Update README.md

info about the onnx model

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+- depth-estimation
+- onnx
+- computer-vision
+- visiondepth3d
+- mit-license
+---
+
+# Distilled AnyDepth (ONNX) – For VisionDepth3D
+
+> **Model Origin:** This model is based on [Distilled AnyDepth by Westlake-AGI-Lab](https://github.com/Westlake-AGI-Lab/Distill-Any-Depth), originally developed by ISL (Intel Intelligent Systems Lab).  
+> I did not train this model — I have converted it to ONNX format for fast, GPU-accelerated inference within tools such as VisionDepth3D.
+
+## 🧠 About This Model
+
+This is a direct conversion of the **Distill-Any-Depth** PyTorch model to **ONNX**, intended for lightweight, real-time depth estimation from single RGB images.
+
+### ✔️ Key Features:
+- ONNX format (exported from PyTorch)
+- Compatible with ONNX Runtime and TensorRT
+- Excellent for 2D to 3D depth workflows
+- Works seamlessly with **VisionDepth3D**
+
+## 📌 Intended Use
+
+- Real-time or batch depth map generation
+- 2D to 3D conversion pipelines (e.g., SBS 3D video)
+- Works on Windows, Linux (CUDA-supported)
+
+## 📜 License and Attribution
+
+### Citation
+@article{he2025distill,
+  title   = {Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator},
+  author  = {Xiankang He and Dongyan Guo and Hongji Li and Ruibo Li and Ying Cui and Chi Zhang},
+  year    = {2025},
+  journal = {arXiv preprint arXiv: 2502.19204}
+}
+
+- **Source Model:** [Distilled AnyDepth by Westlake-AGI-Lab](https://github.com/Westlake-AGI-Lab/Distill-Any-Depth)
+- **License:** MIT  
+- **Modifications:** Only format conversion (no retraining or weight changes)
+
+> If you use this model, please credit the original authors: Westlake-AGI-Lab.
+
+## 💻 How to Use
+
+```python
+import onnxruntime as ort
+ort.InferenceSession("distilled_anydepth.onnx", providers=["CUDAExecutionProvider"])