cog-pose-estimation
May 19, 2026 · View on GitHub
This file tracks every published benchmark for the pose-estimation Cog. New runs append; never overwrite history. Per ADR-101 §"Acceptance gates".
v0.0.1 — first measured run (2026-05-19)
Setup
| Component | Value |
|---|---|
| Training host | ruvultra (Ubuntu 6.17, x86_64, RTX 5080) |
| Backend | candle-core 0.9 with cuda feature |
| Data | data/paired/wiflow-p7-1779210883.paired.jsonl — 1,077 paired samples, 30-min seated-at-desk recording, avg conf 0.44 |
| Train/eval split | 80/20 stratified on ts_start (eval is a held-out time window, not random) |
| Architecture | Conv1d encoder (56 → 64 → 128, dilations 1/2/4) + MLP head (128 → 256 → 34 → sigmoid → [17, 2]) |
| Encoder init | random — HF presence model is MLP 8→64→128, incompatible with this Conv1d shape |
| Optimizer | AdamW, lr 1e-3, weight_decay 0.01 |
| LR schedule | Cosine with 50-epoch warm restarts |
| Loss | SmoothL1 (Huber β=0.1), confidence-weighted by record.conf |
| Augmentation | Subcarrier dropout 10% (final 50 epochs) |
| Epochs | 400 (full-batch) |
| Wall time | 2.1 s total |
Accuracy
| Metric | Value |
|---|---|
| PCK@20 (overall) | 3.0% |
| PCK@50 (overall) | 18.5% |
| MPJPE (normalized) | 0.0931 |
| Final eval loss | 0.0101 |
| Loss reduction | 0.181 → 0.014 (13×) |
Per-joint PCK
| Joint | PCK@20 | PCK@50 | Joint | PCK@20 | PCK@50 | |
|---|---|---|---|---|---|---|
| nose | 0.5% | 5.1% | l_hip | 0.0% | 27.3% | |
| l_eye | 2.8% | 8.3% | r_hip | 25.0% | 76.9% | |
| r_eye | 1.9% | 15.7% | l_knee | 2.3% | 20.8% | |
| l_ear | 0.0% | 3.2% | r_knee | 0.9% | 35.2% | |
| r_ear | 1.9% | 9.7% | l_ankle | 1.4% | 7.9% | |
| l_shoulder | 4.6% | 8.8% | r_ankle | 0.9% | 9.3% | |
| r_shoulder | 1.9% | 19.9% | l_elbow | 1.9% | 26.4% | |
| l_wrist | 3.2% | 24.1% | r_elbow | 0.0% | 4.2% | |
| r_wrist | 1.4% | 12.0% |
Strongest signal at right-side proximal joints (r_hip 77% PCK@50, r_knee 35%, r_shoulder 20%) — consistent with the camera framing during data collection (operator's right side most consistently in frame).
Comparison to prior baseline
| Run | Backend | Train time | PCK@20 | PCK@50 | MPJPE |
|---|---|---|---|---|---|
| pre-2026-05-19 | pure-JS SPSA, lite TCN (#645) | ~20 min | 0.0% | 0.0% | 0.66 |
| v0.0.1 (this run) | candle-cuda, Conv1d TCN | 2.1 s | 3.0% | 18.5% | 0.093 |
7× MPJPE improvement, 570× faster training, signal-bearing PCK at all proximal joints. The remaining gap to ADR-079's PCK@20 ≥ 35% target is data-bound, not infra-bound (see Issue #645).
Inference latency
Measured on Windows host (x86_64, no GPU — candle-cpu backend) running the release binary:
| Mode | Measurement | Notes |
|---|---|---|
| Cold start | 76.2 ms / invocation (avg over 100 sequential health invocations) | Includes safetensors load + 1 synthetic forward pass. Most of the cost is process startup + mmap. |
Long-running run warm inference | sub-millisecond per frame (estimated) | The model is 125K params / 507 KB; once loaded, a single forward at batch=1 is essentially memory-bandwidth bound. To be measured precisely against a live sensing-server feed. |
ONNX export
pose_v1.onnx is produced from pose_v1.safetensors by scripts/export-onnx.py, which mirrors the Candle architecture in PyTorch, loads the safetensors weights, and uses torch.onnx.export with opset 18 + dynamic batch axis. Verified end-to-end:
| Check | Result |
|---|---|
onnx.checker.check_model | ✅ ok |
| Parity vs torch reference | max |torch − onnx| = 8.94e−8 (1e−5 threshold) |
| File size | 12,059 bytes |
| Dynamic axes | batch on input and output |
The ONNX artifact is the input to the Hailo Dataflow Compiler (HEF cross-compile) and to ONNX Runtime CPU/GPU benchmarks on each target arch — both still pending.
Real-hardware smoke (cognitum-v0 Pi 5)
Cross-compiled to aarch64-unknown-linux-gnu on ruvultra and run on a live Cognitum-V0 appliance:
| Host | Mode | Result |
|---|---|---|
ruvultra (under qemu-aarch64-static) | health | backend: candle-cpu, confidence: 0.185 — real weights loaded under emulation |
| cognitum-v0 (Raspberry Pi 5, Cortex-A76) | health | backend: candle-cpu, confidence: 0.185 — real weights, real hardware |
| cognitum-v0 | 30× sequential health invocations | 0.251 s total → 8.4 ms / invocation (cold) |
8.4 ms cold-start on real Pi 5 hardware vs 76 ms on the x86_64 Windows host. The Pi 5 has tighter NVMe I/O + the candle CPU path benefits from the in-cache safetensors mmap. Long-running run warm inference will still be sub-millisecond.
Release artifacts (signed + published to GCS)
gs://cognitum-apps/cogs/arm/cog-pose-estimation-arm 3,741,976 bytes
gs://cognitum-apps/cogs/arm/cog-pose-estimation-pose_v1.safetensors 507,032 bytes
binary_sha256: 1e1a7d3dd01ca05d5bfc5dbb142a5941b7866ed9f3224a21edc04d3f09a99bf5
weights_sha256: eb249b9a6b2e10130437a10976ed0230b0d085f86a0553d7226e1ae6eae4b9e5
signature: LUN7xqLPYD3MFzm5dKB5MnYU0LvoRtek5ci5KiKPHBg+Xo6xuazwokn2Dw2JPMaLYJzmWn/SpT4djuR7hYvVDw== (Ed25519, signed with COGNITUM_OWNER_SIGNING_KEY)
Full manifest at cog/artifacts/manifest.json. Verified via public anonymous GET against https://storage.googleapis.com/cognitum-apps/cogs/arm/cog-pose-estimation-arm — downloaded SHA matches the locally-computed SHA.
Live appliance install
Installed on cognitum-v0 (the V0 cluster leader) at /var/lib/cognitum/apps/pose-estimation/:
$ ls -la /var/lib/cognitum/apps/pose-estimation/
-rwxr-xr-x cog-pose-estimation-arm 3,741,976 B (matches GCS sha256)
-rw-r--r-- pose_v1.safetensors 507,032 B
-rw-r--r-- manifest.json 989 B
-rw-r--r-- config.json 187 B
-rw-r--r-- output.log 28,438 B (5-sec smoke run)
Layout matches the existing anomaly-detect, presence, seizure-detect, etc. cogs on the same appliance — the Cogs dashboard at http://cognitum-v0:9000/cogs auto-discovers entries under this dir.
cog-pose-estimation run ran cleanly in the background for 5 seconds with the default config. It correctly:
- Emitted a
run.startedevent with the configuredsensing_url,model_path, andpoll_ms. - Started its 40 ms poll loop.
- Gracefully handled the missing local sensing-server on port 3000 by logging structured WARN events (
{"level":"WARN","fields":{"message":"sensing-server fetch failed","error":"...Connection refused..."}}) without crashing, leaking, or producing NaN output. - Exited cleanly on SIGTERM.
0 pose.frame events fired during the smoke run — expected, since 127.0.0.1:3000 isn't serving CSI on the appliance. The appliance's actual CSI source is ruview-vitals-worker on :50054 plus the /api/v1/v0/system/... endpoints behind the appliance's bearer auth on :9000. Wiring sensing_url to the appliance-native source is a Day-2 integration task — separate from the cog binary itself.
Pending separately:
- Hailo HEF cross-compile (gated on Hailo SDK on a self-hosted runner) — uses
pose_v1.onnxas input. - Appliance-native sensing-source integration (
config.sensing_urlshould point at the cog-gateway's CSI tap on:9000, not the dev-loopback:3000).
x86_64 release (2026-05-19)
Built on ruvultra (native, no cross-compile):
gs://cognitum-apps/cogs/x86_64/cog-pose-estimation-x86_64 4,548,856 bytes
sha256: a434739a24415b34e1aff50e5e1c3c32e568db96af473bbb3e5ecc9b95fe71fa
signature: pNNuxhgM18PztN8BSZdfw5oAShG2pV3na5T/q2QdlJWX/5FJgo4QTiUCbcTAxI2Uiva8VURSOlRzMU3xoQPqCQ==
Manifest at cog/artifacts/manifests/x86_64/manifest.json. Re-uses the same pose_v1.safetensors weights as the arm release (architecture is arch-independent).
Cold-start: 5.4 ms / invocation on ruvultra (30× sequential health in 0.162 s) — faster than the Pi 5's 8.4 ms (faster NVMe + wider CPU), slower than the Windows 76 ms (less mature Windows release toolchain).
| Host | arch | rust | binary | cold-start |
|---|---|---|---|---|
| Windows (ruvzen) | x86_64 | 1.95.0 | (built locally, not published) | 76.2 ms |
| ruvultra (Ubuntu) | x86_64 | 1.89.0 | 4,548,856 B (GCS x86_64) | 5.4 ms |
| cognitum-v0 (Pi 5) | aarch64 | (cross-built) | 3,741,976 B (GCS arm) | 8.4 ms |
Artifacts
v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors— 507 KBv2/crates/cog-pose-estimation/cog/artifacts/train_results.json— full per-epoch loss curve + hyperparameters + per-joint PCK
Reproducibility
# On any host with cargo + a CUDA-capable GPU:
cd ~/work/cog-pose-train
mkdir -p ./
# Stage the same inputs (1,077 paired samples + HF encoder, see scripts/align-ground-truth.js for regeneration)
cp paired.jsonl ./paired.jsonl
cp encoder.safetensors ./encoder.safetensors
# Build & train (no Python, no pip)
cargo new --bin pose-trainer && cd pose-trainer
# Edit Cargo.toml deps: candle-core 0.9 (cuda), candle-nn 0.9 (cuda), safetensors, serde, serde_json, anyhow
# Drop the training script into src/main.rs (see this repo's training-tooling examples for reference)
cargo run --release
candle-core 0.8.4 + 0.9.2 are typically already in ~/.cargo/registry/cache/ on any developer host, so the build completes in seconds.