--- license: mit language: - en base_model: - Ultralytics/YOLO11 pipeline_tag: object-detection tags: - Ultralytics - YOLO11 - YOLO11-Seg --- # YOLO11-Seg This version of YOLO11-Seg 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 AXera Platform](https://github.com/AXERA-TECH/ax-samples), which you can get the detial of guide - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - AX650 - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) |Chips|YOLO11x-Seg| |--|--| |AX650| 34 ms | |AX630C| TBD ms | ## How to use Download all files from this repository to the device ``` root@ax650:~/YOLO11-Pose# tree . |-- ax650 | `-- yolo11x-seg.axmodel |-- ax_yolo11_seg |-- football.jpg `-- yolo11_seg_out.jpg ``` ### Inference Input image: ![](./football.jpg) #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) ``` root@ax650:~/samples/AXERA-TECH/YOLO11-Seg# ./ax_yolo11_seg -m ax650/yolo11x-seg.axmodel -i football.jpg -------------------------------------- model file : ax650/yolo11x-seg.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. -------------------------------------- input size: 1 name: images [UINT8] [BGR] 1 x 640 x 640 x 3 output size: 7 name: /model.23/Concat_1_output_0 [FLOAT32] 1 x 80 x 80 x 144 name: /model.23/Concat_2_output_0 [FLOAT32] 1 x 40 x 40 x 144 name: /model.23/Concat_3_output_0 [FLOAT32] 1 x 20 x 20 x 144 name: /model.23/cv4.0/cv4.0.2/Conv_output_0 [FLOAT32] 1 x 80 x 80 x 32 name: /model.23/cv4.1/cv4.1.2/Conv_output_0 [FLOAT32] 1 x 40 x 40 x 32 name: /model.23/cv4.2/cv4.2.2/Conv_output_0 [FLOAT32] 1 x 20 x 20 x 32 name: output1 [FLOAT32] 1 x 32 x 160 x 160 post process cost time:16.90 ms -------------------------------------- Repeat 1 times, avg time 34.59 ms, max_time 34.59 ms, min_time 34.59 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_seg_out.jpg) #### Inference with M.2 Accelerator card ``` (base) axera@raspberrypi:~/lhj/YOLO11-Seg $ ./axcl_aarch64/axcl_yolo11_seg -m ax650/yolo11x-seg.axmodel -i football.jpg -------------------------------------- model file : ax650/yolo11x-seg.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: 7 name: /model.23/Concat_1_output_0 1 x 80 x 80 x 144 name: /model.23/Concat_2_output_0 1 x 40 x 40 x 144 name: /model.23/Concat_3_output_0 1 x 20 x 20 x 144 name: /model.23/cv4.0/cv4.0.2/Conv_output_0 1 x 80 x 80 x 32 name: /model.23/cv4.1/cv4.1.2/Conv_output_0 1 x 40 x 40 x 32 name: /model.23/cv4.2/cv4.2.2/Conv_output_0 1 x 20 x 20 x 32 name: output1 1 x 32 x 160 x 160 ================================================== Engine push input is done. -------------------------------------- post process cost time:3.47 ms -------------------------------------- Repeat 1 times, avg time 34.89 ms, max_time 34.89 ms, min_time 34.89 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_seg_out_axcl.jpg)