add yolov5-seg and ax650 example

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: object-detection
+tags:
+- Ultralytics
+- YOLOv5
+- YOLOv5-Seg
+---
+
+# YOLOv5
+
+This version of YOLOv5 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 ax-samples](https://github.com/AXERA-TECH/ax-samples), which you can get the how to build the `ax_yolov5s_seg`
+
+- [The repo of axcl-samples](https://github.com/AXERA-TECH/axcl-samples), which you can get the how to build the `axcl_yolov5s_seg`
+
+- [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|cost|
+|--|--|
+|AX650| 9.55 ms |
+|AX630C| TBD ms |
+
+## How to use
+
+Download all files from this repository to the device
+
+```
+root@ax650 ~/yolov5-seg # tree -L 2
+.
+├── ax650
+│   └── yolov5s-seg.axmodel
+├── ax_aarch64
+│   └── ax_yolov5s_seg
+├── config.json
+├── football.jpg
+├── README.md
+├── yolov5_seg_config.json
+├── yolov5s-seg-cut.onnx
+├── yolov5s-seg.onnx
+└── yolov5s_seg_out.jpg
+
+3 directories, 10 files
+```
+
+### Inference
+
+Input image:
+![](./football.jpg)
+
+#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)
+
+```
+root@ax650 ~/yolov5-seg # ./ax_yolov5s_seg -m yolov5s-seg.axmodel -i football.jpg
+--------------------------------------
+model file : yolov5s-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.
+--------------------------------------
+post process cost time:9.19 ms
+--------------------------------------
+Repeat 1 times, avg time 9.55 ms, max_time 9.55 ms, min_time 9.55 ms
+--------------------------------------
+detection num: 6
+ 0:  90%, [ 747,  224, 1140, 1147], person
+ 0:  89%, [1356,  337, 1622, 1035], person
+ 0:  88%, [   3,  364,  308, 1094], person
+ 0:  81%, [ 491,  479,  668, 1015], person
+32:  78%, [ 777,  887,  827,  942], sports ball
+ 0:  59%, [1840,  690, 1905,  812], person
+--------------------------------------
+```
+
+Output image:
+![](./yolov5s_seg_out.jpg)

ax650/yolov5s-seg.axmodel

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+oid sha256:c76e0b975b20532d27ab001701d6048d99879b2770ee5833337cf49760668ed0
+size 8153338

ax_aarch64/ax_yolov5s_seg

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad56305f3b6481dccba9f788775357b9917c3870798891c0cd5d20e628016207
+size 5470728

yolov5_seg_config.json

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+{
+  "model_type": "ONNX",
+  "npu_mode": "NPU1",
+  "quant": {
+    "input_configs": [
+      {
+        "tensor_name": "images",
+        "calibration_dataset": "coco_1000.tar",
+        "calibration_size": 64,
+        "calibration_mean": [0, 0, 0],
+        "calibration_std": [255.0, 255.0, 255.0]
+      }
+    ],
+    "calibration_method": "MinMax",
+    "precision_analysis": true,
+    "precision_analysis_method":"EndToEnd"
+  },
+  "input_processors": [
+    {
+      "tensor_name": "images",
+      "tensor_format": "RGB",
+      "src_format": "BGR",
+      "src_dtype": "U8",
+      "src_layout": "NHWC"
+    }
+  ],
+  "output_processors": [
+    {
+      "tensor_name": "output1",
+      "dst_perm": [0, 1, 2, 3]
+    },    {
+      "tensor_name": "/model.24/m.0/Conv_output_0",
+      "dst_perm": [0, 2, 3, 1]
+    },    {
+      "tensor_name": "/model.24/m.1/Conv_output_0",
+      "dst_perm": [0, 2, 3, 1]
+    },    {
+      "tensor_name": "/model.24/m.2/Conv_output_0",
+      "dst_perm": [0, 2, 3, 1]
+    }
+  ],
+  "compiler": {
+    "check": 2
+  }
+}

yolov5s-seg-cut.onnx

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+size 30498589

yolov5s-seg.onnx

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