metadata
license: mit
language:
- en
pipeline_tag: object-detection
tags:
- Ultralytics
- YOLOv5
YOLOv5
This version of YOLOv5 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: 3.4
Convert tools links:
For those who are interested in model conversion, you can try to export axmodel through
The repo of ax-samples, which you can get the how to build the
ax_yolov5sThe repo of axcl-samples, which you can get the how to build the
axcl_yolov5s
Support Platform
| Chips | cost |
|---|---|
| AX650 | 6.32 ms |
| AX630C | TBD ms |
How to use
Download all files from this repository to the device
root@ax650 ~/yolov5 # tree -L 2
.
├── ax650
│ └── yolov5s.axmodel
├── ax_aarch64
│ └── ax_yolov5s
├── config.json
├── ssd_horse.jpg
├── README.md
├── yolov5_config.json
├── yolov5s-cut.onnx
├── yolov5s.onnx
└── yolov5s_out.jpg
3 directories, 9 files
Inference
Input image:
Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)
root@ax650 ~/yolov5 # ./ax_yolov5s -m yolov5s.axmodel -i ssd_horse.jpg
--------------------------------------
model file : yolov5s.axmodel
image file : ssd_horse.jpg
img_h, img_w : 640 640
--------------------------------------
Engine creating handle is done.
Engine creating context is done.
Engine get io info is done.
Engine alloc io is done.
Engine push input is done.
--------------------------------------
post process cost time:1.91 ms
--------------------------------------
Repeat 1 times, avg time 6.32 ms, max_time 6.32 ms, min_time 6.32 ms
--------------------------------------
detection num: 6
0: 83%, [ 270, 13, 352, 228], person
17: 83%, [ 213, 60, 431, 363], horse
16: 79%, [ 143, 197, 195, 351], dog
0: 74%, [ 431, 125, 450, 177], person
7: 73%, [ 0, 103, 136, 199], truck
0: 47%, [ 402, 130, 411, 148], person
--------------------------------------
Output image:
