mirror of
https://github.com/ruvnet/RuView.git
synced 2026-05-20 01:19:38 +00:00
fix(docs): correct unsubstantiated 92.9% PCK@20 camera-supervised claim (#535)
The README claimed "92.9% PCK@20" for camera-supervised pose training. That
figure appears nowhere in ADR-079 (the source ADR) and is ~2.6x the ADR's own
success target (">35% PCK@20"). ADR-079 phases P7 (data collection), P8
(training + evaluation on real paired data) and P9 (cross-room LoRA) are all
still `Pending`, so no measured camera-supervised PCK@20 has been published.
- README: replace the two "92.9% PCK@20" claims with the proxy-supervised
baseline (~2.5%) and the ADR-079 target (35%+), noting the eval phases are
pending.
- CHANGELOG: add an Unreleased entry.
Surfaced by the PowerPlatePulse training-pipeline audit (2026-05-11). Six other
audit findings (vitals features absent from training; wifi-densepose-signal
ghost dep; PIR/BME280 in MODEL_CARD unimplemented; proof.rs uses
SyntheticCsiDataset only; 56-subcarrier/1-NIC default; multi-band 168-subcarrier
mesh not in training config) are listed in the PR body for follow-up.
This commit is contained in:
parent
df9d3b0eea
commit
bd4f81749a
2 changed files with 13 additions and 2 deletions
11
CHANGELOG.md
11
CHANGELOG.md
|
|
@ -7,6 +7,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
|||
|
||||
## [Unreleased]
|
||||
|
||||
### Fixed
|
||||
- **README: corrected the camera-supervised pose-accuracy claim.** The README stated
|
||||
"92.9% PCK@20" for camera-supervised training; that figure does not appear in
|
||||
ADR-079 and is ~2.6× the ADR's own success target (>35% PCK@20). ADR-079 phases
|
||||
P7 (data collection), P8 (training + evaluation on real paired data) and P9
|
||||
(cross-room LoRA) are still `Pending`, so no measured camera-supervised PCK@20 has
|
||||
been published. README now states the proxy-supervised baseline (≈2.5%) and the
|
||||
ADR-079 target (35%+), and notes the eval phases are pending. Surfaced by the
|
||||
PowerPlatePulse training-pipeline audit (2026-05-11); 6 remaining audit findings
|
||||
tracked in the PR.
|
||||
|
||||
### Added
|
||||
- **`nvsim` crate — deterministic NV-diamond magnetometer pipeline simulator** (ADR-089) —
|
||||
New standalone leaf crate at `v2/crates/nvsim` modeling a forward-only
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@
|
|||
> **Beta Software** — Under active development. APIs and firmware may change. Known limitations:
|
||||
> - ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
|
||||
> - Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a [Cognitum Seed](https://cognitum.one) for best results
|
||||
> - Camera-free pose accuracy is limited — use [camera ground-truth training](docs/adr/ADR-079-camera-ground-truth-training.md) for 92.9% PCK@20
|
||||
> - Camera-free pose accuracy is limited (PCK@20 ≈ 2.5% with proxy labels) — [camera ground-truth training](docs/adr/ADR-079-camera-ground-truth-training.md) targets **35%+ PCK@20**; the pipeline is implemented, but the data-collection and evaluation phases (ADR-079 P7–P9) are still pending, so no measured camera-supervised PCK@20 has been published yet
|
||||
>
|
||||
> Contributions and bug reports welcome at [Issues](https://github.com/ruvnet/RuView/issues).
|
||||
|
||||
|
|
@ -56,7 +56,7 @@ RuView also supports pose estimation (17 COCO keypoints via the WiFlow architect
|
|||
> | 🧱 **Through-wall** | Fresnel zone geometry + multipath modeling | Up to 5m depth |
|
||||
> | 🧠 **Edge intelligence** | 8-dim feature vectors + RVF store on Cognitum Seed | $140 total BOM |
|
||||
> | 🎯 **Camera-free training** | 10 sensor signals, no labels needed | 84s on M4 Pro |
|
||||
> | 📷 **Camera-supervised training** | MediaPipe + ESP32 CSI → 92.9% PCK@20 | 19 min on laptop |
|
||||
> | 📷 **Camera-supervised training** | MediaPipe + ESP32 CSI → **35%+ PCK@20 target** (ADR-079; eval phases pending) | ~19 min on laptop (pipeline) |
|
||||
> | 📡 **Multi-frequency mesh** | Channel hopping across 6 bands, neighbor APs as illuminators | 3x sensing bandwidth |
|
||||
> | 🌐 **3D point cloud** *(optional fusion)* | Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model | 22 ms pipeline · 19K+ points/frame |
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue