Merge branch 'develop' of https://github.com/PaddlePaddle/FastDeploy into huawei

cmake/paddle_inference.cmake

@@ -48,7 +48,7 @@ endif(WIN32)
 
 
 set(PADDLEINFERENCE_URL_BASE "https://bj.bcebos.com/fastdeploy/third_libs/")
-set(PADDLEINFERENCE_VERSION "2.4-dev4")
+set(PADDLEINFERENCE_VERSION "2.4-dev3")
 if(WIN32)
   if (WITH_GPU)
     set(PADDLEINFERENCE_FILE "paddle_inference-win-x64-gpu-trt-${PADDLEINFERENCE_VERSION}.zip")

examples/vision/classification/paddleclas/android/README.md

@@ -125,7 +125,7 @@ option.enableLiteFp16();
 model.init(modelFile, paramFile, configFile, option);
 // Bitmap读取、模型预测、资源释放 同上 ...
 ```
-更详细的用法请参考 [MainActivity](./app/src/main/java/com/baidu/paddle/fastdeploy/examples/MainActivity.java#L207) 中的用法
+更详细的用法请参考 [MainActivity](./app/src/main/java/com/baidu/paddle/fastdeploy/app/examples/classification/ClassificationMainActivity.java) 中的用法
 
 ## 替换 FastDeploy 预测库和模型  
 替换FastDeploy预测库和模型的步骤非常简单。预测库所在的位置为 `app/libs/fastdeploy-android-xxx-shared`，其中 `xxx` 表示当前您使用的预测库版本号。模型所在的位置为，`app/src/main/assets/models/MobileNetV1_x0_25_infer`。  

examples/vision/ocr/PP-OCRv2/android/README.md

@@ -27,14 +27,14 @@
   | ---     | --- | --- |
 | ![app_pic](https://user-images.githubusercontent.com/14995488/203484427-83de2316-fd60-4baf-93b6-3755f9b5559d.jpg)   | ![app_res](https://user-images.githubusercontent.com/14995488/203495616-af42a5b7-d3bc-4fce-8d5e-2ed88454f618.jpg) |  ![app_setup](https://user-images.githubusercontent.com/14995488/203484436-57fdd041-7dcc-4e0e-b6cb-43e5ac1e729b.jpg) |  
 
-### PP-OCRv2 & PP-OCRv3 Java API 说明
+### PP-OCRv2 Java API 说明
 
 - 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。 PP-OCR初始化参数说明如下：
   - modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
   - paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
   - labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 ppocr_keys_v1.txt，每一行包含一个label
   - option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
-    与其他模型不同的是，PP-OCRv2 和 PP-OCRv3 包含 DBDetector、Classifier和Recognizer等基础模型，以及PPOCRv2和PPOCRv3等pipeline类型。
+    与其他模型不同的是，PP-OCRv2 包含 DBDetector、Classifier和Recognizer等基础模型，以及pipeline类型。
 ```java
 // 构造函数: constructor w/o label file
 public DBDetector(String modelFile, String paramsFile);
@@ -47,10 +47,6 @@ public PPOCRv2();  // 空构造函数，之后可以调用init初始化
 // Constructor w/o classifier
 public PPOCRv2(DBDetector detModel, Recognizer recModel);
 public PPOCRv2(DBDetector detModel, Classifier clsModel, Recognizer recModel);
-public PPOCRv3();  // 空构造函数，之后可以调用init初始化
-// Constructor w/o classifier
-public PPOCRv3(DBDetector detModel, Recognizer recModel);
-public PPOCRv3(DBDetector detModel, Classifier clsModel, Recognizer recModel);
 ```  
 - 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
 ```java
@@ -66,6 +62,126 @@ public boolean release(); // 释放native资源
 public boolean initialized(); // 检查是否初始化成功
 ```
 
+- RuntimeOption设置说明
+
+```java  
+public void enableLiteFp16(); // 开启fp16精度推理
+public void disableLiteFP16(); // 关闭fp16精度推理
+public void enableLiteInt8(); // 开启int8精度推理，针对量化模型
+public void disableLiteInt8(); // 关闭int8精度推理
+public void setCpuThreadNum(int threadNum); // 设置线程数
+public void setLitePowerMode(LitePowerMode mode);  // 设置能耗模式
+public void setLitePowerMode(String modeStr);  // 通过字符串形式设置能耗模式
+```
+
+- 模型结果OCRResult说明
+```java
+public class OCRResult {
+    public int[][] mBoxes; // 表示单张图片检测出来的所有目标框坐标，每个框以8个int数值依次表示框的4个坐标点，顺序为左下，右下，右上，左上
+    public String[] mText; // 表示多个文本框内被识别出来的文本内容
+    public float[] mRecScores; // 表示文本框内识别出来的文本的置信度
+    public float[] mClsScores; // 表示文本框的分类结果的置信度
+    public int[] mClsLabels; // 表示文本框的方向分类类别
+    public boolean mInitialized = false; // 检测结果是否有效
+}  
+```
+其他参考：C++/Python对应的OCRResult说明: [api/vision_results/ocr_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/ocr_result.md)
+
+
+- 模型调用示例1：使用构造函数
+```java  
+import java.nio.ByteBuffer;
+import android.graphics.Bitmap;
+import android.opengl.GLES20;
+
+import com.baidu.paddle.fastdeploy.RuntimeOption;
+import com.baidu.paddle.fastdeploy.LitePowerMode;
+import com.baidu.paddle.fastdeploy.vision.OCRResult;
+import com.baidu.paddle.fastdeploy.vision.ocr.Classifier;
+import com.baidu.paddle.fastdeploy.vision.ocr.DBDetector;
+import com.baidu.paddle.fastdeploy.vision.ocr.Recognizer;
+
+// 模型路径
+String detModelFile = "ch_PP-OCRv2_det_infer/inference.pdmodel";
+String detParamsFile = "ch_PP-OCRv2_det_infer/inference.pdiparams";
+String clsModelFile = "ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel";
+String clsParamsFile = "ch_ppocr_mobile_v2.0_cls_infer/inference.pdiparams";
+String recModelFile = "ch_PP-OCRv2_rec_infer/inference.pdmodel";
+String recParamsFile = "ch_PP-OCRv2_rec_infer/inference.pdiparams";
+String recLabelFilePath = "labels/ppocr_keys_v1.txt";
+// 设置RuntimeOption
+RuntimeOption detOption = new RuntimeOption();
+RuntimeOption clsOption = new RuntimeOption();
+RuntimeOption recOption = new RuntimeOption();
+detOption.setCpuThreadNum(2);
+clsOption.setCpuThreadNum(2);
+recOption.setCpuThreadNum(2);
+detOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+clsOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+recOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+detOption.enableLiteFp16();  
+clsOption.enableLiteFp16();  
+recOption.enableLiteFp16();  
+// 初始化模型
+DBDetector detModel = new DBDetector(detModelFile, detParamsFile, detOption);
+Classifier clsModel = new Classifier(clsModelFile, clsParamsFile, clsOption);
+Recognizer recModel = new Recognizer(recModelFile, recParamsFile, recLabelFilePath, recOption);
+PPOCRv2 model = new PPOCRv2(detModel, clsModel, recModel);
+
+// 读取图片: 以下仅为读取Bitmap的伪代码
+ByteBuffer pixelBuffer = ByteBuffer.allocate(width * height * 4);
+GLES20.glReadPixels(0, 0, width, height, GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, pixelBuffer);
+Bitmap ARGB8888ImageBitmap = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888);
+ARGB8888ImageBitmap.copyPixelsFromBuffer(pixelBuffer);
+
+// 模型推理
+OCRResult result = model.predict(ARGB8888ImageBitmap);  
+
+// 释放模型资源  
+model.release();
+```  
+
+- 模型调用示例2: 在合适的程序节点，手动调用init
+```java  
+// import 同上 ...
+import com.baidu.paddle.fastdeploy.RuntimeOption;
+import com.baidu.paddle.fastdeploy.LitePowerMode;
+import com.baidu.paddle.fastdeploy.vision.OCRResult;
+import com.baidu.paddle.fastdeploy.vision.ocr.Classifier;
+import com.baidu.paddle.fastdeploy.vision.ocr.DBDetector;
+import com.baidu.paddle.fastdeploy.vision.ocr.Recognizer;
+// 新建空模型
+PPOCRv2 model = new PPOCRv2();  
+// 模型路径
+String detModelFile = "ch_PP-OCRv2_det_infer/inference.pdmodel";
+String detParamsFile = "ch_PP-OCRv2_det_infer/inference.pdiparams";
+String clsModelFile = "ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel";
+String clsParamsFile = "ch_ppocr_mobile_v2.0_cls_infer/inference.pdiparams";
+String recModelFile = "ch_PP-OCRv2_rec_infer/inference.pdmodel";
+String recParamsFile = "ch_PP-OCRv2_rec_infer/inference.pdiparams";
+String recLabelFilePath = "labels/ppocr_keys_v1.txt";
+// 设置RuntimeOption
+RuntimeOption detOption = new RuntimeOption();
+RuntimeOption clsOption = new RuntimeOption();
+RuntimeOption recOption = new RuntimeOption();
+detOption.setCpuThreadNum(2);
+clsOption.setCpuThreadNum(2);
+recOption.setCpuThreadNum(2);
+detOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+clsOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+recOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+detOption.enableLiteFp16();  
+clsOption.enableLiteFp16();  
+recOption.enableLiteFp16();  
+// 使用init函数初始化
+DBDetector detModel = new DBDetector(detModelFile, detParamsFile, detOption);
+Classifier clsModel = new Classifier(clsModelFile, clsParamsFile, clsOption);
+Recognizer recModel = new Recognizer(recModelFile, recParamsFile, recLabelFilePath, recOption);
+model.init(detModel, clsModel, recModel);
+// Bitmap读取、模型预测、资源释放 同上 ...
+```
+更详细的用法请参考 [OcrMainActivity](./app/src/main/java/com/baidu/paddle/fastdeploy/app/examples/ocr/OcrMainActivity.java)中的用法
+
 ## 替换 FastDeploy SDK和模型
 替换FastDeploy预测库和模型的步骤非常简单。预测库所在的位置为 `app/libs/fastdeploy-android-sdk-xxx.aar`，其中 `xxx` 表示当前您使用的预测库版本号。模型所在的位置为，`app/src/main/assets/models`。
 - 替换FastDeploy Android SDK: 下载或编译最新的FastDeploy Android SDK，解压缩后放在 `app/libs` 目录下；详细配置文档可参考:

examples/vision/ocr/PP-OCRv3/android/README.md

@@ -27,14 +27,14 @@
   | ---     | --- | --- |
 | ![app_pic](https://user-images.githubusercontent.com/14995488/203484427-83de2316-fd60-4baf-93b6-3755f9b5559d.jpg)   | ![app_res](https://user-images.githubusercontent.com/14995488/203495616-af42a5b7-d3bc-4fce-8d5e-2ed88454f618.jpg) |  ![app_setup](https://user-images.githubusercontent.com/14995488/203484436-57fdd041-7dcc-4e0e-b6cb-43e5ac1e729b.jpg) |  
 
-### PP-OCRv2 & PP-OCRv3 Java API 说明
+### PP-OCRv3 Java API 说明
 
 - 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。 PP-OCR初始化参数说明如下：
   - modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
   - paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
   - labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 ppocr_keys_v1.txt，每一行包含一个label
   - option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
-    与其他模型不同的是，PP-OCRv2 和 PP-OCRv3 包含 DBDetector、Classifier和Recognizer等基础模型，以及PPOCRv2和PPOCRv3等pipeline类型。
+    与其他模型不同的是，PP-OCRv3 包含 DBDetector、Classifier和Recognizer等基础模型，以及pipeline类型。
 ```java
 // 构造函数: constructor w/o label file
 public DBDetector(String modelFile, String paramsFile);
@@ -43,10 +43,6 @@ public Classifier(String modelFile, String paramsFile);
 public Classifier(String modelFile, String paramsFile, RuntimeOption option);
 public Recognizer(String modelFile, String paramsFile, String labelPath);
 public Recognizer(String modelFile, String paramsFile,  String labelPath, RuntimeOption option);
-public PPOCRv2();  // 空构造函数，之后可以调用init初始化
-// Constructor w/o classifier
-public PPOCRv2(DBDetector detModel, Recognizer recModel);
-public PPOCRv2(DBDetector detModel, Classifier clsModel, Recognizer recModel);
 public PPOCRv3();  // 空构造函数，之后可以调用init初始化
 // Constructor w/o classifier
 public PPOCRv3(DBDetector detModel, Recognizer recModel);
@@ -66,6 +62,126 @@ public boolean release(); // 释放native资源
 public boolean initialized(); // 检查是否初始化成功
 ```
 
+- RuntimeOption设置说明
+
+```java  
+public void enableLiteFp16(); // 开启fp16精度推理
+public void disableLiteFP16(); // 关闭fp16精度推理
+public void enableLiteInt8(); // 开启int8精度推理，针对量化模型
+public void disableLiteInt8(); // 关闭int8精度推理
+public void setCpuThreadNum(int threadNum); // 设置线程数
+public void setLitePowerMode(LitePowerMode mode);  // 设置能耗模式
+public void setLitePowerMode(String modeStr);  // 通过字符串形式设置能耗模式
+```
+
+- 模型结果OCRResult说明
+```java
+public class OCRResult {
+  public int[][] mBoxes; // 表示单张图片检测出来的所有目标框坐标，每个框以8个int数值依次表示框的4个坐标点，顺序为左下，右下，右上，左上
+  public String[] mText; // 表示多个文本框内被识别出来的文本内容
+  public float[] mRecScores; // 表示文本框内识别出来的文本的置信度
+  public float[] mClsScores; // 表示文本框的分类结果的置信度
+  public int[] mClsLabels; // 表示文本框的方向分类类别
+  public boolean mInitialized = false; // 检测结果是否有效
+}  
+```
+其他参考：C++/Python对应的OCRResult说明: [api/vision_results/ocr_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/ocr_result.md)
+
+
+- 模型调用示例1：使用构造函数
+```java  
+import java.nio.ByteBuffer;
+import android.graphics.Bitmap;
+import android.opengl.GLES20;
+
+import com.baidu.paddle.fastdeploy.RuntimeOption;
+import com.baidu.paddle.fastdeploy.LitePowerMode;
+import com.baidu.paddle.fastdeploy.vision.OCRResult;
+import com.baidu.paddle.fastdeploy.vision.ocr.Classifier;
+import com.baidu.paddle.fastdeploy.vision.ocr.DBDetector;
+import com.baidu.paddle.fastdeploy.vision.ocr.Recognizer;
+
+// 模型路径
+String detModelFile = "ch_PP-OCRv3_det_infer/inference.pdmodel";
+String detParamsFile = "ch_PP-OCRv3_det_infer/inference.pdiparams";
+String clsModelFile = "ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel";
+String clsParamsFile = "ch_ppocr_mobile_v2.0_cls_infer/inference.pdiparams";
+String recModelFile = "ch_PP-OCRv3_rec_infer/inference.pdmodel";
+String recParamsFile = "ch_PP-OCRv3_rec_infer/inference.pdiparams";
+String recLabelFilePath = "labels/ppocr_keys_v1.txt";
+// 设置RuntimeOption
+RuntimeOption detOption = new RuntimeOption();
+RuntimeOption clsOption = new RuntimeOption();
+RuntimeOption recOption = new RuntimeOption();
+detOption.setCpuThreadNum(2);
+clsOption.setCpuThreadNum(2);
+recOption.setCpuThreadNum(2);
+detOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+clsOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+recOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+detOption.enableLiteFp16();  
+clsOption.enableLiteFp16();  
+recOption.enableLiteFp16();  
+// 初始化模型
+DBDetector detModel = new DBDetector(detModelFile, detParamsFile, detOption);
+Classifier clsModel = new Classifier(clsModelFile, clsParamsFile, clsOption);
+Recognizer recModel = new Recognizer(recModelFile, recParamsFile, recLabelFilePath, recOption);
+PPOCRv3 model = new PPOCRv3(detModel，clsModel，recModel);
+
+// 读取图片: 以下仅为读取Bitmap的伪代码
+ByteBuffer pixelBuffer = ByteBuffer.allocate(width * height * 4);
+GLES20.glReadPixels(0, 0, width, height, GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, pixelBuffer);
+Bitmap ARGB8888ImageBitmap = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888);
+ARGB8888ImageBitmap.copyPixelsFromBuffer(pixelBuffer);
+
+// 模型推理
+OCRResult result = model.predict(ARGB8888ImageBitmap);  
+
+// 释放模型资源  
+model.release();
+```  
+
+- 模型调用示例2: 在合适的程序节点，手动调用init
+```java  
+// import 同上 ...
+import com.baidu.paddle.fastdeploy.RuntimeOption;
+import com.baidu.paddle.fastdeploy.LitePowerMode;
+import com.baidu.paddle.fastdeploy.vision.OCRResult;
+import com.baidu.paddle.fastdeploy.vision.ocr.Classifier;
+import com.baidu.paddle.fastdeploy.vision.ocr.DBDetector;
+import com.baidu.paddle.fastdeploy.vision.ocr.Recognizer;
+// 新建空模型
+PPOCRv3 model = new PPOCRv3();  
+// 模型路径
+String detModelFile = "ch_PP-OCRv3_det_infer/inference.pdmodel";
+String detParamsFile = "ch_PP-OCRv3_det_infer/inference.pdiparams";
+String clsModelFile = "ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel";
+String clsParamsFile = "ch_ppocr_mobile_v2.0_cls_infer/inference.pdiparams";
+String recModelFile = "ch_PP-OCRv3_rec_infer/inference.pdmodel";
+String recParamsFile = "ch_PP-OCRv3_rec_infer/inference.pdiparams";
+String recLabelFilePath = "labels/ppocr_keys_v1.txt";
+// 设置RuntimeOption
+RuntimeOption detOption = new RuntimeOption();
+RuntimeOption clsOption = new RuntimeOption();
+RuntimeOption recOption = new RuntimeOption();
+detOption.setCpuThreadNum(2);
+clsOption.setCpuThreadNum(2);
+recOption.setCpuThreadNum(2);
+detOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+clsOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+recOption.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
+detOption.enableLiteFp16();  
+clsOption.enableLiteFp16();  
+recOption.enableLiteFp16();  
+// 使用init函数初始化
+DBDetector detModel = new DBDetector(detModelFile, detParamsFile, detOption);
+Classifier clsModel = new Classifier(clsModelFile, clsParamsFile, clsOption);
+Recognizer recModel = new Recognizer(recModelFile, recParamsFile, recLabelFilePath, recOption);
+model.init(detModel, clsModel, recModel);
+// Bitmap读取、模型预测、资源释放 同上 ...
+```
+更详细的用法请参考 [OcrMainActivity](./app/src/main/java/com/baidu/paddle/fastdeploy/app/examples/ocr/OcrMainActivity.java)中的用法
+
 ## 替换 FastDeploy SDK和模型
 替换FastDeploy预测库和模型的步骤非常简单。预测库所在的位置为 `app/libs/fastdeploy-android-sdk-xxx.aar`，其中 `xxx` 表示当前您使用的预测库版本号。模型所在的位置为，`app/src/main/assets/models`。
 - 替换FastDeploy Android SDK: 下载或编译最新的FastDeploy Android SDK，解压缩后放在 `app/libs` 目录下；详细配置文档可参考:

examples/vision/segmentation/paddleseg/android/README.md

@@ -155,7 +155,7 @@ model.setVerticalScreenFlag(true);
 model.init(modelFile, paramFile, configFile, option);
 // Bitmap读取、模型预测、资源释放 同上 ...
 ```
-更详细的用法请参考 [SegmentationMainActivity](./app/src/main/java/com/baidu/paddle/fastdeploy/app/examples/detection/SegmentationMainActivity.java) 中的用法
+更详细的用法请参考 [SegmentationMainActivity](./app/src/main/java/com/baidu/paddle/fastdeploy/app/examples/segmentation/SegmentationMainActivity.java) 中的用法
 
 ## 替换 FastDeploy SDK和模型  
 替换FastDeploy预测库和模型的步骤非常简单。预测库所在的位置为 `app/libs/fastdeploy-android-sdk-xxx.aar`，其中 `xxx` 表示当前您使用的预测库版本号。模型所在的位置为，`app/src/main/assets/models/portrait_pp_humansegv2_lite_256x144_inference_model`。  

fastdeploy/vision/ocr/ppocr/utils/sorted_boxes.cc

@@ -37,11 +37,14 @@ void SortBoxes(std::vector<std::array<int, 8>>* boxes) {
   }
   
   for (int i = 0; i < boxes->size() - 1; i++) {
-    if (std::abs((*boxes)[i + 1][1] - (*boxes)[i][1]) < 10 &&
+    for (int j = i; j >=0 ; j--){
-        ((*boxes)[i + 1][0] < (*boxes)[i][0])) {
+        if (std::abs((*boxes)[j + 1][1] - (*boxes)[j][1]) < 10 &&
+        ((*boxes)[j + 1][0] < (*boxes)[j][0])) {
           std::swap((*boxes)[i], (*boxes)[i + 1]);
         }
     }
+  }
+
 }
 
 std::vector<int> ArgSort(const std::vector<float> &array) {

tools/common_tools/auto_compression/README.md

@@ -112,14 +112,14 @@ FastDeploy目前为用户提供了多个模型的压缩[config](./configs/)文
 ## 4. FastDeploy 部署量化模型
 用户在获得量化模型之后，即可以使用FastDeploy进行部署, 部署文档请参考:
 具体请用户参考示例文档:
-- [YOLOv5 量化模型部署](../../examples/vision/detection/yolov5/quantize/)
+- [YOLOv5 量化模型部署](../../../examples/vision/detection/yolov5/quantize/)
 
-- [YOLOv6 量化模型部署](../../examples/vision/detection/yolov6/quantize/)
+- [YOLOv6 量化模型部署](../../../examples/vision/detection/yolov6/quantize/)
 
-- [YOLOv7 量化模型部署](../../examples/vision/detection/yolov7/quantize/)
+- [YOLOv7 量化模型部署](../../../examples/vision/detection/yolov7/quantize/)
 
-- [PadddleClas 量化模型部署](../../examples/vision/classification/paddleclas/quantize/)
+- [PadddleClas 量化模型部署](../../../examples/vision/classification/paddleclas/quantize/)
 
-- [PadddleDetection 量化模型部署](../../examples/vision/detection/paddledetection/quantize/)
+- [PadddleDetection 量化模型部署](../../../examples/vision/detection/paddledetection/quantize/)
 
-- [PadddleSegmentation 量化模型部署](../../examples/vision/segmentation/paddleseg/quantize/)
+- [PadddleSegmentation 量化模型部署](../../../examples/vision/segmentation/paddleseg/quantize/)

tools/common_tools/auto_compression/README_EN.md

@@ -121,10 +121,14 @@ FastDeploy currently provides users with compression [config](./configs/) files
 
 Once obtained the quantized model, developers can deploy it on FastDeploy. Please refer to the following docs for more details
 
-- [YOLOv5 Quantized Model Deployment](../../examples/vision/detection/yolov5/quantize/)
+- [YOLOv5 Quantized Model Deployment](../../../examples/vision/detection/yolov5/quantize/)
 
-- [YOLOv6 Quantized Model Deployment](../../examples/vision/detection/yolov6/quantize/)
+- [YOLOv6 Quantized Model Deployment](../../../examples/vision/detection/yolov6/quantize/)
 
-- [YOLOv7 Quantized Model Deployment](../../examples/vision/detection/yolov7/quantize/)
+- [YOLOv7 Quantized Model Deployment](../../../examples/vision/detection/yolov7/quantize/)
 
-- [PadddleClas Quantized Model Deployment](../../examples/vision/classification/paddleclas/quantize/)
+- [PadddleClas Quantized Model Deployment](../../../examples/vision/classification/paddleclas/quantize/)
+
+- [PadddleDetection Quantized Model Deployment](../../../examples/vision/detection/paddledetection/quantize/)
+
+- [PadddleSegmentation Quantized Model Deployment](../../../examples/vision/segmentation/paddleseg/quantize/)