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globalnebula/insightface-buffalo-l-onnx
insightface-buffalo-l-onnx is a image feature extraction model from globalnebula. Use it for the image feature extraction task on the model card, and read the license before you ship it in a product. It is set up for onnxruntime. The card lists the license as other.
A mirror of the two ONNX models from the InsightFace buffalol pack used by the Piccy AI worker: SCRFD-10GF for detection (with 5-point landmarks) and ArcFace w600kr50 for recognition (512-d embeddings). Both run on on…
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Updated Aug 26, 2026
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From the Hugging Face model README
A mirror of the two ONNX models from the InsightFace buffalo_l pack used by
the Piccy AI worker: SCRFD-10GF for detection (with 5-point landmarks) and
ArcFace w600k_r50 for recognition (512-d embeddings). Both run on
onnxruntime with no PyTorch. The weights are unchanged from upstream; this
mirror just gives the build a stable, self-controlled download source.
| File | What it is | Notes |
|---|---|---|
scrfd_10g.onnx | SCRFD-10GF detector | upstream det_10g.onnx, renamed |
arcface_w600k_r50.onnx | ArcFace ResNet50 recognizer | upstream w600k_r50.onnx, renamed |
buffalo_l.zip | zip of det_10g.onnx + w600k_r50.onnx | for the Docker build arg below |
Point the build at the zip in this repo (works with the existing
INSIGHTFACE_BUFFALO_URL build arg, which extracts det_10g.onnx and
w600k_r50.onnx):
INSIGHTFACE_BUFFALO_URL="https://huggingface.co/globalnebula/insightface-buffalo-l-onnx/resolve/main/buffalo_l.zip" \
docker compose build ai-worker
Drop the two standalone files into the worker's models/ directory:
ai-worker/models/scrfd_10g.onnx
ai-worker/models/arcface_w600k_r50.onnx
(x-127.5)/128
internally (see ai-worker/insight_face.py). Outputs boxes, per-face scores,
and 5 landmarks.(batch, 3, 112, 112), an ArcFace-aligned 112x112 crop
normalized (x-127.5)/127.5. Output: (batch, 512) embedding; L2-normalize
before cosine comparison.buffalo_l model pack
(https://github.com/deepinsight/insightface).