arcface-onnx

ArcFace ONNX

A high-accuracy face recognition (embedding) model exported to ONNX format, ready to run with onnxruntime.

• Input: Cropped RGB face image, resized to 112x112.
• Output: 512-dimensional embedding (vector).
• Use case: Face verification and recognition (compare two faces for similarity).

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📥 Download Model

Download the ONNX model using:

wget https://huggingface.co/garavv/arcface-onnx/resolve/main/arc.onnx?download=true -O arcface.onnx

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🚀 Quick Start

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)

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📦 Dependencies

• Python 3.7+
• onnxruntime
• numpy
• opencv-python

Install with:

pip install onnxruntime numpy opencv-python

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📝 Model Details

• Architecture: ArcFace (ONNX, 512-dim output)
• Input shape: (1, 112, 112, 3) (batch, height, width, channels)
• Output: (1, 512) embedding vector

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