vmaf_tiny_v4.json

{
  "id": "vmaf_tiny_v4",
  "kind": "fr",
  "onnx": "vmaf_tiny_v4.onnx",
  "opset": 17,
  "sha256": "2bccd339caa08f25433120546e45f2cb435288edeeefeafc56f3668986904e8f",
  "input_name": "features",
  "input_shape": [
    -1,
    6
  ],
  "output_name": "vmaf",
  "output_shape": [
    -1
  ],
  "features": [
    "adm2",
    "vif_scale0",
    "vif_scale1",
    "vif_scale2",
    "vif_scale3",
    "motion2"
  ],
  "input_mean": [
    0.9154358735163397,
    0.5044673305940499,
    0.756364188222672,
    0.8398883767464267,
    0.89199739393249,
    6.417078245524368
  ],
  "input_std": [
    0.03473643563995937,
    0.15760624188237923,
    0.09289338872033201,
    0.069510538567901,
    0.05259210477314612,
    7.320738307667759
  ],
  "arch": "mlp_large",
  "n_params": 3073,
  "quant_mode": "dynamic",
  "int8_sha256": "203a25905a3797b1cc3e3f347f4f6f491b491000d4424017beef64219767a9e9",
  "quant_accuracy_budget_plcc": 0.01,
  "onnx_has_scaler": true,
  "notes": "vmaf_tiny_v4 \u2014 canonical-6 + StandardScaler + mlp_large (3073 params), 90 epochs Adam @ lr=1e-3, MSE. Scaler (mean, std) baked into the ONNX graph as Constant nodes so the runtime feeds raw feature values. Same recipe as vmaf_tiny_v2/v3 but with ~3.5x hidden capacity over v3 (6 \u2192 64 \u2192 32 \u2192 16 \u2192 1). Dynamic-PTQ int8 sidecar via ai/scripts/ptq_dynamic.py (ADR-0275); measured drop=0.000145 vs 0.01 budget on Netflix features."
}