add M2 toolkit data: 1445 embeddings, 0 face_labels
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- README.md +79 -0
- arcface_embeddings/heldout/lecun/lecun_01.png.arcface.npy +3 -0
- arcface_embeddings/heldout/lecun/lecun_02.png.arcface.npy +3 -0
- arcface_embeddings/heldout/lecun/lecun_03.png.arcface.npy +3 -0
- arcface_embeddings/heldout/lecun/lecun_04.png.arcface.npy +3 -0
- arcface_embeddings/heldout/lecun/lecun_05.png.arcface.npy +3 -0
- arcface_embeddings/heldout/obama/obama_01.png.arcface.npy +3 -0
- arcface_embeddings/heldout/obama/obama_02.png.arcface.npy +3 -0
- arcface_embeddings/heldout/obama/obama_03.png.arcface.npy +3 -0
- arcface_embeddings/heldout/obama/obama_04.png.arcface.npy +3 -0
- arcface_embeddings/heldout/swift/swift_01.png.arcface.npy +3 -0
- arcface_embeddings/heldout/swift/swift_02.png.arcface.npy +3 -0
- arcface_embeddings/heldout/swift/swift_03.png.arcface.npy +3 -0
- arcface_embeddings/heldout/swift/swift_04.png.arcface.npy +3 -0
- arcface_embeddings/heldout/swift/swift_05.png.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/01.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/02.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/03.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/04.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/05.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/06.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/07.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/08.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/09.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/adam_sandler/10.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/01.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/02.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/03.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/05.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/06.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/07.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/08.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/09.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/al_pacino/10.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrej_karpathy/01.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrej_karpathy/02.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrej_karpathy/03.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrej_karpathy/05.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrej_karpathy/06.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrej_karpathy/face_crop_03.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrej_karpathy/face_crop_06.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrew_ng/01.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrew_ng/02.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrew_ng/03.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrew_ng/04.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrew_ng/05.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrew_ng/06.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrew_ng/07.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andrew_ng/09.jpg.arcface.npy +3 -0
- arcface_embeddings/scraped/andy_jassy/01.jpg.arcface.npy +3 -0
README.md
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# Eval 3 — M2 ArcFace toolkit data
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Training-time-only artifacts for the M2 ArcFace cosine-distillation loss
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(`dev/m2-arcface-toolkit` branch).
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**Inference contract is unchanged.** This data is consumed only inside
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the training-time dataloader. The deployed policy graph contains
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`SmolVLA` alone — no ArcFace, no RetinaFace, no InsightFace.
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## Contents
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| Path | What | Size |
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|---|---|---|
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| `celeb_embeddings.json` | Manifest: per-photo paths + per-celeb 512-D centroid (mean over the celeb's photos, L2-normalized) | ~3 MB |
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| `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 |
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| `arcface_embeddings/scraped/<celeb>/<photo>.arcface.npy` | Per-photo embeddings for 189 OOD celebs from the web-scraped bank | ~2.8 MB |
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| `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 |
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## How it was built
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1. **ArcFace cache:** `eval_3/aug/cache_arcface_embeddings.py` ran InsightFace
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`buffalo_l` on every photo in `eval3_celebs/{heldout,scraped}/`,
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keeping the largest detected face. 1,445 embeddings, 0 failures.
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Leave-one-out top-1 identity recall on the full bank: **99.5 %**.
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2. **Face labels:** `eval_3/aug/build_face_labels.py` grouped the 9,216
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augmented variants by source-episode prefix (151 unique sources, ~60
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variants each share camera trajectory because the camera is
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fixed), ran RetinaFace at `det_size=640` and `stride=5` (linear bbox
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interpolation between keyframes) on one representative camera1 video
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per source, and emitted per-frame bboxes sorted left-to-right by
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x-center.
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## How to use at training time
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```python
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import json, numpy as np
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from pathlib import Path
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# Step 1: load the manifest
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manifest = json.loads((repo_root / "celeb_embeddings.json").read_text())
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# Step 2: per-celeb centroid for the alignment-loss target
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def celeb_centroid(slug: str) -> np.ndarray:
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return np.asarray(manifest["celebs"][slug]["centroid"], dtype=np.float32)
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# Step 3: per-source bboxes
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def load_face_labels(source_episode: str) -> dict:
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p = repo_root / "face_labels" / f"{source_episode}.face_labels.json"
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return json.loads(p.read_text())
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# Step 4: at each training step, join via the variant's augmentation.json:
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# - read augmentation.json[new_layout_camera_lmr] (e.g. "OLS")
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# - look up "OLS" → [obama@left, lecun@middle, swift@right]
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# - load face_labels[source].frames[frame_idx].bboxes (sorted left-to-right)
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# - per-bbox supervision target = ArcFace centroid of the celeb at that slot
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```
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## Identity-matching reliability
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The matcher (RetinaFace bbox → ArcFace embedding → nearest centroid in
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the 192-celeb gallery) was validated under leave-one-out:
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| Bucket (photos/celeb) | LOO top-1 | Mean top1−top2 margin |
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|---|---|---|
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| 2 | 100 % | +0.36 |
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| 3–4 | 100 % | +0.59 |
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| 5–7 | 100 % | +0.60 |
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| 8–12 | 99.2 % | +0.60 |
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| 13+ | 100 % | +0.68 |
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The 7 misses (out of 1,445) cluster on one celeb (`oier_mees`) whose
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scraped bank is structurally broken (intra-celeb own-photo cosines
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0.05–0.13). Flag him before any reliance on OOD coverage.
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## Branch & docs
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- Source branch: [`dev/m2-arcface-toolkit`](https://github.com/Ace3Z/LeMonkey/tree/dev/m2-arcface-toolkit)
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- Experiment log: `docs/experiments/2026-05-19_m2_data_foundation.md`
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- Validation report: `docs/report/2026-05-18_m2_arcface_validation.md`
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