add python demos

examples/yolov8/README.md

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+# yolov8
+
+## 1.Overview
+
+​		YOLOv8 was released by Ultralytics on January 10, 2023, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduced new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/blog/a-guide-to-deep-dive-into-object-detection-in-2025) tasks in a wide range of applications. 
+
+## 2.Model Download
+
+- **Open Source model**
+
+  - **Open Source projects:** https://github.com/ultralytics/ultralytics/tree/v8.2.0
+
+  - **Export Model Step:**	
+
+    - **Install ultralytics**
+
+        pip install torch==2.4.1
+
+        pip install torchvision==0.19.1
+
+        pip install ultralytics==8.2.0
+
+    - **Download weights**
+
+      wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt
+
+      wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
+
+      wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt
+
+    - **Export Model**
+
+      ```
+      from ultralytics import YOLO
+      
+      model = YOLO("yolov8m.pt")
+      model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
+      ```
+
+
+- **Exported Model**
+
+  ​	link to amlogic server( **onnx model   or quantized tflite**)
+
+  
+
+## 3. Model Conversion
+
+```
+cd model
+Usage:   ./adla_covnert.sh model_path adla_tookkit_path target_platform
+
+example
+ ./adla_covnert.sh yolov8m.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
+ ./adla_covnert.sh yolov8s.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
+ ./adla_covnert.sh yolov8n.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
+```
+
+| Parameter         | Discription                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| model_path        | onnx model path                                              |
+| adla_tookkit_path | path to adla_toolkit                                         |
+| target_platform   | Specify target platform. for A311D2 : PRODUCT_PID0XA003。for S905X5:  PRODUCT_PID0XA005 |
+
+
+
+## 4. Demo Run
+
+### CPP
+
+#### 1. Compile
+
+**Prerequisites:**
+- Android NDK (r25e recommended)
+- `ANDROID_NDK_PATH` environment variable set
+
+**Build:**
+```bash
+# Build for arm64-v8a
+cd examples/yolov8/cpp
+./build-android.sh -a arm64-v8a
+```
+
+The executable will be generated at `build/android/yolov8_demo` (Note: executable name may vary, verify in build folder).
+
+#### 2. Run
+
+```bash
+# Push executable to device
+adb push build/android/yolov8_demo /data/local/tmp/
+adb push model/yolov8s_int8_A311D2.adla /data/local/tmp/
+adb push test_image.jpg /data/local/tmp/
+
+# Run on device
+adb shell
+cd /data/local/tmp
+chmod +x yolov8_demo
+export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
+
+# Usage: ./yolo_world_demo <model_path> <image_path>
+./yolov8_demo yolov8s_int8_A311D2.adla test_image.jpg"
+```
+
+**Note:** Replace `yolov8s_int8_A311D2.adla` with your actual model file path.
+
+### Python
+
+**Prerequisites:**
+- Python 3.10
+- Required packages: `numpy`, `opencv-python`, `amlnnlite`
+
+**Install dependencies:**
+```bash
+pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+python yolov8.py --model-path ./yolov8s_int8_A311D2.adla
+```
+
+The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and save results to a `{model_name}_result` folder.
+
+## 5.Results
+The program will print the detection count and inference time. The result image with bounding boxes will be saved to the specified output path (`result.jpg` by default).
+
+
+You can pull the result image back to view it:
+```bash
+adb pull result.jpg.
+```
+![alt text](result.jpg)
+