registry.json

{
  "$schema": "./registry.schema.json",
  "models": [
    {
      "id": "dists_sq_placeholder_v0",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "DISTS-Sq smoke placeholder. Synthetic mean-squared tensor distance over two ImageNet-normalised RGB NCHW inputs; not a production DISTS checkpoint. Real upstream-derived weights from Ding et al. are tracked as T7-DISTS-followup. See docs/ai/models/dists_sq.md and ADR-0236.",
      "onnx": "dists_sq.onnx",
      "opset": 17,
      "sha256": "ec8433e8c7c6a33ef3032a6e4538833e0bbb59de9f088054bbcb3be0e371ee55",
      "sigstore_bundle": "dists_sq.onnx.sigstore.json",
      "smoke": true
    },
    {
      "description": "FastDVDnet temporal pre-filter (5-frame luma window) \u2014 upstream m-tassano/fastdvdnet weights wrapped for the fork's luma-only NCHW contract.",
      "id": "fastdvdnet_pre",
      "kind": "filter",
      "license": "MIT",
      "license_url": "https://github.com/m-tassano/fastdvdnet/blob/c8fdf6182a0340e89dd18f5df25b47337cbede6f/LICENSE",
      "notes": "Real FastDVDnet upstream weights (Tassano, Delon, Veit 2020; MIT). Upstream pinned at github.com/m-tassano/fastdvdnet@c8fdf61; luma adapter (Y->[Y,Y,Y] tile, sigma=25/255 noise map, RGB->Y BT.601 collapse) preserves the libvmaf 5-frame luma I/O contract from ADR-0215. Exported via ai/scripts/export_fastdvdnet_pre.py. T6-7b. See docs/ai/models/fastdvdnet_pre.md and docs/adr/0215-fastdvdnet-pre-filter.md.",
      "onnx": "fastdvdnet_pre.onnx",
      "opset": 17,
      "sha256": "eb9444cf6f07eefdc7f4f68d09131074dbd1dcee6f88a331ba684dd2fb5937d4",
      "sigstore_bundle": "fastdvdnet_pre.onnx.sigstore.json",
      "smoke": false
    },
    {
      "id": "fr_regressor_v1",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Tiny FR regressor (C1) \u2014 6-feature canonical-6 vector (adm2, vif_scale0..3, motion2) \u2192 VMAF teacher score scalar. Trained on the Netflix Public Dataset (9 ref + 70 dis YUVs) with vmaf_v0.6.1 as DMOS-aligned teacher. 9-fold LOSO mean PLCC = 0.9982 \u00b1 0.0014. Shipped checkpoint trained on all 9 sources. Exported via ai/scripts/train_fr_regressor.py. See docs/ai/models/fr_regressor_v1.md + ADR-0249.",
      "onnx": "fr_regressor_v1.onnx",
      "opset": 17,
      "sha256": "b57dee2509290d77c7980f8f23aa1380f64937c485d1b1d1e5f78c13a3a54c63",
      "sigstore_bundle": "fr_regressor_v1.onnx.sigstore.json"
    },
    {
      "id": "fr_regressor_v2",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Tiny FR regressor v2 (codec-aware) \u2014 6 canonical libvmaf features (adm2, vif_scale0..3, motion2) + 8-D codec block (6 encoder one-hot + preset_norm + crf_norm) -> VMAF teacher score. Trained on the vmaf-tune Phase A JSONL corpus (ADR-0237). Production checkpoint. In-sample PLCC=0.9794. Exported via ai/scripts/train_fr_regressor_v2.py. See docs/ai/models/fr_regressor_v2.md + ADR-0272 + ADR-0235.",
      "onnx": "fr_regressor_v2.onnx",
      "opset": 17,
      "sha256": "67934b0b61c73eb852d84ffb34e3333756e8da2530179ecc830336133e63e69e",
      "sigstore_bundle": "fr_regressor_v2.onnx.sigstore.json",
      "smoke": false
    },
    {
      "id": "fr_regressor_v2_ensemble_v1_seed0",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Smoke placeholder for ensemble member seed=0. The production weights (LOSO-validated, see seed sidecar) were trained against codec_vocab=14; the vocab was later trimmed to 6, so the on-disk ONNX was regenerated in smoke mode (#865) to keep the load path correct. Production re-export is deferred to the one-shot post-RC retrain (see ADR-0321 follow-up). smoke=true reflects the shipped weights honestly.",
      "onnx": "fr_regressor_v2_ensemble_v1_seed0.onnx",
      "opset": 17,
      "sha256": "98e4b08cc23e4941d4c839a5c734bd33efbb083f3cf7923a04c0d22351185444",
      "sigstore_bundle": "fr_regressor_v2_ensemble_v1_seed0.onnx.sigstore.json",
      "smoke": true
    },
    {
      "id": "fr_regressor_v2_ensemble_v1_seed1",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Smoke placeholder for ensemble member seed=1. The production weights (LOSO-validated, see seed sidecar) were trained against codec_vocab=14; the vocab was later trimmed to 6, so the on-disk ONNX was regenerated in smoke mode (#865) to keep the load path correct. Production re-export is deferred to the one-shot post-RC retrain (see ADR-0321 follow-up). smoke=true reflects the shipped weights honestly.",
      "onnx": "fr_regressor_v2_ensemble_v1_seed1.onnx",
      "opset": 17,
      "sha256": "4d6824fafceb6e3f1fdb6b2306167e0951b5eac8f54318c7cb4eb82284c3025b",
      "sigstore_bundle": "fr_regressor_v2_ensemble_v1_seed1.onnx.sigstore.json",
      "smoke": true
    },
    {
      "id": "fr_regressor_v2_ensemble_v1_seed2",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Smoke placeholder for ensemble member seed=2. The production weights (LOSO-validated, see seed sidecar) were trained against codec_vocab=14; the vocab was later trimmed to 6, so the on-disk ONNX was regenerated in smoke mode (#865) to keep the load path correct. Production re-export is deferred to the one-shot post-RC retrain (see ADR-0321 follow-up). smoke=true reflects the shipped weights honestly.",
      "onnx": "fr_regressor_v2_ensemble_v1_seed2.onnx",
      "opset": 17,
      "sha256": "1a0d0561fba4955b986d623446edd38d4eb0d1bd6e079fb6a2142a6033aa9f13",
      "sigstore_bundle": "fr_regressor_v2_ensemble_v1_seed2.onnx.sigstore.json",
      "smoke": true
    },
    {
      "id": "fr_regressor_v2_ensemble_v1_seed3",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Smoke placeholder for ensemble member seed=3. The production weights (LOSO-validated, see seed sidecar) were trained against codec_vocab=14; the vocab was later trimmed to 6, so the on-disk ONNX was regenerated in smoke mode (#865) to keep the load path correct. Production re-export is deferred to the one-shot post-RC retrain (see ADR-0321 follow-up). smoke=true reflects the shipped weights honestly.",
      "onnx": "fr_regressor_v2_ensemble_v1_seed3.onnx",
      "opset": 17,
      "sha256": "ba623fa4cbaff325aa3f3bb50ecc1b7edffd288e2c463cd2bcceb87f33894ec5",
      "sigstore_bundle": "fr_regressor_v2_ensemble_v1_seed3.onnx.sigstore.json",
      "smoke": true
    },
    {
      "id": "fr_regressor_v2_ensemble_v1_seed4",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Smoke placeholder for ensemble member seed=4. The production weights (LOSO-validated, see seed sidecar) were trained against codec_vocab=14; the vocab was later trimmed to 6, so the on-disk ONNX was regenerated in smoke mode (#865) to keep the load path correct. Production re-export is deferred to the one-shot post-RC retrain (see ADR-0321 follow-up). smoke=true reflects the shipped weights honestly.",
      "onnx": "fr_regressor_v2_ensemble_v1_seed4.onnx",
      "opset": 17,
      "sha256": "af727669a6dec5a36906ab7dd06f2a83e9a00c2706d6e0b2f3200c4af4409a43",
      "sigstore_bundle": "fr_regressor_v2_ensemble_v1_seed4.onnx.sigstore.json",
      "smoke": true
    },
    {
      "id": "fr_regressor_v3",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Tiny FR regressor v3 (codec-aware, ENCODER_VOCAB v3 16-slot) \u2014 6 canonical libvmaf features (adm2, vif_scale0..3, motion2) + 18-D codec block (16 encoder one-hot + preset_norm + crf_norm) -> VMAF teacher score. LOSO mean PLCC=0.9975 (gate >= 0.95). Production checkpoint. See docs/ai/models/fr_regressor_v3.md + ADR-0323 + ADR-0302 + ADR-0291 + ADR-0235.",
      "onnx": "fr_regressor_v3.onnx",
      "opset": 17,
      "sha256": "eaa16d23461eda74940b2ed590edfcaf13428aade294e47792a5a15f4d3b999c",
      "sigstore_bundle": "fr_regressor_v3.onnx.sigstore.json",
      "smoke": false
    },
    {
      "description": "Tiny residual filter for vmaf_pre \u2014 degraded \u2192 clean luma.",
      "id": "learned_filter_v1",
      "int8_sha256": "1cff6fe07f89f6c6a2eb498e60ad2699a3538846369c3724c9f30ba9600662d3",
      "kind": "filter",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Tiny residual filter baseline for vmaf_pre \u2014 self-supervised on KoNViD-1k frames with synthetic blur+JPEG degradation. ~19K params; opset 17. See docs/adr/0168-tinyai-konvid-baselines.md.",
      "onnx": "learned_filter_v1.onnx",
      "opset": 17,
      "quant_accuracy_budget_plcc": 0.01,
      "quant_mode": "dynamic",
      "sha256": "412d53700704a7aed26908c4fa0c4de9fabab08b6f2ae330ea5b9e4a4c462e27",
      "sigstore_bundle": "learned_filter_v1.onnx.sigstore.json"
    },
    {
      "description": "LPIPS-SqueezeNet perceptual distance (upstream richzhang/PerceptualSimilarity v0.1).",
      "id": "lpips_sq_v1",
      "kind": "fr",
      "license": "BSD-2-Clause",
      "license_url": "https://github.com/richzhang/PerceptualSimilarity/blob/master/LICENSE",
      "notes": "LPIPS-SqueezeNet (richzhang/PerceptualSimilarity v0.1). Two-input model: 'ref' + 'dist' ImageNet-normalised NCHW; scalar 'score' output. Exported via ai/lpips_export.py.",
      "onnx": "lpips_sq.onnx",
      "opset": 18,
      "sha256": "1402626680d5b69a793e647edda2c32f04e192f5cf1e7837bec8bde14187a261",
      "sigstore_bundle": "lpips_sq.onnx.sigstore.json"
    },
    {
      "id": "mobilesal_placeholder_v0",
      "kind": "nr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Superseded by saliency_student_v1 (ADR-0286) for production use; retained as the historical smoke-only placeholder ADR-0218 / ADR-0257 referenced. Synthetic Conv+Sigmoid matching the MobileSal I/O contract; emits per-pixel saliency in [0,1]. Real-weights swap from upstream MobileSal was deferred per ADR-0257 (CC BY-NC-SA 4.0 + Google-Drive-walled + RGB-D); the fork-trained saliency_student_v1 replaces it. See docs/ai/models/saliency_student_v1.md.",
      "onnx": "mobilesal.onnx",
      "opset": 17,
      "sha256": "f122631089977c4be7d60b9bf3d4daf186d275bd0587db2c9878578e006b91d4",
      "sigstore_bundle": "mobilesal.onnx.sigstore.json",
      "smoke": true
    },
    {
      "description": "Tiny no-reference quality metric \u2014 single-frame 224\u00d7224 grayscale.",
      "id": "nr_metric_v1",
      "int8_sha256": "e5ba2086f53c74539902883b12b01d83f06806f084b692b1d5ad325f9300cab2",
      "kind": "nr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Tiny NR MobileNet baseline trained on KoNViD-1k (CC BY 4.0; not redistributed). 224\u00d7224 grayscale input; ~19K params; opset 17. Dynamic-PTQ int8 sidecar produced via ai/scripts/ptq_dynamic.py (T5-3d). See docs/adr/0168-tinyai-konvid-baselines.md and docs/adr/0174-per-model-ptq.md.",
      "onnx": "nr_metric_v1.onnx",
      "opset": 17,
      "quant_accuracy_budget_plcc": 0.01,
      "quant_mode": "dynamic",
      "sha256": "75eff676bff0f0f911fd59ac72c90240e95caff67302b37f6e35bfbeac2b680b",
      "sigstore_bundle": "nr_metric_v1.onnx.sigstore.json"
    },
    {
      "description": "Fork-trained tiny U-Net saliency student trained on DUTS-TR \u2014 superseded by saliency_student_v2 as the production default (2026-05-15, ADR-0444).",
      "id": "saliency_student_v1",
      "kind": "nr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Fork-trained tiny U-Net (~113K params) on DUTS-TR. ConvTranspose stride-2 upsample. 50ep Adam lr=1e-3 BCE+Dice bs=32 seed=42; best val IoU=0.6558; PT<->ORT max-abs-diff=1.49e-6. Superseded by saliency_student_v2 (IoU 0.7105, +8.3%) as production default; retained for regression baselines. See docs/ai/models/saliency_student_v1.md and ADR-0444.",
      "onnx": "saliency_student_v1.onnx",
      "opset": 17,
      "sha256": "1edc64772498ab73ebdb17e21f49679ce5ded82534d47f557cd15fd36e1b46e7",
      "sigstore_bundle": "saliency_student_v1.onnx.sigstore.json",
      "smoke": false
    },
    {
      "description": "Fork-trained tiny U-Net saliency student trained on DUTS-TR \u2014 production default since 2026-05-15 (ADR-0444). Supersedes saliency_student_v1 (IoU +8.3%).",
      "id": "saliency_student_v2",
      "kind": "nr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Fork-trained tiny U-Net (~124K params) trained from scratch on DUTS-TR (Wang et al. 2017). Single-variable ablation against v1: decoder upsampler swapped from ConvTranspose2d to bilinear resize + 3x3 conv. Held-out val IoU=0.7105 (gate PASS vs v1=0.6558, +0.0547 / +8.3%). Drop-in for the mobilesal extractor (same input/saliency_map names + NCHW shapes). Production default since 2026-05-15. See docs/ai/models/saliency_student_v2.md + ADR-0332 + ADR-0444 + Research-0089.",
      "onnx": "saliency_student_v2.onnx",
      "opset": 17,
      "sha256": "8cdaa6c10ab44d9b03a6a207603bd0b61f5c75a6beafdc82da57215f0a2ab2ed",
      "sigstore_bundle": "saliency_student_v2.onnx.sigstore.json",
      "smoke": false
    },
    {
      "description": "fp16 I/O round-trip CI probe (Identity op).",
      "id": "smoke_fp16_v0",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "fp16 I/O round-trip probe (Identity op). Exercises the fp16_io cast path; not a quality model.",
      "onnx": "smoke_fp16_v0.onnx",
      "opset": 17,
      "sha256": "6cbf16be5d2cfb858f1eb60bfdcc9e674c15f17b3ff365afa475bbe9be76258b",
      "sigstore_bundle": "smoke_fp16_v0.onnx.sigstore.json",
      "smoke": true
    },
    {
      "description": "CI attached multi-output probe \u2014 two scalar outputs (mean_score, peak_score).",
      "id": "smoke_multi_output_v0",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "CI attached multi-output fixture used by test_vmaf_use_tiny_model.c (SMOKE_MULTI_OUTPUT_MODEL). Exercises the DNN multi-output + attached-output path; not a quality model. Generated by scripts/gen_multi_output_smoke_onnx.py.",
      "onnx": "smoke_multi_output_v0.onnx",
      "opset": 17,
      "sha256": "e5f353d65d6766b9beac0e59ea586c419308c0e24a6316a7714b7a5e4aef30e9",
      "sigstore_bundle": "smoke_multi_output_v0.onnx.sigstore.json",
      "smoke": true
    },
    {
      "description": "CI load-path probe (Conv + Identity).",
      "id": "smoke_v0",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "CI load-path probe (Conv + Identity). Not a quality model.",
      "onnx": "smoke_v0.onnx",
      "opset": 17,
      "sha256": "c83a5f217fa3736bf575c52f7f4c187a6201951e8ddccb51bdcdcc136108fbe0",
      "sigstore_bundle": "smoke_v0.onnx.sigstore.json",
      "smoke": true
    },
    {
      "description": "CI symbolic-batch probe \u2014 dynamic first dimension to exercise batch-agnostic load path.",
      "id": "smoke_v0_symbolic_batch",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "CI symbolic-batch fixture used by test_vmaf_use_tiny_model.c (SMOKE_SYMBATCH_MODEL). Exercises the DNN dynamic/symbolic first-dim load path; not a quality model. See comment block above SMOKE_SYMBATCH_MODEL in test_vmaf_use_tiny_model.c.",
      "onnx": "smoke_v0_symbolic_batch.onnx",
      "opset": 17,
      "sha256": "cc1a75a7518b27cdfc9df481bb1282950cc51fe7ef6f62c5dbccb106b73cc509",
      "sigstore_bundle": "smoke_v0_symbolic_batch.onnx.sigstore.json",
      "smoke": true
    },
    {
      "description": "TransNet V2 shot-boundary detector (Soucek & Lokoc 2020) \u2014 100-frame window of 27x48 RGB thumbnails -> per-frame logits.",
      "id": "transnet_v2",
      "kind": "nr",
      "license": "MIT",
      "license_url": "https://github.com/soCzech/TransNetV2/blob/77498b8e4a6d61ed7c3d9bd56f4de2b29ab7f4db/LICENSE",
      "notes": "TransNet V2 shot-boundary detector (T6-3a-followup). 100-frame window of 27x48 RGB thumbnails -> per-frame shot-boundary logits. Real upstream weights from Soucek & Lokoc 2020 (MIT). Wrapper transposes NTCHW->NTHWC; ColorHistograms ScatterND-rewritten for opset-17. See docs/ai/models/transnet_v2.md.",
      "onnx": "transnet_v2.onnx",
      "opset": 17,
      "sha256": "89280a199327871a0eb2c52914449ce339b7d8e8846a6245e4eb72d23d21cfc9",
      "sigstore_bundle": "transnet_v2.onnx.sigstore.json"
    },
    {
      "description": "Legacy tiny VMAF fusion regressor (mlp_small) \u2014 superseded by vmaf_tiny_v2 (ADR-0244); retained for LOSO-eval baselines + quantization regression tests.",
      "id": "vmaf_tiny_v1",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Legacy mlp_small (6\u219216\u21928\u21921) baseline over canonical-6 (adm2, vif_scale0..3, motion2). Superseded by vmaf_tiny_v2 (ADR-0244) as the default tiny FR fusion model. Retained on disk and in registry because docs/ai/loso-eval.md + docs/ai/quant-eps.md + docs/ai/quantization.md + ADR-0203 reference it as the single-split LOSO baseline and the quant-eps regression fixture (external-data filename repair landed via PR #296 / PR #174).",
      "onnx": "vmaf_tiny_v1.onnx",
      "opset": 17,
      "sha256": "d30201dfa8a0cb1d6d5bbe342b0f9049e40bf86e57b2e3b14cbfcade9231e7a6",
      "sigstore_bundle": "vmaf_tiny_v1.onnx.sigstore.json",
      "smoke": true
    },
    {
      "description": "Legacy tiny VMAF fusion regressor (mlp_medium sibling of vmaf_tiny_v1) \u2014 superseded by vmaf_tiny_v3; retained for LOSO-eval baselines.",
      "id": "vmaf_tiny_v1_medium",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "Legacy mlp_medium sibling of vmaf_tiny_v1. Superseded by vmaf_tiny_v3 as the medium-capacity default. Retained on disk and in registry because docs/ai/loso-eval.md references both vmaf_tiny_v1*.onnx as single-split LOSO baselines.",
      "onnx": "vmaf_tiny_v1_medium.onnx",
      "opset": 17,
      "sha256": "97f6116b44913f4076170a2f0cb78042db85aac7c56d432e14d9fe138ab952b7",
      "sigstore_bundle": "vmaf_tiny_v1_medium.onnx.sigstore.json",
      "smoke": true
    },
    {
      "description": "Tiny VMAF fusion regressor \u2014 canonical-6 features + bundled StandardScaler + mlp_small (~257 params).",
      "id": "vmaf_tiny_v2",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "vmaf_tiny_v2 \u2014 Phase-3 validated mlp_small (6\u219216\u21928\u21921) over canonical-6 (adm2, vif_scale0..3, motion2). 90ep Adam lr=1e-3 MSE bs=256. StandardScaler baked into ONNX graph (ADR-0244). Trained 4-corpus parquet (NF+KV+BVI-DVC A+B+C+D, 330499 rows). PLCC 0.9978\u00b10.0021 NF LOSO; 0.9998 KV 5-fold; 0.9999 ship-gate. See docs/ai/models/vmaf_tiny_v2.md.",
      "onnx": "vmaf_tiny_v2.onnx",
      "opset": 17,
      "sha256": "d7000bf5c5fd1ed39528546dd8464db44a13cb73cfed33fd8f134e5848b009a7",
      "sigstore_bundle": "vmaf_tiny_v2.onnx.sigstore.json",
      "smoke": false
    },
    {
      "description": "Tiny VMAF fusion regressor \u2014 canonical-6 features + bundled StandardScaler + mlp_medium (~769 params).",
      "id": "vmaf_tiny_v3",
      "int8_sha256": "b4bdbb353b1b395adb22b77e890117228de1cfd12b94cb46fd0173c2fd12a343",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "vmaf_tiny_v3 \u2014 mlp_medium (6\u219232\u219216\u21921) ~3x v2 capacity over canonical-6. 90ep Adam lr=1e-3 MSE bs=256. StandardScaler baked into ONNX graph. Trained 4-corpus parquet. NF LOSO PLCC 0.9986\u00b10.0015. Dynamic-PTQ int8 sidecar via ai/scripts/ptq_dynamic.py (ADR-0275). See docs/ai/models/vmaf_tiny_v3.md.",
      "onnx": "vmaf_tiny_v3.onnx",
      "opset": 17,
      "quant_accuracy_budget_plcc": 0.01,
      "quant_mode": "dynamic",
      "sha256": "57b2b7e0c62e84e3238b266e423e2008da76ec12ce957c173b2bbed11c65eb78",
      "sigstore_bundle": "vmaf_tiny_v3.onnx.sigstore.json"
    },
    {
      "description": "Tiny VMAF fusion regressor \u2014 canonical-6 features + bundled StandardScaler + mlp_large (~3073 params).",
      "id": "vmaf_tiny_v4",
      "int8_sha256": "203a25905a3797b1cc3e3f347f4f6f491b491000d4424017beef64219767a9e9",
      "kind": "fr",
      "license": "BSD-3-Clause-Plus-Patent",
      "license_url": "https://github.com/VMAFx/vmafx/blob/master/LICENSE",
      "notes": "vmaf_tiny_v4 \u2014 mlp_large (6\u219264\u219232\u219216\u21921) ~12x v2 / ~4x v3 capacity over canonical-6. 90ep Adam lr=1e-3 MSE bs=256. StandardScaler baked into ONNX graph. Trained 4-corpus parquet. NF LOSO PLCC 0.9987\u00b10.0015. Dynamic-PTQ int8 sidecar via ai/scripts/ptq_dynamic.py (ADR-0275). See docs/ai/models/vmaf_tiny_v4.md.",
      "onnx": "vmaf_tiny_v4.onnx",
      "opset": 17,
      "quant_accuracy_budget_plcc": 0.01,
      "quant_mode": "dynamic",
      "sha256": "2bccd339caa08f25433120546e45f2cb435288edeeefeafc56f3668986904e8f",
      "sigstore_bundle": "vmaf_tiny_v4.onnx.sigstore.json"
    }
  ],
  "schema_version": 1
}