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Shusek00/ray-local-models
ray-local-models is a machine learning model from Shusek00. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as other.
Deployment exports for the Ray Local Suvio plugin, evaluated on Apple M1 Max and a Qualcomm SM8850 Android device. These are converted pretrained models, not new training runs.
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Updated Sep 8, 2026
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From the Hugging Face model README
Deployment exports for the Ray Local Suvio plugin, evaluated on Apple M1 Max and a Qualcomm SM8850 Android device. These are converted pretrained models, not new training runs.
| File | Bytes | Role |
|---|---|---|
tinyfacematch-fp16.onnx | 6,948,481 | Default recognizer, 128 dimensions |
adaface-ir50-webface4m-fp16.onnx | 87,227,974 | Optional larger recognizer, 512 dimensions |
yunet-2026may-dynamic-fp32.onnx | 229,738 | Shared face detector and five landmarks |
The base pair is 7,178,219 bytes. All three current files together are 94,406,193 bytes.
The earlier yunet-2023mar-640-fp32.onnx remains available for reproducing previous
benchmarks; current plugin packages include only the dynamic detector.
manifest.json records exact SHA-256 digests and tensor contracts. Applications should
pin an immutable repository commit and verify both length and digest before opening a file.
Both recognizers take input: float32 [1, 3, 112, 112], RGB, NCHW, after five-point
ArcFace alignment. Float16 weights and internal computation retain float32 public I/O.
(pixel - 127.5) / 128.0; embedding: float32 [1, 128], L2 normalized.(pixel - 127.5) / 127.5; embedding: float32 [1, 512]. L2 normalize the output.
This CVLFace export uses RGB; do not substitute the BGR convention of other AdaFace exports.input: float32 [1, 3, height, width], BGR pixels in [0, 255].
Pad each spatial dimension to a multiple of 32. Ray limits the longest source edge to
640 without upscaling, then adds zero padding at the right and bottom. Decode cls_*,
obj_*, bbox_*, kps_* at strides 8, 16, 32 using the actual padded dimensions.
A 640×360 frame uses 640×384. The dynamic model shares the 2023 model's learned weights.The detector is still required. A larger embedding model does not replace face detection or correct inaccurate landmarks. Embeddings from different recognizers are incompatible.
Aggregate public benchmark evaluation on prealigned crops, canonical ten-fold held-out threshold selection, no flip augmentation:
| Export | CFP-FP, 7,000 pairs | CPLFW, 6,000 pairs |
|---|---|---|
| TinyFaceMatch FP16 | 96.1143% | 90.8667% |
| AdaFace FP16 | 98.9429% | 93.9167% |
Warm batch-one recognition latency with synthetic inputs: TinyFaceMatch / AdaFace 1.251 / 3.724 ms on M1 Max CoreML, and 0.920 / 4.689 ms on Qualcomm QNN HTP. Android strict QNN profiles recorded accelerator execution with CPU graph fallback disabled. These are standalone model measurements; application integration and session loading add cost.
Android incremental warm PSS with YuNet on CPU: approximately 149 / 443 MB. These earlier combined-memory measurements used the fixed 640×640 detector. Dynamic detector geometry was separately checked on synthetic inputs on M1 Max; recognition inputs remain static 112×112. QNN EP requires static shapes, so the dynamic detector uses CPU in the current host while supported recognizers can use QNN HTP. This is process memory, not a measurement of all NPU memory. File size is not runtime memory. No identity labels, user photos, per-pair scores or embeddings are distributed here.
This repository does not grant new rights to upstream pretrained weights or training data.
tinyfacematch-128-pretrained.onnx, upstream SHA-256
6d8588c1dc1f91fab930be355d33d4b6be0b74d70c46ae0f9c65d89be2865aa4.
The repository code is MIT, but the model metadata and export script identify
InsightFace buffalo_s/w600k_mbf.onnx as its pretrained base, followed by PCA projection.
InsightFace's pretrained model terms
restrict the provided weights to non-commercial research. Do not treat the wrapper's MIT
license as commercial clearance for these derivative weights.face_detection_yunet_2026may.onnx, revision 47534e27c9851bb1128ccc0102f1145e27f23f98,
MIT model directory license. The current file is byte-identical to the official dynamic
FP32 export; it is not a retrained or larger detector. The legacy static file is retained
separately for reproducibility.For any intended distribution or use, the applicable upstream model and dataset terms remain in force. This export repository makes no independent claim of commercial permission.