| # 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 |
|
|
| ```python |
| 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`](https://github.com/Ace3Z/LeMonkey/tree/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` |
|
|
