This article will dissect every component of this file—from the architecture (R50) and the dataset (W600K) to the format (ONNX). By the end, you will understand why this specific model has become a go-to solution for production-grade face recognition.
The name "w600k-r50" describes its training background and architecture: : Indicates the model was trained on the MS1M-RetinaFace w600k-r50.onnx
While W600K-R50.onnx is a powerful model, it is not without its challenges and limitations. Here are a few: This article will dissect every component of this
| Model | Size (FP32) | LFW Accuracy | CPU Inference (Intel i7) | GPU (RTX 3060) | | :--- | :--- | :--- | :--- | :--- | | | 96 MB | 99.78% | 35 ms | 3 ms | | FaceNet (Inception) | 180 MB | 99.65% | 85 ms | 7 ms | | MobileFaceNet | 4 MB | 99.48% | 8 ms | 1 ms | | VGG-Face (16) | 500 MB | 98.95% | 120 ms | 9 ms | Here are a few: | Model | Size