VirtuoTuring Face Embedder
ONNX model for generating facial embeddings and comparing two faces.
What it does
This model takes an aligned face as input and returns a 512-dimensional facial embedding.
Expected input
- RGB image
- cropped/aligned face
- size: 112x112
- type: float32
- normalization: [-1, 1]
Output
- 512-dimensional float32 embedding
Comparison
Compare embeddings using cosine similarity.
Suggested initial threshold: - 0.93
Training data
This model was trained on a curated dataset of 23,660 face images.
The dataset contains cropped and aligned face images and was assembled to include variation in apparent age, sex presentation, accessories, and appearance, including examples with hats and glasses.
The training set was built from sources that were identified by the curator as providing permissive reuse terms and/or explicit permission for access and reuse. Where images were discovered through search engines, they were not treated as licensed merely because they appeared in search results; inclusion was intended only where the original source indicated free-use terms, permissive licensing, or other documented authorization.
Data provenance and legal notice
Face images may constitute personal data and, when used for face recognition or identity-related processing, may fall within the scope of biometric data under applicable data protection law. Accordingly, this model card should not be read as a blanket statement that any downstream use is lawful in every jurisdiction.
The publisher represents that the dataset curation process sought to limit training data to images believed to be available under free-use terms and/or explicit authorization. However, users remain responsible for conducting their own legal review regarding copyright, privacy, data protection, biometric-data compliance, and any sector-specific rules that may apply in their jurisdiction.
Preprocessing
All training images were face-cropped and aligned before training. The model expects:
- RGB face image
- cropped/aligned face
- size: 112x112
- dtype: float32
- normalization: [-1, 1]
Limitations
This model does not perform face detection on its own and assumes that the face is already cropped and aligned. It is not a certified biometric identification system and may perform poorly under extreme pose, occlusion, poor lighting, or partial-face conditions.
Limitations
- it does not perform face detection on its own
- it assumes the face is already cropped/aligned
- it is not a certified biometric system
- it may fail with extreme pose, occlusions, poor lighting, or partial faces
Files
virtuoturing.onnx- external weights file
.data
Usage example
import onnxruntime as ort
sess = ort.InferenceSession("virtuoturing.onnx")
print(sess.get_inputs()[0].name, sess.get_inputs()[0].shape)
print(sess.get_outputs()[0].name, sess.get_outputs()[0].shape)