133 lines
3.9 KiB
Markdown
133 lines
3.9 KiB
Markdown
# yolov8
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## 1.Overview
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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.
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## 2.Model Download
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- **Open Source model**
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- **Open Source projects:** https://github.com/ultralytics/ultralytics/tree/v8.2.0
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- **Export Model Step:**
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- **Install ultralytics**
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pip install torch==2.4.1
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pip install torchvision==0.19.1
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pip install ultralytics==8.2.0
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- **Download weights**
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt
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- **Export Model**
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```
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from ultralytics import YOLO
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model = YOLO("yolov8m.pt")
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model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
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```
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- **Exported Model**
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link to amlogic server( **onnx model or quantized tflite**)
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## 3. Model Conversion
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```
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cd model
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Usage: ./adla_convert.sh model_path adla_toolkit_path target_platform
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example
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./adla_convert.sh yolov8m.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_convert.sh yolov8s.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_convert.sh yolov8n.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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```
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| Parameter | Description |
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| ----------------- | ------------------------------------------------------------ |
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| model_path | onnx model path |
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| adla_toolkit_path | path to adla_toolkit |
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| target_platform | Specify target platform. For A311D2: PRODUCT_PID0XA003. For S905X5: PRODUCT_PID0XA005 |
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## 4. Demo Run
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### CPP
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#### 1. Compile
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**Prerequisites:**
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- Android NDK (r25e recommended)
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- `ANDROID_NDK_PATH` environment variable set
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**Build:**
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```bash
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# Build for arm64-v8a
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cd examples/yolov8/cpp
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./build-android.sh -a arm64-v8a
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```
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The executable will be generated at `build/android/yolov8_demo` (Note: executable name may vary, verify in build folder).
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#### 2. Run
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```bash
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# Push executable to device
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adb push build/android/yolov8_demo /data/local/tmp/
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adb push model/yolov8s_int8_A311D2.adla /data/local/tmp/
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adb push test_image.jpg /data/local/tmp/
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# Run on device
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adb shell
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cd /data/local/tmp
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chmod +x yolov8_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./yolo_world_demo <model_path> <image_path>
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./yolov8_demo yolov8s_int8_A311D2.adla test_image.jpg"
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```
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**Note:** Replace `yolov8s_int8_A311D2.adla` with your actual model file path.
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### Python
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**Prerequisites:**
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- Python 3.10
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- Required packages: `numpy`, `opencv-python`, `amlnnlite`
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**Install dependencies:**
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```bash
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pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
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```
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**Run on device:**
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```bash
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python yolov8.py --model-path ./yolov8s_int8_A311D2.adla
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```
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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.
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## 5.Results
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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).
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You can pull the result image back to view it:
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```bash
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adb pull result.jpg.
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```
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