feat(cog-person-count): train count_v1.safetensors — honest v0.0.1 (ADR-103) (#695)

Phase 2 of ADR-103: trained count head on the existing 1,077 paired
samples (the same data that produced pose_v1 yesterday).

Honest result: 65.1% eval accuracy / 100% within ±1 / MAE 0.349 on
the held-out time-window. Per-class: 100% on "empty room" / 0% on
"1 person". The model overfit by epoch 100 (train_acc → 1.0,
eval_loss climbed 0.67 → 7.8) and the "best" checkpoint is the
snapshot that happened to predict the eval window's class
distribution (140/215 = 65.1%, matches eval_acc exactly). Confidence
head Spearman = 0.023 ⇒ uncalibrated. Same data-bound failure mode
as pose_v1 (#645), bounded by single-session training data; same
fix path (multi-room).

What v0.0.1 still validates end-to-end:
* PyTorch → safetensors → Candle Rust loads cleanly on first try.
  `cog-person-count health` reports `backend: candle-cpu` and emits
  real per-frame predictions instead of the stub backend's hard-coded
  {1 person, 0 confidence}. Architecture parity between train-count.py
  and src/inference.rs::CountNet is bit-exact.
* ONNX export bit-clean (16 KB, opset 18, dynamic batch axis).
* Training wall time: 5.6 s for 400 epochs on RTX 5080.
* Binary size unchanged (2.36 MB stripped), model loads via mmap at
  runtime.

This commit ships:

* scripts/align-ground-truth.js: extended to emit n_persons_mode +
  n_persons_max per window so the training pipeline has count
  labels. Backwards-compatible (additive fields).
* scripts/train-count.py: new — mirrors CountNet architecture
  exactly, loads paired.jsonl, trains 400 epochs with
  CE+BCE+Brier loss, exports safetensors + ONNX + per-epoch JSON.
* v2/.../cog/artifacts/{count_v1.safetensors,count_v1.onnx,
  count_train_results.json}: the trained artifacts.
* v2/.../cog/README.md: Status table updated with the v0.0.1 numbers
  + an Honest Caveat section explaining the data-bound result.
* docs/benchmarks/person-count-cog.md: new — full v0.0.1 benchmark
  log mirroring the format docs/benchmarks/pose-estimation-cog.md
  established. Includes comparison to ADR-103 v0.1.0 acceptance
  gates and per-class breakdown.

Still pending:
* `run` subcommand wiring (long-running polling loop, same as pose)
* Cross-compile + sign + GCS upload (mirror of pose cog pipeline)
* Live install on cognitum-v0
* v0.2.0: re-train on multi-room data, LoRA per-room adapters,
  Stoer-Wagner min-cut clip in fusion stage

v2/crates/cog-person-count/cog/README.md

@@ -27,19 +27,25 @@ Replaces the PR #491 slot heuristic (`subcarrier_diversity / dedup_factor`) with
 
 Downstream consumers can render the **most-likely count** when confidence is high, or fall back to a `[lo, hi]` band with a "?" badge when the model is uncertain — that's how this Cog closes the loop on #499's ghost-skeleton UX.
 
-## Status — v0.0.1 (this scaffold)
+## Status — v0.0.1
 
 | Component | State |
 |---|---|
 | Crate compiles, library API stable | ✅ |
-| Tests pass (`cargo test -p cog-person-count`) | ✅ |
+| Tests pass (15 total: 8 smoke + 7 fusion) | ✅ |
 | Four-verb runtime contract (`version`, `manifest`, `health`) | ✅ |
-| `run` subcommand (long-running loop) | ⏳ v0.0.1 follow-up |
-| Trained `count_v1.safetensors` artifact | ⏳ same training pipeline that produced `pose_v1` — bootstrap on the existing 1,077 paired samples |
-| Signed binary on GCS | ⏳ once trained |
+| Trained `count_v1.safetensors` artifact | ✅ shipped at `cog/artifacts/count_v1.safetensors` (392 KB) |
+| ONNX export | ✅ `count_v1.onnx` (16 KB), bit-compatible architecture |
+| Honest accuracy reporting | ✅ See `docs/benchmarks/person-count-cog.md` — 65.1% eval acc on a single-session dataset; confidence head Spearman 0.023 ⇒ uncalibrated for v0.0.1 |
+| `run` subcommand (long-running loop) | ⏳ same shape as cog-pose-estimation::runtime, lands in follow-up |
+| Signed binary on GCS | ⏳ release pipeline |
 | Stoer-Wagner min-cut clip in fusion stage | ⏳ v0.2.0 (hook in `fusion::fuse_with_mincut_clip` is stubbed) |
 
-The stub backend emits a "1 person, confidence 0" prediction so the dashboard surfaces "no model yet" honestly until the trained safetensors lands.
+### Honest v0.0.1 caveat
+
+`count_v1` was trained on a single 30-minute solo recording. The model overfit by epoch ~100 and the "best" checkpoint is one that effectively predicts the eval-window class distribution (mostly class-0). Class-1 accuracy on the held-out tail = 0%. **This v0.0.1 is a working pipeline with a degenerate model**, not a usable counter yet — same data-bound failure mode as `pose_v1` (#645), same fix: multi-room paired recordings.
+
+`cog-person-count health` will load the real safetensors and report `backend: candle-cpu` rather than `backend: stub`, so the cog-gateway can verify the model loaded — but operators should treat the v0.0.1 count outputs as scaffold-validation rather than production data. The 2.36 MB binary + 392 KB weights + 16 KB ONNX are all real and reusable as soon as more data lands.
 
 ## Security