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README.md
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---
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license:
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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/YOLO11
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pipeline_tag: object-detection
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tags:
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- Ultralytics
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- YOLO11
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- YOLO11-Seg
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---
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# YOLO11-Seg
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This version of YOLO11-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|YOLO11x-Seg|
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|--|--|
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|AX650| 34 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:~/YOLO11-Pose# tree
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.
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|-- ax650
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| `-- yolo11x-seg.axmodel
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|-- ax_yolo11_seg
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|-- football.jpg
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`-- yolo11_seg_out.jpg
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```
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### Inference
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Input image:
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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/YOLO11-Seg# ./ax_yolo11_seg -m ax650/yolo11x-seg.axmodel -i football.jpg
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--------------------------------------
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model file : ax650/yolo11x-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.23/Concat_1_output_0 [FLOAT32]
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1 x 80 x 80 x 144
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name: /model.23/Concat_2_output_0 [FLOAT32]
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1 x 40 x 40 x 144
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name: /model.23/Concat_3_output_0 [FLOAT32]
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1 x 20 x 20 x 144
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name: /model.23/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.23/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.23/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:16.90 ms
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--------------------------------------
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Repeat 1 times, avg time 34.59 ms, max_time 34.59 ms, min_time 34.59 ms
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--------------------------------------
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detection num: 9
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0: 94%, [ 757, 220, 1127, 1154], person
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0: 94%, [ 0, 357, 314, 1112], person
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0: 93%, [1353, 339, 1629, 1037], person
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0: 91%, [ 494, 476, 659, 1001], person
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32: 86%, [1231, 877, 1281, 922], sports ball
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32: 73%, [ 774, 887, 828, 938], sports ball
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32: 66%, [1012, 882, 1051, 927], sports ball
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0: 54%, [ 0, 543, 83, 1000], person
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0: 46%, [1837, 696, 1877, 814], person
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--------------------------------------
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```
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Output image:
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