--- 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: 5.0 ## 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 - **AX650N/AX8850** - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://docs.m5stack.com/en/ai_hardware/LLM-8850_Card) - **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) - **AX615** - **AX637** ### Performance Statistics #### AX650N | Model | Latency(ms) npu1 | Latency(ms) npu3 | | :--- | :---: | :---: | | **yolo26n-seg** | - | - | | **yolo26s-seg** | - | - | | **yolo26m-seg** | - | - | | **yolo26l-seg** | - | - | | **yolo26x-seg** | - | - | #### AX630C | Model | Latency(ms) npu1 | Latency(ms) npu2 | | :--- | :---: | :---: | | **yolo26n-seg** | - | - | | **yolo26s-seg** | - | - | | **yolo26m-seg** | - | - | | **yolo26l-seg** | - | - | | **yolo26x-seg** | - | - | #### AX615 | Model | Latency(ms) npu1 | Latency(ms) npu2 | | :--- | :---: | :---: | | **yolo26n-seg** | - | - | | **yolo26s-seg** | - | - | | **yolo26m-seg** | - | - | | **yolo26l-seg** | - | - | | **yolo26x-seg** | - | - | #### AX637 | Model | Latency(ms) npu1 | | :--- | :---: | | **yolo26n-seg** | - | | **yolo26s-seg** | - | | **yolo26m-seg** | - | | **yolo26l-seg** | - | | **yolo26x-seg** | - | ## 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) ``` (base) root@ax650:~/ax650seg# python3 ax_infer.py --model-path yolov8m-seg_640x640_npu3.axmodel --test-img bus.jpg [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-Seg] [15:38:19.398] [DEBUG] Load model time = 592.02 ms [YOLOv8-Seg] [15:38:19.433] [DEBUG] Pre-process time = 8.17 ms [YOLOv8-Seg] [15:38:19.472] [DEBUG] Forward time = 38.19 ms [YOLOv8-Seg] [15:38:19.482] [DEBUG] Post-process time = 9.47 ms [YOLOv8-Seg] [15:38:19.483] [DEBUG] Proto shape: (32, 160, 160) [YOLOv8-Seg] [15:38:19.534] [INFO] Draw Results (6 objects): [YOLOv8-Seg] [15:38:19.535] [INFO] (13, 230, 803, 736) -> bus: 0.93 [YOLOv8-Seg] [15:38:19.580] [INFO] (49, 398, 244, 904) -> person: 0.91 [YOLOv8-Seg] [15:38:19.599] [INFO] (222, 397, 345, 860) -> person: 0.89 [YOLOv8-Seg] [15:38:19.616] [INFO] (667, 395, 810, 880) -> person: 0.88 [YOLOv8-Seg] [15:38:19.632] [INFO] (0, 551, 78, 866) -> person: 0.59 [YOLOv8-Seg] [15:38:19.647] [INFO] (137, 472, 148, 504) -> tie: 0.37 [YOLOv8-Seg] [15:38:19.685] [INFO] Saved to result_yolov8_seg.jpg ``` Output image: 
