HBOrtiz/eval3_m2_arcface_toolkit

Eval 3 — M2 ArcFace toolkit data

Training-time-only artifacts for the M2 ArcFace cosine-distillation loss (dev/m2-arcface-toolkit branch).

Inference contract is unchanged. This data is consumed only inside the training-time dataloader. The deployed policy graph contains SmolVLA alone — no ArcFace, no RetinaFace, no InsightFace.

Contents

Path	What	Size
celeb_embeddings.json	Manifest: per-photo paths + per-celeb 512-D centroid (mean over the celeb's photos, L2-normalized)	~3 MB
arcface_embeddings/heldout/<celeb>/<photo>.arcface.npy	Per-photo ArcFace buffalo_l embeddings for the 3 IID celebs' workspace photos (14 photos × 3 celebs)	~30 KB
arcface_embeddings/scraped/<celeb>/<photo>.arcface.npy	Per-photo embeddings for 189 OOD celebs from the web-scraped bank	~2.8 MB
face_labels/<source_episode>.face_labels.json	Per-frame face bboxes for one representative variant per source episode (151 sources × ~250 KB each)	~39 MB

How it was built

1. ArcFace cache: eval_3/aug/cache_arcface_embeddings.py ran InsightFace buffalo_l on every photo in eval3_celebs/{heldout,scraped}/, keeping the largest detected face. 1,445 embeddings, 0 failures. Leave-one-out top-1 identity recall on the full bank: 99.5 %.
2. Face labels: eval_3/aug/build_face_labels.py grouped the 9,216 augmented variants by source-episode prefix (151 unique sources, ~60 variants each share camera trajectory because the camera is fixed), ran RetinaFace at det_size=640 and stride=5 (linear bbox interpolation between keyframes) on one representative camera1 video per source, and emitted per-frame bboxes sorted left-to-right by x-center.

How to use at training time

import json, numpy as np
from pathlib import Path

# Step 1: load the manifest
manifest = json.loads((repo_root / "celeb_embeddings.json").read_text())

# Step 2: per-celeb centroid for the alignment-loss target
def celeb_centroid(slug: str) -> np.ndarray:
    return np.asarray(manifest["celebs"][slug]["centroid"], dtype=np.float32)

# Step 3: per-source bboxes
def load_face_labels(source_episode: str) -> dict:
    p = repo_root / "face_labels" / f"{source_episode}.face_labels.json"
    return json.loads(p.read_text())

# Step 4: at each training step, join via the variant's augmentation.json:
#   - read augmentation.json[new_layout_camera_lmr] (e.g. "OLS")
#   - look up "OLS" → [obama@left, lecun@middle, swift@right]
#   - load face_labels[source].frames[frame_idx].bboxes (sorted left-to-right)
#   - per-bbox supervision target = ArcFace centroid of the celeb at that slot

Identity-matching reliability

The matcher (RetinaFace bbox → ArcFace embedding → nearest centroid in the 192-celeb gallery) was validated under leave-one-out:

Bucket (photos/celeb)	LOO top-1	Mean top1−top2 margin
2	100 %	+0.36
3–4	100 %	+0.59
5–7	100 %	+0.60
8–12	99.2 %	+0.60
13+	100 %	+0.68

The 7 misses (out of 1,445) cluster on one celeb (oier_mees) whose scraped bank is structurally broken (intra-celeb own-photo cosines 0.05–0.13). Flag him before any reliance on OOD coverage.

Branch & docs

• Source branch: dev/m2-arcface-toolkit
• Experiment log: docs/experiments/2026-05-19_m2_data_foundation.md
• Validation report: docs/report/2026-05-18_m2_arcface_validation.md