Create README.md

README.md

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+# ArcFace ONNX
+
+A high-accuracy face recognition (embedding) model exported to ONNX format, ready to run with [onnxruntime](https://onnxruntime.ai/).
+
+- **Input:** Cropped RGB face image, resized to 112x112.
+- **Output:** 512-dimensional embedding (vector).
+- **Use case:** Face verification and recognition (compare two faces for similarity).
+
+---
+
+## 📥 Download Model
+
+Download the ONNX model using:
+
+```bash
+wget https://huggingface.co/garavv/arcface-onnx/resolve/main/arc.onnx?download=true -O arcface.onnx
+```
+
+---
+
+## 🚀 Quick Start
+
+```python
+import cv2
+import numpy as np
+import onnxruntime as ort
+
+def preprocess(img_path):
+    img = cv2.imread(img_path)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, (112, 112))
+    img = (img.astype(np.float32) - 127.5) / 128.0
+    return img[np.newaxis, ...]   # shape: (1, 112, 112, 3)
+
+sess = ort.InferenceSession("arcface.onnx")
+input_name = sess.get_inputs()[0].name
+output_name = sess.get_outputs()[0].name
+
+emb1 = sess.run([output_name], {input_name: preprocess("face1.jpg")})[0][0]
+emb2 = sess.run([output_name], {input_name: preprocess("face2.jpg")})[0][0]
+
+# Normalize
+emb1 = emb1 / np.linalg.norm(emb1)
+emb2 = emb2 / np.linalg.norm(emb2)
+cosine_sim = np.dot(emb1, emb2)
+print("Cosine similarity:", cosine_sim)
+```
+
+---
+
+## 📦 Dependencies
+
+- Python 3.7+
+- onnxruntime
+- numpy
+- opencv-python
+
+**Install with:**
+
+```bash
+pip install onnxruntime numpy opencv-python
+```
+
+---
+
+## 📝 Model Details
+
+- **Architecture:** ArcFace (ONNX, 512-dim output)
+- **Input shape:** (1, 112, 112, 3) (batch, height, width, channels)
+- **Output:** (1, 512) embedding vector
+
+---
+
+