Shusek00/ray-local-models

Ray Local model exports

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.

Tensor contracts

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.

• TinyFaceMatch: (pixel - 127.5) / 128.0; embedding: float32 [1, 128], L2 normalized.
• AdaFace: (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.
• YuNet: 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.

Measurements

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.

Provenance and terms

This repository does not grant new rights to upstream pretrained weights or training data.

• AdaFace: CVLFace AdaFace IR50 WebFace4M, by Minchul Kim and collaborators. The upstream model card requires following the training dataset's license. See AdaFace (CVPR 2022) and CVLFace. ONNX export at opset 17; static batch one; FP16 conversion with ONNX Runtime 1.29.0 and ONNX 1.22.0.
• TinyFaceMatch: yuvrajraina/tinyfacematch, 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.
• YuNet: OpenCV Zoo, 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.