Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+base_model:
+- RTMPose-M
+pipeline_tag: image-classification
+tags:
+- Axera
+- RTMPose
+- Pose Estimation
+- Keypoint Detection
+- SimCC
+- OpenMMLab
 ---
+
+# RTMPose-M
+
+This version of **RTMPose-M** (256x192) has been converted to run on the Axera NPU using **mixed w16/fp32** quantization. It is optimized for real-time human pose estimation with 17 COCO keypoints using the SimCC decoding approach.
+
+Compatible with Pulsar2 version: 6.0.
+
+## Model Info
+
+| Item | Value |
+| :--- | :--- |
+| **Architecture** | RTMPose-M (CSPNeXt + SimCC Head) |
+| **Parameters** | 13.58M |
+| **Input** | 1x256x192x3 (NHWC, uint8, BGR) |
+| **Output** | simcc_x (1,17,384), simcc_y (1,17,512) |
+| **Keypoints** | 17 (COCO format) |
+| **Source** | [OpenMMLab MMPose](https://github.com/open-mmlab/mmpose) |
+
+## 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), where you can get the detailed 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)
+
+### Performance Statistics
+
+#### AX650N
+
+| Model | Latency(ms) npu3 |
+| :--- | :---: |
+| **rtmpose_m** | 2.881 |
+
+## Conversion Pipeline
+
+1. **Export ONNX** — Download official RTMPose-M from OpenMMLab and fix batch dim:
+
+```bash
+python export_onnx.py
+```
+
+2. **Replace HardSigmoid** — Replace HardSigmoid ops with Mul+Add+Clip for better NPU quantization:
+
+```bash
+python replace_hardsigmoid.py
+```
+
+3. **Compile axmodel** — Use Pulsar2 with the provided `config.json` to quantize and compile:
+
+```bash
+pulsar2 build --target_hardware AX650 --config config.json --input rtmpose_m_256x192_no_hs.onnx --output_dir AX650
+```
+
+## How to use
+
+Download all files from this repository to the device.
+
+### python env requirement
+
+#### pyaxengine
+
+https://github.com/AXERA-TECH/pyaxengine
+
+```bash
+wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc2/axengine-0.1.3-py3-none-any.whl
+pip install axengine-0.1.3-py3-none-any.whl
+```
+
+### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)
+
+Input image:
+
+![](test.jpg)
+
+run
+```bash
+python3 ax_infer.py -m rtmpose_m_npu3.axmodel -i test.jpg
+```
+
+```bash
+root@ax650:~/data# python3 ax_infer.py -m rtmpose_m_npu3.axmodel -i test.jpg
+[INFO] Available providers:  ['AxEngineExecutionProvider']
+[INFO] Using provider: AxEngineExecutionProvider
+[INFO] Chip type: ChipType.MC50
+[INFO] VNPU type: VNPUType.DISABLED
+[INFO] Engine version: 2.10.1s
+[INFO] Model type: 2 (triple core)
+[INFO] Compiler version: 6.0 93b95f7f
+Model input: name=input, shape=[1, 256, 192, 3], dtype=uint8
+Forward: 3.38 ms (avg of 10 runs)
+simcc_x: shape=(1, 17, 384), range=[-0.58, 0.88]
+simcc_y: shape=(1, 17, 512), range=[-0.49, 0.88]
+kpts above 0.3: 17/17
+  kp00: ( 359.6,   83.3)  score=0.6773
+  kp01: ( 370.0,   79.2)  score=0.6950
+  kp02: ( 359.6,   77.1)  score=0.6878
+  kp03: ( 384.6,   79.2)  score=0.7398
+  kp04: ( 359.6,   79.2)  score=0.6385
+  kp05: ( 403.3,  106.3)  score=0.7596
+  kp06: ( 367.9,  116.7)  score=0.7683
+  kp07: ( 432.5,  152.1)  score=0.4699
+  kp08: ( 342.9,  158.3)  score=0.6831
+  kp09: ( 445.0,  177.1)  score=0.3021
+  kp10: ( 305.4,  179.2)  score=0.5798
+  kp11: ( 432.5,  212.5)  score=0.7872
+  kp12: ( 399.2,  218.7)  score=0.8110
+  kp13: ( 432.5,  289.6)  score=0.7358
+  kp14: ( 372.1,  279.2)  score=0.8252
+  kp15: ( 470.0,  356.2)  score=0.6704
+  kp16: ( 399.2,  345.8)  score=0.8183
+Saved: ax_result.jpg
+```
+
+Output image:
+
+![](ax_result.jpg)
+