * `dict`: The key is the class ID, and the value is the threshold, allowing different thresholds for different classes.
   * `dict`: The key is the class ID, and the value is the threshold, allowing different thresholds for different classes.
 
 
-* `img_size` is the resolution used by the model for prediction. The default is `None`, which means using the settings from the previous layer. The priority of parameter settings from high to low is: `create_model initialization > yaml configuration file setting`.
+* `img_size` is the resolution used by the model for prediction. The default is `None`, which means using the settings from the lower priority. The priority of parameter settings from high to low is: `create_model initialization > yaml configuration file setting`.
 
 
 * The `predict()` method of the rotated object detection model is called for inference prediction. The parameters of the `predict()` method are `input`, `batch_size`, and `threshold`, with specific explanations as follows:
 * The `predict()` method of the rotated object detection model is called for inference prediction. The parameters of the `predict()` method are `input`, `batch_size`, and `threshold`, with specific explanations as follows:
 
 
@@ -157,7 +156,7 @@ Related methods and parameter explanations are as follows:
 <td><code>float</code>/<code>dict</code>/<code>None</code></td>
 <td><code>float</code>/<code>dict</code>/<code>None</code></td>
 <td>
 <td>
 <ul>
 <ul>
-  <li><b>None</b>, indicating the use of settings from the previous layer. The priority of parameter settings from high to low is: <code>predict parameter input > create_model initialization > yaml configuration file setting</code></li>
+  <li><b>None</b>, indicating the use of settings from the lower priority. The priority of parameter settings from high to low is: <code>predict parameter input > create_model initialization > yaml configuration file setting</code></li>
   <li><b>float</b>, such as 0.5, indicating the use of <code>0.5</code> as the threshold for all classes during inference</li>
   <li><b>float</b>, such as 0.5, indicating the use of <code>0.5</code> as the threshold for all classes during inference</li>
   <li><b>dict</b>, such as <code>{0: 0.5, 1: 0.35}</code>, indicating the use of 0.5 as the threshold for class 0 and 0.35 as the threshold for class 1 during inference</li>
   <li><b>dict</b>, such as <code>{0: 0.5, 1: 0.35}</code>, indicating the use of 0.5 as the threshold for class 0 and 0.35 as the threshold for class 1 during inference</li>
 </ul>
 </ul>

+ 2 - 2
docs/module_usage/tutorials/cv_modules/semantic_segmentation.en.md

@@ -279,7 +279,7 @@ Related methods, parameters, and explanations are as follows:
 
 
 * The `model_name` must be specified. After specifying `model_name`, the built-in model parameters of PaddleX are used by default. If `model_dir` is specified, the user-defined model is used.
 * The `model_name` must be specified. After specifying `model_name`, the built-in model parameters of PaddleX are used by default. If `model_dir` is specified, the user-defined model is used.
 
 
-* The `target_size` is specified during initialization to set the resolution for model inference. The default value is `None`. `-1` indicates that the original image size is used for inference, and `None` indicates that the settings from the previous layer are used. The priority order for parameter settings is: `predict parameter &gt; create_model initialization &gt; yaml configuration file`.
+* The `target_size` is specified during initialization to set the resolution for model inference. The default value is `None`. `-1` indicates that the original image size is used for inference, and `None` indicates that the settings from the lower priority are used. The priority order for parameter settings is: `predict parameter > create_model initialization > yaml configuration file`.
 
 
 * The `predict()` method of the general semantic segmentation model is called for inference and prediction. The parameters of the `predict()` method are `input`, `batch_size`, and `target_size`, with specific explanations as follows:
 * The `predict()` method of the general semantic segmentation model is called for inference and prediction. The parameters of the `predict()` method are `input`, `batch_size`, and `target_size`, with specific explanations as follows:
 
 
@@ -322,7 +322,7 @@ Related methods, parameters, and explanations are as follows:
 <td>
 <td>
 <ul>
 <ul>
 <li><b>-1</b>, indicating inference using the original image size</li>
 <li><b>-1</b>, indicating inference using the original image size</li>
-<li><b>None</b>, indicating the settings from the previous layer are used. The priority order for parameter settings is: <code>predict parameter &gt; create_model initialization &gt; yaml configuration file</code></li>
+<li><b>None</b>, indicating the settings from the lower priority are used. The priority order for parameter settings is: <code>predict parameter &gt; create_model initialization &gt; yaml configuration file</code></li>
 <li><b>int</b>, such as 512, indicating inference using a resolution of <code>(512, 512)</code></li>
 <li><b>int</b>, such as 512, indicating inference using a resolution of <code>(512, 512)</code></li>
 <li><b>tuple</b>, such as (512, 1024), indicating inference using a resolution of <code>(512, 1024)</code></li>
 <li><b>tuple</b>, such as (512, 1024), indicating inference using a resolution of <code>(512, 1024)</code></li>
 </ul>
 </ul>

+ 2 - 2
docs/module_usage/tutorials/cv_modules/small_object_detection.en.md

@@ -124,7 +124,7 @@ Related methods, parameters, and explanations are as follows:
 </table>
 </table>
 
 
 * The `model_name` must be specified. After specifying `model_name`, the default model parameters built into PaddleX are used. If `model_dir` is specified, the user-defined model is used.
 * The `model_name` must be specified. After specifying `model_name`, the default model parameters built into PaddleX are used. If `model_dir` is specified, the user-defined model is used.
-* `threshold` is the threshold for filtering low-confidence objects. The default is `None`, which means using the settings from the previous layer. The priority of parameter settings from highest to lowest is: `predict parameter > create_model initialization > yaml configuration file`. Currently, two types of threshold settings are supported:
+* `threshold` is the threshold for filtering low-confidence objects. The default is `None`, which means using the settings from the lower priority. The priority of parameter settings from highest to lowest is: `predict parameter > create_model initialization > yaml configuration file`. Currently, two types of threshold settings are supported:
   * `float`, using the same threshold for all classes.
   * `float`, using the same threshold for all classes.
   * `dict`, where the key is the class ID and the value is the threshold, allowing different thresholds for different classes.
   * `dict`, where the key is the class ID and the value is the threshold, allowing different thresholds for different classes.
 
 
@@ -168,7 +168,7 @@ Related methods, parameters, and explanations are as follows:
 <td><code>float</code>/<code>dict</code>/<code>None</code></td>
 <td><code>float</code>/<code>dict</code>/<code>None</code></td>
 <td>
 <td>
 <ul>
 <ul>
-<li><b>None</b>, indicating the use of settings from the previous layer. The priority of parameter settings from highest to lowest is: <code>predict parameter > create_model initialization > yaml configuration file</code></li>
+<li><b>None</b>, indicating the use of settings from the lower priority. The priority of parameter settings from highest to lowest is: <code>predict parameter > create_model initialization > yaml configuration file</code></li>
 <li><b>float</b>, such as 0.5, indicating the use of <code>0.5</code> as the threshold for filtering low-confidence objects during inference</li>
 <li><b>float</b>, such as 0.5, indicating the use of <code>0.5</code> as the threshold for filtering low-confidence objects during inference</li>
 <li><b>dict</b>, such as <code>{0: 0.5, 1: 0.35}</code>, indicating the use of 0.5 as the threshold for class 0 and 0.35 for class 1 during inference.</li>
 <li><b>dict</b>, such as <code>{0: 0.5, 1: 0.35}</code>, indicating the use of 0.5 as the threshold for class 0 and 0.35 for class 1 during inference.</li>
 </ul>
 </ul>

+ 3 - 3
docs/module_usage/tutorials/cv_modules/vehicle_detection.en.md

@@ -110,7 +110,7 @@ Related methods, parameters, and explanations are as follows:
 
 
 * The `model_name` must be specified. After specifying `model_name`, the built-in model parameters of PaddleX are used by default. If `model_dir` is specified, the user-defined model is used.
 * The `model_name` must be specified. After specifying `model_name`, the built-in model parameters of PaddleX are used by default. If `model_dir` is specified, the user-defined model is used.
 
 
-* The `threshold` is the threshold for filtering low-score objects. The default value is `None`, indicating that the settings from the previous layer are used. The priority order for parameter settings is: `predict parameter &gt; create_model initialization &gt; yaml configuration file`. Currently, two types of threshold settings are supported:
+* The `threshold` is the threshold for filtering low-score objects. The default value is `None`, indicating that the settings from the lower priority are used. The priority order for parameter settings is: `predict parameter > create_model initialization > yaml configuration file`. Currently, two types of threshold settings are supported:
   * `float`: Use the same threshold for all classes.
   * `float`: Use the same threshold for all classes.
   * `dict`: The key is the class ID, and the value is the threshold. Different thresholds can be set for different classes. For vehicle detection, which is a single-class detection task, this setting is not required.
   * `dict`: The key is the class ID, and the value is the threshold. Different thresholds can be set for different classes. For vehicle detection, which is a single-class detection task, this setting is not required.
 
 
@@ -154,7 +154,7 @@ Related methods, parameters, and explanations are as follows:
 <td><code>float</code>/<code>dict</code>/<code>None</code></td>
 <td><code>float</code>/<code>dict</code>/<code>None</code></td>
 <td>
 <td>
 <ul>
 <ul>
-<li><b>None</b>, indicating the settings from the previous layer are used. The priority order for parameter settings is: <code>predict parameter &gt; create_model initialization &gt; yaml configuration file</code></li>
+<li><b>None</b>, indicating the settings from the lower priority are used. The priority order for parameter settings is: <code>predict parameter &gt; create_model initialization &gt; yaml configuration file</code></li>
 <li><b>float</b>, such as 0.5, indicating the threshold of 0.5 is used for filtering low-score objects during inference</li>
 <li><b>float</b>, such as 0.5, indicating the threshold of 0.5 is used for filtering low-score objects during inference</li>
 <li><b>dict</b>, such as <code>{0: 0.5, 1: 0.35}</code>, indicating a threshold of 0.5 for class 0 and 0.35 for class 1 during inference. Vehicle detection is a single-class detection task and does not require this setting.</li>
 <li><b>dict</b>, such as <code>{0: 0.5, 1: 0.35}</code>, indicating a threshold of 0.5 for class 0 and 0.35 for class 1 during inference. Vehicle detection is a single-class detection task and does not require this setting.</li>
 </ul>
 </ul>
@@ -430,4 +430,4 @@ Other related parameters can be set by modifying the fields under `Global` and `
 
 
 #### 4.4.2 Model Integration
 #### 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.
 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.
-</table>
+</table>

+ 1 - 1
docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.en.md

@@ -1034,7 +1034,7 @@ SubModules:
   InstanceSegmentation:
   InstanceSegmentation:
     module_name: instance_segmentation
     module_name: instance_segmentation
     model_name: Mask-RT-DETR-S
     model_name: Mask-RT-DETR-S
-    model_dir: null # 替换为微调后的实例分割模型权重路径
+    model_dir: null # Here replaced with the newly fine-tuned weight path.
     batch_size: 1
     batch_size: 1
     threshold: 0.5
     threshold: 0.5
 ```
 ```

+ 5 - 5
docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.en.md

@@ -537,9 +537,9 @@ print(result["detectedObjects"])
 <details><summary>C++</summary>
 <details><summary>C++</summary>
 
 
 <pre><code class="language-cpp">#include &lt;iostream&gt;
 <pre><code class="language-cpp">#include &lt;iostream&gt;
-#include &quot;cpp-httplib/httplib.h&quot; // <url id="cu9pu8852ceh1d3h24gg" type="url" status="parsed" title="GitHub - Huiyicc/cpp-httplib: A C++ header-only HTTP/HTTPS server and client library" wc="15064">https://github.com/Huiyicc/cpp-httplib</url> 
-#include &quot;nlohmann/json.hpp&quot; // <url id="cu9pu8852ceh1d3h24h0" type="url" status="parsed" title="GitHub - nlohmann/json: JSON for Modern C++" wc="80311">https://github.com/nlohmann/json</url> 
-#include &quot;base64.hpp&quot; // <url id="cu9pu8852ceh1d3h24hg" type="url" status="parsed" title="GitHub - tobiaslocker/base64: A modern C++ base64 encoder / decoder" wc="2293">https://github.com/tobiaslocker/base64</url> 
+#include &quot;cpp-httplib/httplib.h&quot; // <url id="cu9pu8852ceh1d3h24gg" type="url" status="parsed" title="GitHub - Huiyicc/cpp-httplib: A C++ header-only HTTP/HTTPS server and client library" wc="15064">https://github.com/Huiyicc/cpp-httplib</url>
+#include &quot;nlohmann/json.hpp&quot; // <url id="cu9pu8852ceh1d3h24h0" type="url" status="parsed" title="GitHub - nlohmann/json: JSON for Modern C++" wc="80311">https://github.com/nlohmann/json</url>
+#include &quot;base64.hpp&quot; // <url id="cu9pu8852ceh1d3h24hg" type="url" status="parsed" title="GitHub - tobiaslocker/base64: A modern C++ base64 encoder / decoder" wc="2293">https://github.com/tobiaslocker/base64</url>
 
 
 int main() {
 int main() {
     httplib::Client client(&quot;localhost:8080&quot;);
     httplib::Client client(&quot;localhost:8080&quot;);
@@ -969,7 +969,7 @@ SubModules:
   RotatedObjectDetection:
   RotatedObjectDetection:
     module_name: rotated_object_detection
     module_name: rotated_object_detection
     model_name: PP-YOLOE-R-L
     model_name: PP-YOLOE-R-L
-    model_dir: null # 替换为微调后的旋转目标检测模型权重路径
+    model_dir: null # Here replaced with the newly fine-tuned weight path.
     batch_size: 1
     batch_size: 1
     threshold: 0.5
     threshold: 0.5
 ```
 ```
@@ -989,4 +989,4 @@ paddlex --pipeline rotated_object_detection \
         --device npu:0
         --device npu:0
 ```
 ```
 
 
-If you want to use rotated object detection on more types of hardware, please refer to the [PaddleX Multi-Hardware Usage Guide](../../../other_devices_support/multi_devices_use_guide.en.md).
+If you want to use rotated object detection on more types of hardware, please refer to the [PaddleX Multi-Hardware Usage Guide](../../../other_devices_support/multi_devices_use_guide.en.md).

+ 1 - 1
docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.en.md

@@ -990,7 +990,7 @@ SubModules:
   SemanticSegmentation:
   SemanticSegmentation:
     module_name: semantic_segmentation
     module_name: semantic_segmentation
     model_name: PP-LiteSeg-T
     model_name: PP-LiteSeg-T
-    model_dir: null # 替换为微调后的语义分割模型权重路径
+    model_dir: null # Here replaced with the newly fine-tuned weight path.
     batch_size: 1
     batch_size: 1
     target_size: None
     target_size: None
 ```
 ```

+ 1 - 1
docs/pipeline_usage/tutorials/cv_pipelines/small_object_detection.en.md

@@ -886,7 +886,7 @@ SubModules:
   SmallObjectDetection:
   SmallObjectDetection:
     module_name: small_object_detection
     module_name: small_object_detection
     model_name: PP-YOLOE_plus_SOD-L
     model_name: PP-YOLOE_plus_SOD-L
-    model_dir: null # 替换为微调后的小目标检测模型权重路径
+    model_dir: null # Here replaced with the newly fine-tuned weight path.
     batch_size: 1
     batch_size: 1
     threshold: 0.5
     threshold: 0.5
 ```
 ```