YOLOv8

This version of YOLOv8 has been converted to run on the Axera NPU using w8a16 quantization.

This model has been optimized with the following LoRA:

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, which you can get the detial of guide

• Pulsar2 Link, How to Convert ONNX to axmodel

Support Platform

• AX650
  • M4N-Dock(爱芯派Pro)
  • M.2 Accelerator card
• AX630C
  • 爱芯派2
  • Module-LLM
  • LLM630 Compute Kit
• AX637
• AX615

##Performance Statistics

AX650N

Model	Latency(ms) npu1	Latency(ms) npu3
yolov8n	4.112	1.410
yolov8s	10.173	3.549
yolov8m	26.824	9.389
yolov8l	51.532	17.858
yolov8x	88.379	28.892

AX630C

Model	Latency(ms) npu1	Latency(ms) npu2
yolov8n	14.489	8.308
yolov8s	31.349	23.601
yolov8m	80.694	57.040
yolov8l	143.709	100.611
yolov8x	244.731	158.686

AX615

Model	Latency(ms) npu1	Latency(ms) npu2
yolov8n	17.830	10.652
yolov8s	48.895	27.398
yolov8m	133.628	69.313

AX637

Model	Latency(ms) npu1
yolov8n	4.714
yolov8s	11.945
yolov8m	28.219
yolov8l	53.790
yolov8x	84.507

How to use

Download all files from this repository to the device

Inference

Input image:

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

root@ax650:~/ax650# python3 ax_infer.py --model-path yolov8m_640x640_npu3.axmodel --test-img bus.jpg
[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: 6.0-dirty a498e20d-dirty
[YOLOv8-Det] [13:44:39.169] [DEBUG] Load model time = 483.73 ms
[YOLOv8-Det] [13:44:39.205] [DEBUG] Pre-process time = 7.49 ms
[YOLOv8-Det] [13:44:39.234] [DEBUG] Forward time = 28.59 ms
[YOLOv8-Det] [13:44:39.245] [DEBUG] Post-process time = 10.48 ms
[YOLOv8-Det] [13:44:39.249] [INFO] Draw Results (5 objects):
[YOLOv8-Det] [13:44:39.250] [INFO] (2, 229, 800, 741) -> bus: 0.97
[YOLOv8-Det] [13:44:39.253] [INFO] (51, 400, 247, 905) -> person: 0.93
[YOLOv8-Det] [13:44:39.254] [INFO] (668, 393, 808, 881) -> person: 0.91
[YOLOv8-Det] [13:44:39.255] [INFO] (221, 403, 343, 859) -> person: 0.89
[YOLOv8-Det] [13:44:39.255] [INFO] (0, 549, 78, 873) -> person: 0.79
[YOLOv8-Det] [13:44:39.281] [INFO] Saved to result_yolov8_det.jpg
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Output image: