# yolov11

## 1.Overview

​		YOLOv11 was released by Ultralytics on October 2, 2024. It introduces significant architectural refinements and efficiency optimizations, delivering superior accuracy-speed trade-offs compared to previous generations. With enhanced feature extraction capabilities, YOLOv11 is designed for high-performance real-time applications—including object detection, instance segmentation, and pose estimation—to handle demanding tasks in a wide range of applications. 

## 2.Model Download

- **Open Source model**

  - **Open Source projects:** https://github.com/ultralytics/ultralytics/tree/v8.3.0

  - **Export Model Step:**	

    - **Install ultralytics**

        pip install torch==2.4.1

        pip install torchvision==0.19.1

        pip install ultralytics==8.3.0

    - **Download weights**

      wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt

      wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt

      wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt

    - **Export Model**

      ```
      from ultralytics import YOLO
      
      model = YOLO("yolo11n.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_convert.sh model_path adla_toolkit_path target_platform

example
 ./adla_convert.sh yolov11m.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
 ./adla_convert.sh yolov11s.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
 ./adla_convert.sh yolov11n.onnx  /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
```

| Parameter         | Description                                                  |
| ----------------- | ------------------------------------------------------------ |
| model_path        | onnx model path                                              |
| adla_toolkit_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/yolov11/cpp
AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a
```

The executable will be generated at `build/android/yolo11_demo` (Note: executable name may vary, verify in build folder).

#### 2. Run

```bash
# Push executable to device
adb push build/android/yolo11_demo /data/local/tmp/
adb push model/yolov11n_int8_A311D2.adla /data/local/tmp/
adb push imgs /data/local/tmp/

# Run on device
adb shell
cd /data/local/tmp
chmod +x yolo11_demo
export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)

# Usage: ./yolo11_demo <model_path> <image_dir>
./yolo11_demo yolov11n_int8_A311D2.adla ./imgs
```

**Note:** Replace `yolov11n_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 yolov11.py \
    --model-path ./yolov11n_int8_A311D2.adla \
    --image-dir ./imgs \
    --run-cycles 1 \
    --loglevel INFO
```

Argument Descriptions:
| Argument         | Description                                                  |
| ----------------- | ------------------------------------------------------------ |
| --board-work-path       | Work path on board, default is /data/local/tmp    |
| --model-path | path to .adla model  |
| --image-dir   | Directory containing test images |
| --run-cycles   | Number of inference cycles, default is 1 |
| --loglevel   | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |

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

**Performance Feedback**

By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:
- Hardware Information: System and ADLA library versions.
- Model Overview: Basic input/output configurations.
- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.

**Detection Output**

The program will print the detection count. The result image with bounding boxes will be saved to the specified output path (`{model_name}_result`). 

You can pull the result folder back to view it:
```bash
adb pull /data/local/tmp/yolov11n_int8_A311D2_result
```
![alt text](result.jpg)

**Profiling Visualization**

After a successful run of the Python demo, a folder named after the model (e.g., `{model_name}`) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:
- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
- `pie_charts_distribution.html`: Overall resource allocation.

You can pull the result folder back to view it:
```bash
adb pull /data/local/tmp/yolov11n_int8_A311D2
```

Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.

![alt text](Visualization.png)