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Merge #1387: Python components to SOTA (ADR-184..187) + QE hardening
Python components to SOTA: ADR-184..187 (pip publish, P6 bindings, training API, archive/v1 deprecation)
This commit is contained in:
Dragan Spiridonov
@@ -58,7 +58,7 @@ RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the
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> | 💓 **Heart rate** | Bandpass 0.8–2.0 Hz, zero-crossing BPM | 40–120 BPM, real-time |
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> | 👤 **Presence detection** | Trained head on Hugging Face ([`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained); v2 encoder = 82.3% held-out temporal-triplet acc, honestly re-benchmarked) + a phase-variance fallback that needs no model | < 1 ms, ~30 s ambient calibration |
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> | 🧬 **CSI embeddings** | 128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB | **164,183 emb/s** on M4 Pro |
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> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle. Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)). **SOTA on MM-Fi:** [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) hits **82.69% torso-PCK@20** (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi `random_split` protocol — self-corrected and auditable on [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena) | 8.4 ms cold-start on a Pi 5 |
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> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle (the committed `pose_v1` is a **first-cut** on-device model: PCK@20 = 3.0%, below the ADR-079 ≥35% target, and its runtime path is still a `confidence=0` stub — see [Model weights: what's real, what's not](#model-weights-whats-real-whats-not); the **82.69%** figure below is the separate published MM-Fi benchmark, not this live cog). Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)). **SOTA on MM-Fi:** [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) hits **82.69% torso-PCK@20** (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi `random_split` protocol — self-corrected and auditable on [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena) | 8.4 ms cold-start on a Pi 5 |
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> | 🚶 **Motion / activity** | Motion-band power + phase acceleration | Real-time |
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> | 🤸 **Fall detection** | Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |
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> | 🧮 **Multi-person count** | Adaptive P95 normalisation + runtime-tunable dedup factor (`/api/v1/config/dedup-factor`, [#491](https://github.com/ruvnet/RuView/pull/491)). Six specialised learned counters available as Cogs: `occupancy-zones`, `elevator-count`, `queue-length`, `customer-flow`, `clean-room`, `person-matching` | Real-time, self-calibrating |
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@@ -128,7 +128,7 @@ pip install "ruview[client]" # or: pip install "wifi-densepose[clie
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>
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> | Option | Hardware | Cost | Full CSI | Capabilities |
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> |--------|----------|------|----------|-------------|
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> | **ESP32 + Cognitum Seed** (recommended) | ESP32-S3 + [Cognitum Seed](https://cognitum.one) | ~$140 | Yes | Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy |
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> | **ESP32 + Cognitum Seed** (recommended) | ESP32-S3 + [Cognitum Seed](https://cognitum.one) | ~$140 | Yes | Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary — first-cut on-device model, see [Model weights: what's real, what's not](#model-weights-whats-real-whats-not)), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy |
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> | **ESP32 Mesh** | 3-6× ESP32-S3 + WiFi router | ~$54 | Yes | Same capabilities as above without the persistent-memory features |
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> | **ESP32-C6 research node** ([ADR-110](docs/adr/ADR-110-esp32-c6-firmware-extension.md), [witness](docs/WITNESS-LOG-110.md), [reviewer guide](docs/ADR-110-REVIEW-GUIDE.md), [firmware v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0-esp32)) | ESP32-C6-DevKit ($6–10) | ~$10 | Yes (Wi-Fi 6 capable) | Same CSI pipeline as S3 with the dual-target firmware. **Firmware-side ADR-110 substrate now closed** (v0.7.0): ESP-NOW cross-board mesh quantified at **99.56 % match / 104 µs smoothed offset stdev / 3.95× EMA suppression** over a 5-min two-board soak (witness §A0.10), 32-byte UDP sync packet with operator-tunable cadence (§A0.12), ADR-018 byte 19 bit 4 wire-fix sourced from the working ESP-NOW path (§A0.13). Wire format ready for HE-LTF PPDU tagging in ADR-018 bytes 18-19 (firmware encoder + Rust + Python decoders verified end-to-end across 23 unit tests). LP-core motion-gate RISC-V program and Wi-Fi 6 soft-AP with TWT Responder both ship as opt-in code paths (default off). **Hardware-gated for measurement**: HE-LTF live subcarrier capture needs an 11ax AP (IDF v5.4 doesn't expose AP-side HE config — §A0.6); ~5 µA LP-core hibernation needs an INA meter to capture; 802.15.4 raw RX is broken in IDF v5.4 (workaround: ESP-NOW transport, shipped + measured). See witness log for the empirical / claimed split. |
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> | **Research NIC** | Intel 5300 / Atheros AR9580 | ~$50-100 | Yes | Full CSI with 3x3 MIMO |
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@@ -145,7 +145,7 @@ pip install "ruview[client]" # or: pip install "wifi-densepose[clie
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<img src="assets/v2-screen.png" alt="WiFi DensePose — Live pose detection with setup guide" width="800">
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</a>
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<br>
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<em>Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables</em>
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<em>Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables (demo visualization; the live CSI-only single-ESP32 17-keypoint model is still first-cut — see <a href="#model-weights-whats-real-whats-not">Model weights: what's real, what's not</a>)</em>
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<br><br>
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<a href="https://ruvnet.github.io/RuView/"><strong>▶ Live Observatory Demo</strong></a>
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@@ -157,7 +157,7 @@ pip install "ruview[client]" # or: pip install "wifi-densepose[clie
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> The [server](#-quick-start) is optional for visualization and aggregation — the ESP32 [runs independently](#esp32-s3-hardware-pipeline) for presence detection, vital signs, and fall alerts.
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>
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> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](#sensing-server) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md).
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> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](#sensing-server) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md). (The webcam supplies ground-truth pose in this dual-modal demo; the CSI-only on-device 17-keypoint model is still first-cut — see [Model weights: what's real, what's not](#model-weights-whats-real-whats-not).)
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>
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> **three.js scene gallery** at [`/three.js/`](https://ruvnet.github.io/RuView/three.js/) — five progressively richer ADR-097 demos: helpers, cinematic, GLTF skinned, FBX skinned, and a live MediaPipe→Mixamo retargeting feed driven by ESP32 CSI. Demos 04 and 05 require a local Mixamo `X Bot.fbx` (license boundary — not redistributed).
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@@ -204,7 +204,26 @@ The separate **17-keypoint pose-estimation model** is now published at [`ruvnet/
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python archive/v1/data/proof/verify.py
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```
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Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) phases P7–P9 for the camera-supervised fine-tune path.
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Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-ground-truth-training.md) phases P7–P9 for the camera-supervised fine-tune path.
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### Model weights: what's real, what's not
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"WiFi → pose" means three different things in this repo, at three different maturity
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levels. Read the label, not the headline ([ADR-187](docs/adr/ADR-187-archive-v1-deprecation-honest-labeling.md)):
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| Tier | Checkpoint(s) | Honest status |
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|------|---------------|---------------|
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| **Real & validated** | [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) (CSI encoder + presence head) · [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) (17-keypoint pose) · `cog-person-count/count_v1` | **MEASURED / published.** Presence = 82.3% held-out temporal-triplet accuracy (the old "100% presence" figure was retracted); MM-Fi pose = 82.69% torso-PCK@20 on the `random_split` protocol. These are the pose/presence numbers the project stands behind today. |
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| **Real but weak (honestly labeled)** | committed `v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors` | First-cut on-device model. **PCK@20 = 3.0% / PCK@50 = 18.5%** on a 217-sample holdout — **below the ADR-079 target of ≥ 35%.** Learns coarse structure (`r_hip` 77% PCK@50); distal/face joints near-random. Its runtime path in `cog-pose-estimation/src/inference.rs` is still a centred-skeleton **stub returning `confidence=0`** — the weights are not yet wired in. Full disclosure in the [cog README](v2/crates/cog-pose-estimation/cog/README.md). |
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| **Architecture only, no weights** | `archive/v1` `DensePoseHead` | Random `kaiming_normal_` init, **no checkpoint of any kind** (zero `.pth`/`.onnx`/`.safetensors` files under `archive/v1/`). Deprecated and superseded — see [`archive/v1/DEPRECATED.md`](archive/v1/DEPRECATED.md). Do not expect real pose output from it. |
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**On the ESP32-SISO question ([#509](https://github.com/ruvnet/RuView/issues/509)):** a
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single-antenna, 56-subcarrier CSI stream at a 20-frame window does *not* carry the
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fine-grained spatial information the multi-antenna NIC research relies on — the cog
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measurements above show distal/face joints near-random. The shippable pose accuracy the
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project can stand behind today is the **MM-Fi benchmark number**, not a live single-ESP32
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number. The path to a first *reproducible* on-device baseline (PCK@20 ≥ 35%) is tracked in
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[ADR-079](docs/adr/ADR-079-camera-ground-truth-training.md) / [#645](https://github.com/ruvnet/RuView/issues/645) — do not advertise the live single-ESP32 17-keypoint feature without the "first-cut, below-target, runtime-stub" caveat until that baseline is measured.
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## 🧩 Edge Module Catalog
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