You can deploy this model and start earning money today!

globalnebula/
adaface-ir50-ms1mv2-onnx

Name: adaface-ir50-ms1mv2-onnx
Availability: InStock

image-feature-extraction

other

Views

No views yet

AdaFace IR-50 (MS1MV2) — ONNX

ONNX export of the AdaFace IR-50 face-recognition backbone trained on MS1MV2. Produces a 512-dimensional face embedding for verification / identification. Runs on onnxruntime (CPU or CUDA) with no PyTorch dependency at inference time.

This is a straight conversion of the published PyTorch checkpoint — the weights are unchanged. It exists because AdaFace ships only as a PyTorch .ckpt and this project's inference worker runs on onnxruntime.

Graph signature

	Name	Shape	Notes
Input	`input`	`(batch, 3, 112, 112)`	ArcFace-aligned BGR face, normalized `(x - 127.5) / 127.5`
Output	`embedding`	`(batch, 512)`	Face feature; L2-normalize before cosine comparison

Opset: 17. Dynamic batch axis on both input and output.
Preprocessing: align the face to the standard ArcFace 5-point template at 112×112 (e.g. OpenCV FaceRecognizerSF.alignCrop), keep channels in BGR, then scale with (x - 127.5) / 127.5.
Compare two faces with cosine similarity on the L2-normalized embeddings.

Usage

python

import cv2, numpy as np, onnxruntime as ort

sess = ort.InferenceSession("adaface_ir50_ms1mv2.onnx", providers=["CPUExecutionProvider"])

def embed(aligned_bgr_112: np.ndarray) -> np.ndarray:
    x = (aligned_bgr_112.astype(np.float32) - 127.5) / 127.5      # (112,112,3) BGR
    x = np.transpose(x, (2, 0, 1))[None]                          # (1,3,112,112)
    e = sess.run(["embedding"], {"input": x})[0][0]               # (512,)
    return e / np.linalg.norm(e)

# cosine similarity
# sim = float(embed(face_a) @ embed(face_b))

Verification

The export was checked against the source PyTorch model on identical inputs:

onnx.checker passes.
Max absolute difference (torch vs. onnxruntime output): ~4.3e-07.
Output shape (1, 512); embedding L2 norm ≈ 1.0.

Provenance & license

Architecture / training: AdaFace (Kim et al., CVPR 2022), IR-50 backbone, MS1MV2 training set. Upstream code: https://github.com/mk-minchul/AdaFace (MIT).
Weights: converted from the published AdaFace IR-50 MS1MV2 checkpoint. The network definition used for the export is the vendored AdaFace architecture (MIT).
License note: the AdaFace code is MIT, but pretrained weights derive from the MS1MV2 dataset, which carries its own dataset/research-use licensing. Review the upstream terms before any production or commercial use.

Citation

bibtex

@inproceedings{kim2022adaface,
  title     = {AdaFace: Quality Adaptive Margin for Face Recognition},
  author    = {Kim, Minchul and Jain, Anil K. and Liu, Xiaoming},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2022}
}

globalnebula/adaface-ir50-ms1mv2-onnx