Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+
+language:
+- en
+
+base_model:
+- princeton-vl/RAFT-Stereo
+
+pipeline_tag: depth-estimation
+
+tags:
+- RAFT-Stereo
+- Stereo-Matching
+- Disparity-Estimation
+- Depth-Estimation
+- Computer-Vision
+- 3DV-2021
+- PyTorch
+- Convolutional-GRU
+---
+
+# RAFT-Stereo on Axera NPU
+
+This repository contains the [RAFT-Stereo](https://arxiv.org/abs/2109.07547) model converted for high-performance inference on the Axera NPU. 
+
+This version is optimized with **w8a16** quantization and is compatible with **Pulsar2 version 4.2**.
+
+## Convert Tools Links
+
+For model conversion and deployment guidance:
+
+* [AXera Platform GitHub Repo](https://github.com/AXERA-TECH/RAFT-Stereo.axera): Sample code and optimization guides for Axera NPU.
+* [Pulsar2 Documentation](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html): Guide for converting ONNX models to `.axmodel`.
+
+## Support Platforms
+
+- **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)
+
+
+| Chips | Model Variant | NPU3 Latency (ms) |
+| --- | --- | --- |
+| AX650 | RAFT-Stereo-Small | TBD |
+
+| Chips | Model Variant | NPU2 Latency (ms) |
+| --- | --- | --- |
+| AX630C| RAFT-Stereo-Small | TBD |
+
+## How to Use
+
+Download the repository and ensure the directory structure is organized as follows:
+
+```text
+
+
+```
+
+### Prerequisites
+
+1. **Environment:** Ensure you have the required Python environment activated with the following core packages installed:
+* **NPU Runtime:** [`axengine` (PyAXEngine)](https://github.com/AXERA-TECH/pyaxengine)
+* **Core Libraries:** `numpy`, `opencv-python`, and `matplotlib`.
+
+
+2. **Model/Data:** Ensure the compiled `.axmodel`, and input stereo pairs are available on the host.
+
+### Inference Command
+
+Run the inference script by providing the compiled model, and input data.
+
+```bash
+python3 infer.py --left examples/left/000051_11.png --right examples/right/000051_11.png --model raft_steoro256x640_r1_npu2.axmodel --width 640 --height 256
+
+```
+
+### Inference with AX630C Host
+
+### Results
+
+The model generates a dense disparity map representing the depth of the scene. Results are saved as colorized depth maps and point cloud projections where applicable.
+
+```
+(base) root@ax630c:~/data# python3 infer.py --left examples/left/000051_11.png --right examples/right/000051_11.png --model raft_steoro256x640_r1_npu2.axmodel --width 640 --height 256
+[INFO] Available providers:  ['AxEngineExecutionProvider']
+[INFO] Using provider: AxEngineExecutionProvider
+[INFO] Chip type: ChipType.MC20E
+[INFO] VNPU type: VNPUType.DISABLED
+[INFO] Engine version: 2.7.2a
+[INFO] Model type: 1 (full core)
+[INFO] Compiler version: 5.1-patch1-dirty e0fdc66b-dirty
+Saved: output-ax.png
+
+```
+
+### Example Visualization
+
+![](output-ax.png)
+
+