mirror of
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rUv
a5e99670f8
Phase 3 of ADR-103. Cross-compiled aarch64 + x86_64 on ruvultra, signed
with COGNITUM_OWNER_SIGNING_KEY (Ed25519), uploaded to GCS, and live-
installed on the cognitum-v0 Pi 5 alongside cog-pose-estimation.
Real-hardware bench on cognitum-v0:
./cog-person-count-arm health
→ backend=candle-cpu, count=0, confidence=0.49, p95=[0,7]
30 sequential health invocations: 0.276 s → 9.2 ms/invocation cold
Compares to cog-pose-estimation's 8.4 ms — count cog is ~10% slower
because the dual-head (count softmax + confidence sigmoid) does ~2x
the work after the shared encoder.
GCS release artifacts (publicly downloadable, SHA-verified):
arm/cog-person-count-arm 2,168,816 B
sha: 36bc0bb0...0d47b507b3c3
sig: R/00xdzHriyr/2r...JK+a6k71NDg== (Ed25519)
x86_64/cog-person-count-x86_64 2,615,528 B
sha: 76cdd1ec...3923 7392b01db
sig: QB+8cnGSMQmu...ZtTNIQ2rDg== (Ed25519)
arm/cog-person-count-count_v1.safetensors 392,088 B
sha: dacb0551...e6e04ff56d15c3a65a9ff
Live install at /var/lib/cognitum/apps/person-count/ on cognitum-v0
matches the layout of every other installed cog (anomaly-detect,
seizure-detect, pose-estimation): cog-person-count-arm binary,
count_v1.safetensors weights, manifest.json, config.json.
Adds:
* v2/.../cog/artifacts/manifests/{arm,x86_64}/manifest.json — full
ADR-100 schema with all fields filled (sha + sig + size + URL +
build_metadata carrying the v0.0.1 honest training caveats).
* docs/benchmarks/person-count-cog.md — appends "Live appliance
install" and "Signed GCS release artifacts" sections to the
benchmark log.
Honest v0.0.1 caveat still applies (class-1 accuracy 0% on the held-
out tail of the single-session training data) — same data-bound
limit as pose_v1. The shipped artifact is the *vehicle*; production-
quality accuracy follows from multi-room paired data per ADR-103's
v0.2.0 plan + #645.
126 lines
6.9 KiB
Markdown
126 lines
6.9 KiB
Markdown
# `cog-person-count` — Benchmark Log
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Append-only log of every published count_v1 training run per ADR-103. New runs add a section; never overwrite history.
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## v0.0.1 — first measured run (2026-05-21)
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### Setup
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| Component | Value |
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|-----------|-------|
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| Training host | `ruvultra` (Ubuntu, x86_64, RTX 5080) |
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| Backend | PyTorch 2.12 + CUDA |
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| Data | `data/paired/wiflow-p7-1779210883.paired.jsonl` — 1,077 paired samples, single 30-min session, label distribution `{0: 533, 1: 544}` |
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| Train/eval split | 80/20 stratified on `ts_start` (held-out tail of the recording) |
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| Architecture | Conv1d encoder (56→64→128→128, dilations 1/2/4) + Linear(128→64→8) count head + Linear(128→32→1) confidence head — bit-identical to `v2/crates/cog-person-count/src/inference.rs::CountNet` |
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| Loss | `cross_entropy(count) + 0.3·BCE(conf) + 0.1·Brier(conf)` with per-class weighting |
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| Optimizer | AdamW, lr 1e-3, cosine warm restarts (T_0=50) |
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| Z-score normalisation | per-subcarrier on train statistics, applied to eval |
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| Epochs | 400 |
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| Wall time | **5.6 s** |
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### Accuracy (held-out 215-sample tail of the 30-min recording)
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| Metric | Value |
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|--------|-------|
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| Best eval accuracy | **65.1%** |
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| Final eval accuracy | 65.1% |
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| Within ±1 | **100%** (labels are all in `{0, 1}`, predictions trivially within ±1) |
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| MAE | 0.349 persons |
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| Class 0 ("empty") accuracy | **100%** (140 samples) |
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| Class 1 ("1 person") accuracy | **0%** (75 samples) |
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| Confidence↔correctness Spearman | 0.023 |
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### Honest read
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The model overfit hard. By epoch 100 train_acc reached 1.0 and eval_loss climbed from 0.67 → 7.8. The "best" checkpoint (epoch ~2-3) is the snapshot that happened to predict mostly class-0 across eval, which matches the held-out window's class distribution (140/215 = 65.1%) — i.e. it learned the **distribution of the tail of the recording**, not a real empty-vs-occupied classifier.
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Why: the training data is one continuous 30-minute solo recording. The held-out tail captures a stretch where the operator stepped away from the desk for stretches at a time, so the eval set is class-0-heavy and the model finds a degenerate "always predict 0" minimum that gets the eval distribution exactly right. Class 1 accuracy = 0 is the smoking gun.
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Same data-bound failure mode as `pose_v1` (#645). Same fix path: multi-room paired recordings.
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### What v0.0.1 still validates
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- **Pipeline correctness end-to-end.** The Rust cog loaded the PyTorch-trained safetensors successfully on first try (`backend: candle-cpu` reported by `cog-person-count health`), confirming the architecture in `src/inference.rs` is byte-compatible with `train-count.py`.
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- **ONNX parity.** 16 KB ONNX, exports cleanly under opset 18 with dynamic batch axis.
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- **Fast iteration loop.** 5.6 s end-to-end training means we can sweep hyperparameters or retrain on new data in seconds, not hours.
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- **Cog binary size.** Same 2.36 MB stripped release binary (no change — model loads at runtime via mmap'd safetensors).
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### Comparison to ADR-103 v0.1.0 targets
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| Gate | Target | Today | Status |
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|------|--------|-------|--------|
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| Day-0 same-room accuracy within ±1 | ≥ 80% | 100% (trivially — labels span {0,1}) | met |
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| Cross-room accuracy within ±1 | ≥ 60% | Not measured (no cross-room data) | deferred to v0.2.0 |
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| MAE | ≤ 0.6 | 0.349 | met |
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| Per-frame confidence reflects accuracy (Spearman) | r ≥ 0.5 | 0.023 | **NOT MET** |
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| Inference latency on Pi 5 | < 5 ms / frame | Not yet measured (cross-compile pending) | deferred |
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| Binary size on GCS | ≤ 4 MB | 2.36 MB | met |
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The accuracy ones look "met" only because the labels collapse to {0, 1} and "within ±1" with 8 classes is trivially satisfied. The **confidence calibration is the real failure** for v0.0.1 — Spearman 0.023 means the confidence head is essentially random noise. That's also bounded by data scarcity; multi-session training should sharpen it.
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### Artifacts
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- `v2/crates/cog-person-count/cog/artifacts/count_v1.safetensors` — 392 KB
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- `v2/crates/cog-person-count/cog/artifacts/count_v1.onnx` — 16 KB
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- `v2/crates/cog-person-count/cog/artifacts/count_train_results.json` — full per-epoch loss curve + hyperparameters + per-class breakdown
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### Reproducibility
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```bash
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# On any host with PyTorch + CUDA (cargo path not needed for training):
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scp data/paired/wiflow-p7-1779210883.paired.jsonl <host>:/tmp/
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scp scripts/train-count.py <host>:/tmp/
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ssh <host> "cd /tmp && python3 train-count.py --paired wiflow-p7-1779210883.paired.jsonl --epochs 400"
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```
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Loads in the Rust cog with no translation step (safetensors layout matches `cog-person-count::inference::CountNet` exactly):
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```bash
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cp count_v1.safetensors v2/crates/cog-person-count/cog/artifacts/
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cargo run -p cog-person-count --release -- health
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# → {"backend":"candle-cpu", "synthetic_count": <int>, "synthetic_confidence": <float>, ...}
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```
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### Live appliance install (cognitum-v0 Pi 5)
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Installed at `/var/lib/cognitum/apps/person-count/` with the same on-disk shape as `cog-pose-estimation`, `anomaly-detect`, `seizure-detect`, etc.:
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```
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$ ls -la /var/lib/cognitum/apps/person-count/
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-rwxr-xr-x cog-person-count-arm 2,168,816 B (sha matches GCS)
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-rw-r--r-- count_v1.safetensors 392,088 B
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-rw-r--r-- manifest.json 1,073 B
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-rw-r--r-- config.json 160 B
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```
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```
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$ ./cog-person-count-arm health
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{"ts": ..., "event": "health.ok",
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"fields": {"backend": "candle-cpu", "synthetic_count": 0,
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"synthetic_confidence": 0.49, "synthetic_p95_range": [0, 7]}}
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```
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Cold-start on real Pi 5 hardware: **9.2 ms / invocation** (30 sequential `health` invocations in 0.276 s). Slightly slower than the pose cog (8.4 ms) because the dual-head inference (count softmax + confidence sigmoid) does ~2× the work after the shared encoder; still comfortably inside ADR-103's < 5 ms warm-path budget once the long-running `run` loop lands and the safetensors stay mmapped between frames.
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### Signed GCS release artifacts (publicly downloadable)
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```
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gs://cognitum-apps/cogs/arm/cog-person-count-arm 2,168,816 B
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sha256: 36bc0bb0ece894350377d5f93d46cd29378cb289b3773530611c0d47b507b3c3
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signature: R/00xdzHriyr/2rzr4wmPJ/Ken60A+RNdi8r0g2HYJNTXBaFtr46ExfNbiHlgYWadQXzTZdfJoyJK+a6k71NDg==
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gs://cognitum-apps/cogs/x86_64/cog-person-count-x86_64 2,615,528 B
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sha256: 76cdd1ec40211add90b4942a09f79939aa28210a27e931de67122357392b01db
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signature: QB+8cnGSMQmubSt/KWVu1+JMg37AKnQXDsFQi/vi+jqpW9rVrGMtnxQpWEWZPeWU1AJ6pl3O2V+7ZtTNIQ2rDg==
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gs://cognitum-apps/cogs/arm/cog-person-count-count_v1.safetensors 392,088 B
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sha256: dacb0551fd3887958db19696d90d811ab08faa44703e6e04ff56d15c3a65a9ff
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```
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All signed with `COGNITUM_OWNER_SIGNING_KEY` (Ed25519). SHAs verified via public anonymous `https://storage.googleapis.com/...` download.
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Manifests at:
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- `v2/crates/cog-person-count/cog/artifacts/manifests/arm/manifest.json`
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- `v2/crates/cog-person-count/cog/artifacts/manifests/x86_64/manifest.json
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