ruview @ v1036

Automated release from CI pipeline

Changes: feat(cog-person-count): v0.0.2 — K-fold + label-smoothing + temperature-calibrated (#699)

• chore: stage v0.0.2 artifacts + temperature scalar for build pipeline

Stages count_v1.{safetensors,onnx,temperature,train_results.json} ahead of the build/sign/upload step. This commit is a momentary side-effect — the next commit will refresh the per-arch manifests with the new binary SHAs once ruvultra finishes the cross-build.

The .temperature file holds the calibration scalar from LBFGS over the held-out conf logits. The Rust cog will read it post-load and divide conf_logits by it before sigmoid, exactly matching the Python eval.

• feat(cog-person-count): v0.0.2 — K-fold validated, label smoothing + early stop + temp scale

The v0.0.1 "65.1% but class-1=0%" result was an unlucky temporal split that let a degenerate "always predict 0" classifier hit eval acc = class-0 fraction. 5-fold stratified random CV proved the architecture actually learns ~57.1% class-1 accuracy under fair splits — a real, modestly useful signal.

v0.0.2 ships a retrained model that:

• Splits randomly (seed=42) 80/20 instead of temporally — eliminates the trailing-window-class-imbalance cheat.
• Class-balanced sampler (multinomial with replacement, weighted by inverse class frequency) — per-batch expected counts are equal regardless of dataset distribution.
• Label smoothing 0.1 on the cross-entropy — reduces confidence saturation that drove v0.0.1's all-or-nothing predictions.
• Early stopping with patience=20 — stops at epoch 29 instead of overfitting through 400.
• Temperature scaling of the conf head — LBFGS fits a scalar T on held-out conf logits; ships as a count_v1.temperature sidecar so the Rust cog can divide conf_logits by T before sigmoid.

Numbers on the same data:

Class-1 accuracy 0 → 34.3% is the headline win. Net acc moves slightly because we stopped cheating on class 0. K-fold's 57% says there's headroom remaining; reaching it needs more independent splits (== more data), not more training tricks.

Confidence calibration didn't move. Temperature scaling alone can't fix a confidence head trained against a noisy argmax==truth indicator over a 62%-accurate classifier — the head's training signal is the issue, not its post-hoc transform. The honest fix is multi-room data (#645), not another calibration knob.

Live on cognitum-v0 at /var/lib/cognitum/apps/person-count/ — health reports candle-cpu backend, count = 1 (was 0 in v0.0.1) on synthetic zero input.

Files changed:

• scripts/train-count.py — adds --k-fold (no sklearn dep, hand-rolled stratified splits with deterministic shuffle) and --v2 paths.
• v2/.../cog/artifacts/count_v1.safetensors (392 KB, new sha 32996433…) + count_v1.onnx (16 KB) + count_v1.temperature (0.9262 scalar) + count_train_results.json (full epoch trace).
• v2/.../cog/artifacts/manifests/{arm,x86_64}/manifest.json bumped to version 0.0.2 with the new weights_sha256 + caveats.
• docs/benchmarks/person-count-cog.md — appends a v0.0.2 section with the K-fold diagnostic table and honest-read paragraph.

GCS: gs://cognitum-apps/cogs/arm/cog-person-count-count_v1.safetensors refreshed (binaries unchanged — load weights via mmap at runtime).

Docker Image: ghcr.io/ruvnet/RuView:b3a5012dbd1db43c444b12d1eae367f95c71ba4e