File size: 1,654 Bytes
9a35af8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
# 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
---
|