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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- Ultralytics/YOLOv8 |
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pipeline_tag: object-detection |
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tags: |
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- Ultralytics |
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- YOLOv8 |
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- YOLOv8-Seg |
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--- |
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# YOLOv8-Seg |
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This version of YOLOv8-Seg has been converted to run on the Axera NPU using **w8a16** quantization. |
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This model has been optimized with the following LoRA: |
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Compatible with Pulsar2 version: 3.4 |
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## Convert tools links: |
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For those who are interested in model conversion, you can try to export axmodel through |
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- [The repo of AXera Platform](https://github.com/AXERA-TECH/ax-samples), which you can get the detial of guide |
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- [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) |
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## Support Platform |
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- AX650 |
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- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) |
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- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) |
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- AX630C |
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- [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) |
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- [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) |
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- [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) |
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|Chips|yolov8s-seg| |
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|--|--| |
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|AX650| 4.6 ms | |
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|AX630C| TBD ms | |
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## How to use |
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Download all files from this repository to the device |
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``` |
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root@ax650:~/YOLOv8-Seg# tree |
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. |
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|-- ax650 |
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| `-- yolov8s-seg.axmodel |
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|-- ax_yolov8_seg |
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|-- football.jpg |
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`-- yolov8_seg_out.jpg |
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``` |
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### Inference |
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Input image: |
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 |
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#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) |
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``` |
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root@ax650:~/samples/AXERA-TECH/YOLOv8-Seg# ./ax_yolov8_seg -m ax650/yolov8s_seg.axmodel -i football.jpg |
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-------------------------------------- |
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model file : ax650/yolov8s_seg.axmodel |
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image file : football.jpg |
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img_h, img_w : 640 640 |
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-------------------------------------- |
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Engine creating handle is done. |
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Engine creating context is done. |
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Engine get io info is done. |
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Engine alloc io is done. |
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Engine push input is done. |
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-------------------------------------- |
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input size: 1 |
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name: images [UINT8] [BGR] |
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1 x 640 x 640 x 3 |
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output size: 7 |
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name: /model.22/Concat_1_output_0 [FLOAT32] |
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1 x 80 x 80 x 144 |
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name: /model.22/Concat_2_output_0 [FLOAT32] |
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1 x 40 x 40 x 144 |
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name: /model.22/Concat_3_output_0 [FLOAT32] |
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1 x 20 x 20 x 144 |
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name: /model.22/cv4.0/cv4.0.2/Conv_output_0 [FLOAT32] |
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1 x 80 x 80 x 32 |
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name: /model.22/cv4.1/cv4.1.2/Conv_output_0 [FLOAT32] |
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1 x 40 x 40 x 32 |
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name: /model.22/cv4.2/cv4.2.2/Conv_output_0 [FLOAT32] |
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1 x 20 x 20 x 32 |
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name: output1 [FLOAT32] |
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1 x 32 x 160 x 160 |
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post process cost time:16.21 ms |
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-------------------------------------- |
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Repeat 1 times, avg time 4.69 ms, max_time 4.69 ms, min_time 4.69 ms |
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-------------------------------------- |
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detection num: 8 |
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0: 92%, [1354, 340, 1629, 1035], person |
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0: 91%, [ 5, 359, 314, 1108], person |
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0: 91%, [ 759, 220, 1121, 1153], person |
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0: 88%, [ 490, 476, 661, 999], person |
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32: 73%, [1233, 877, 1286, 923], sports ball |
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32: 63%, [ 772, 888, 828, 937], sports ball |
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32: 63%, [ 450, 882, 475, 902], sports ball |
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0: 55%, [1838, 690, 1907, 811], person |
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-------------------------------------- |
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``` |
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Output image: |
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