Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- tianweiy/CenterPoint
+pipeline_tag: object-detection
+tags:
+- Axera
+- NPU
+- Pulsar2
+- CenterPoint
+- 3D-Object-Detection
+- LiDAR
+---
+
+# CenterPoint on Axera NPU
+
+This repository contains the [CenterPoint](https://arxiv.org/abs/2006.11275) model converted for high-performance inference on the Axera NPU. CenterPoint is a center-based framework for 3D object detection and tracking that represents objects as points, significantly simplifying the detection pipeline while achieving state-of-the-art performance on LiDAR point clouds.
+
+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/centerpoint.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
+
+- **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)
+
+| Chips | Model Variant  | NPU3 Latency (Per Frame) |
+|---|---|---|---|
+| AX650 | CenterPoint-Pillar |  TBD |
+
+## How to Use
+
+CenterPoint requires 3D point cloud inputs (typically LiDAR data in `.bin` or `.pcd` format).
+
+### Prerequisites
+
+1.  **Environment:** Ensure you have the required Python environment activated with the following core packages installed:
+    * **NPU Runtime:** `axengine` (PyAXEngine)
+    * **Core Libraries:** `numba` , `opencv-python` and `tqdm`, and.
+
+2.  **Model/Data:** Ensure the compiled `.axmodel`, `inference_config.json`, and input data (`inference_data/`) are available on the host.
+
+### Inference Command
+
+Run the inference script by providing the compiled model, configuration, and data directory.
+
+```bash
+python inference_axmodel.py compiled.axmodel inference_config.json inference_data/ --output-dir inference_results
+```
+
+### Inference with AX650 Host
+
+### Results
+The model generates a 3D detection map with bounding boxes oriented in 3D space. Results are saved as images and videos which visualize the ego-vehicle, point cloud data, and detected objects.
+
+### Example Visualization
+