--- license: mit language: - en base_model: - Ultralytics/YOLO11 pipeline_tag: object-detection tags: - Ultralytics - YOLO11 --- # YOLO11 This version of YOLO11 has been converted to run on the WaveMatrix 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](https://github.com/WaveMatrix/ax-samples), which you can get the how to build the `ax_yolo11` - [The repo of axcl-samples](https://github.com/WaveMatrix/axcl-samples), which you can get the how to build the `axcl_yolo11` - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - WM9955 - Demo board - M.2 Accelerator card |Chips|cost| |--|--| |WM9955| 25 ms | ## How to use Download all files from this repository to the device ``` (axcl) wavematrix@raspberrypi:~/samples/WaveMatrix/YOLO11 $ tree -L 2 . ├── wm9955 │   ├── yolo11s.axmodel │   └── yolo11x.axmodel ├── ax_aarch64 │   └── ax_yolo11 ├── axcl_aarch64 │   └── axcl_yolo11 ├── axcl_x86_64 │   └── axcl_yolo11 ├── config.json ├── cut-onnx.py ├── football.jpg ├── README.md ├── ssd_horse.jpg ├── yolo11_config.json ├── yolo11_out.jpg ├── yolo11s-cut.onnx └── yolo11-test.py 6 directories, 15 files ``` ### Inference Input image: ![](./football.jpg) #### Inference with WM9955 Host, such as demo board ``` root@wm9955:~/samples/WaveMatrix/YOLO11# ./ax_aarch64/ax_yolo11 -m wm9955/yolo11x.axmodel -i football.jpg -------------------------------------- model file : wm9955/yolo11x.axmodel image file : football.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:4.20 ms -------------------------------------- Repeat 1 times, avg time 24.56 ms, max_time 24.56 ms, min_time 24.56 ms -------------------------------------- detection num: 9 0: 94%, [ 757, 220, 1127, 1154], person 0: 94%, [ 0, 357, 314, 1112], person 0: 93%, [1353, 339, 1629, 1037], person 0: 91%, [ 494, 476, 659, 1001], person 32: 86%, [1231, 877, 1281, 922], sports ball 32: 73%, [ 774, 887, 828, 938], sports ball 32: 66%, [1012, 882, 1051, 927], sports ball 0: 54%, [ 0, 543, 83, 1000], person 0: 46%, [1837, 696, 1877, 814], person -------------------------------------- ``` Output image: ![](./yolo11_out.jpg) #### Inference with M.2 Accelerator card ``` (axcl) wavematrix@raspberrypi:~/samples/WaveMatrix/YOLO11 $ ./axcl_aarch64/axcl_yolo11 -m wm9955/yolo11x.axmodel -i football.jpg -------------------------------------- model file : wm9955/yolo11x.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- axclrtEngineCreateContextt is done. axclrtEngineGetIOInfo is done. grpid: 0 input size: 1 name: images 1 x 640 x 640 x 3 output size: 3 name: /model.23/Concat_output_0 1 x 80 x 80 x 144 name: /model.23/Concat_1_output_0 1 x 40 x 40 x 144 name: /model.23/Concat_2_output_0 1 x 20 x 20 x 144 ================================================== Engine push input is done. -------------------------------------- post process cost time:1.38 ms -------------------------------------- Repeat 1 times, avg time 24.73 ms, max_time 24.73 ms, min_time 24.73 ms -------------------------------------- detection num: 9 0: 94%, [ 757, 220, 1127, 1154], person 0: 94%, [ 0, 357, 314, 1112], person 0: 93%, [1353, 339, 1629, 1037], person 0: 91%, [ 494, 476, 659, 1001], person 32: 86%, [1231, 877, 1281, 922], sports ball 32: 73%, [ 774, 887, 828, 938], sports ball 32: 66%, [1012, 882, 1051, 927], sports ball 0: 54%, [ 0, 543, 83, 1000], person 0: 46%, [1837, 696, 1877, 814], person -------------------------------------- ```