--- license: mit language: - en base_model: - Ultralytics/YOLOv8 pipeline_tag: object-detection tags: - Ultralytics - YOLOv8 - YOLOv8-Seg --- # YOLOv8-Seg This version of YOLOv8-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|yolov8s-seg| |--|--| |AX650| 4.6 ms | |AX630C| TBD ms | ## How to use Download all files from this repository to the device ``` root@ax650:~/YOLOv8-Seg# tree . |-- ax650 | `-- yolov8s-seg.axmodel |-- ax_yolov8_seg |-- football.jpg `-- yolov8_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/YOLOv8-Seg# ./ax_yolov8_seg -m ax650/yolov8s_seg.axmodel -i football.jpg -------------------------------------- model file : ax650/yolov8s_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.22/Concat_1_output_0 [FLOAT32] 1 x 80 x 80 x 144 name: /model.22/Concat_2_output_0 [FLOAT32] 1 x 40 x 40 x 144 name: /model.22/Concat_3_output_0 [FLOAT32] 1 x 20 x 20 x 144 name: /model.22/cv4.0/cv4.0.2/Conv_output_0 [FLOAT32] 1 x 80 x 80 x 32 name: /model.22/cv4.1/cv4.1.2/Conv_output_0 [FLOAT32] 1 x 40 x 40 x 32 name: /model.22/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:16.21 ms -------------------------------------- Repeat 1 times, avg time 4.69 ms, max_time 4.69 ms, min_time 4.69 ms -------------------------------------- detection num: 8 0: 92%, [1354, 340, 1629, 1035], person 0: 91%, [ 5, 359, 314, 1108], person 0: 91%, [ 759, 220, 1121, 1153], person 0: 88%, [ 490, 476, 661, 999], person 32: 73%, [1233, 877, 1286, 923], sports ball 32: 63%, [ 772, 888, 828, 937], sports ball 32: 63%, [ 450, 882, 475, 902], sports ball 0: 55%, [1838, 690, 1907, 811], person -------------------------------------- ``` Output image: ![](./yolov8_seg_out.jpg) #### Inference with M.2 Accelerator card ``` (base) axera@raspberrypi:~/lhj/YOLOv8-Seg $ ./axcl_aarch64/axcl_yolov8_seg -m ax650/yolov8s_seg.axmodel -i football.jpg -------------------------------------- model file : ax650/yolov8s_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.22/Concat_1_output_0 1 x 80 x 80 x 144 name: /model.22/Concat_2_output_0 1 x 40 x 40 x 144 name: /model.22/Concat_3_output_0 1 x 20 x 20 x 144 name: /model.22/cv4.0/cv4.0.2/Conv_output_0 1 x 80 x 80 x 32 name: /model.22/cv4.1/cv4.1.2/Conv_output_0 1 x 40 x 40 x 32 name: /model.22/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:3.67 ms -------------------------------------- Repeat 1 times, avg time 4.85 ms, max_time 4.85 ms, min_time 4.85 ms -------------------------------------- detection num: 8 0: 92%, [1354, 340, 1629, 1035], person 0: 91%, [ 5, 359, 314, 1108], person 0: 91%, [ 759, 220, 1121, 1153], person 0: 88%, [ 490, 476, 661, 999], person 32: 73%, [1233, 877, 1286, 923], sports ball 32: 63%, [ 772, 888, 828, 937], sports ball 32: 63%, [ 450, 882, 475, 902], sports ball 0: 55%, [1838, 690, 1907, 811], person -------------------------------------- ``` Output image: ![](./yolov8_seg_axcl_out.jpg)