face_input = preprocess_face("face.jpg") embedding = session.run(["output"], "input": face_input)[0] print(f"Embedding shape: embedding.shape") # (1, 512)
The "R50" stands for ResNet-50. ResNet (Residual Network) was a breakthrough architecture introduced by Microsoft Research in 2015. Before ResNet, training very deep neural networks was difficult due to the "vanishing gradient" problem.
import onnxruntime as ort
import cv2
import numpy as np
Using ONNX Runtime Web, you can run this model client-side in a browser. This eliminates the need to send face images to a server, solving major privacy (GDPA) concerns. w600k-r50.onnx
Title: Download w600k-r50.onnx – High-Performance Face Recognition Model
Meta Description: Get the w600k-r50.onnx file for ArcFace inference. A ResNet-50 backbone trained on 600k identities. Supports ONNX Runtime for CPU/GPU deployment. Perfect for real-time face verification.
The model expects specific preprocessing: face_input = preprocess_face("face
Title: Model Card: w600k-r50.onnx
Overview
w600k-r50.onnx is a deep learning model serialized in the Open Neural Network Exchange (ONNX) format. It is designed for face recognition tasks, specifically tailored for high-performance identity verification. The "R50" stands for ResNet-50
Architecture & Specifications
Intended Use
Input/Output
import cv2
import numpy as np
import onnxruntime as ort