YOLOv8-Aquarium

This version of YOLOv8-Aquarium has been converted to run on the Axera NPU using w8a16 quantization. It is fine-tuned to detect aquatic life in aquarium environments.

Supported Classes

This model is trained to detect the following 5 classes:

1. fish
2. turtle
3. shrimp
4. crab
5. snail

Compatible with Pulsar2 version: 5.1.

Convert tools links:

For those who are interested in model conversion, you can try to export axmodel through:

• The repo of AXera Platform, where you can get the detailed guide.
• Pulsar2 Link, How to Convert ONNX to axmodel

Support Platform

https://docs.m5stack.com/zh_CN/ai_hardware/AI_Pyramid-Pro

• AX650N/AX8850
  • M4N-Dock(爱芯派Pro)
  • AI Pyramid
  • M.2 Accelerator card
• AX620E
  • 爱芯派2
  • Module-LLM
• AX637

How to use

Download all files from this repository to the device.

python env requirement

pyaxengine

https://github.com/AXERA-TECH/pyaxengine

wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc2/axengine-0.1.3-py3-none-any.whl
pip install axengine-0.1.3-py3-none-any.whl

Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)

Input image:

run

python3 infer_yolov8_pyax.py --model-path aquarium_yolov8s_6way.axmodel --test-img test.png

root@ax650:~/yolo8# python3 infer_yolov8_pyax.py --model-path aquarium_yolov8s_6way.axmodel --test-img test.png
[INFO] Available providers:  ['AxEngineExecutionProvider', 'AXCLRTExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.12.0s
[INFO] Model type: 2 (triple core)
[INFO] Compiler version: 5.2-dirty a6f1799a-dirty
[Aquarium-YOLOv8-6way] [14:15:20.397] [DEBUG] Load model time = 485.48 ms
[Aquarium-YOLOv8-6way] [14:15:20.463] [DEBUG] Pre-process time = 8.36 ms
[Aquarium-YOLOv8-6way] [14:15:20.483] [DEBUG] Forward time min/avg/max = 19.42/19.42/19.42 ms (n=1)
[Aquarium-YOLOv8-6way] [14:15:20.490] [DEBUG] Post-process time = 6.78 ms
[Aquarium-YOLOv8-6way] [14:15:20.491] [INFO] Detections: 26
[Aquarium-YOLOv8-6way] [14:15:20.491] [INFO]   fish     score=0.850  xyxy=(168.9,517.7,285.1,583.1)
[Aquarium-YOLOv8-6way] [14:15:20.491] [INFO]   fish     score=0.771  xyxy=(757.8,573.3,801.3,601.8)
...
[Aquarium-YOLOv8-6way] [14:15:20.496] [INFO]   fish     score=0.452  xyxy=(600.2,412.4,652.9,460.7)
[Aquarium-YOLOv8-6way] [14:15:20.496] [INFO] per-class: {'fish': 26, 'turtle': 0, 'shrimp': 0, 'crab': 0, 'snail': 0}
[Aquarium-YOLOv8-6way] [14:15:20.526] [INFO] Saved to result_aquarium_yolov8.jpg
root@ax650:~/yolo8# ls
aquarium_yolov8s_6way.axmodel  infer_yolov8_pyax.py  result_aquarium_yolov8.jpg  test.png
root@ax650:~/yolo8# realpath result_aquarium_yolov8.jpg
/root/yolo8/result_aquarium_yolov8.jpg

Output image: