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rUv
6b4994e105
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