ADR-187: archive/v1 Deprecation & Model-Weights Honest Labeling

• Status: Accepted
• Date: 2026-07-21
• Deciders: ruv
• Tags: archive-v1, deprecation, densepose-head, model-weights, honest-labeling, prove-everything, credibility, pip-tombstone
• Refs: #509 (missing model weights / reproducibility), #1125 ("has anyone got this to work?")
• Relates to: ADR-117 (pip modernization + 1.99.0 tombstone), ADR-160 (honest-labeling precedent), ADR-079 (camera-supervised pose target), ADR-152 (WiFlow-STD PCK@20 measurement), ADR-175 (int8 pose trade-off), ADR-101 (pose cog)

---

Context

Two open GitHub issues are, at root, the same complaint: the project's public surface lets a reader believe a WiFi→17-keypoint pose model exists and produces real accuracy, when the specific code they land on cannot back that claim.

• #509 — a detailed technical review states: "While the network architecture for DensePoseHead is defined in the code, there are no pre-trained weights (.pth or .onnx files) available in the repository," and questions whether ESP32 1×1 SISO antennas can match the multi-antenna NIC research this project is inspired by.
• #1125 — a user asks for anyone to testify the project actually runs and returns real data. A pure credibility complaint.

This ADR follows the prove-everything / anti-"AI-slop" directive and the honest-labeling precedent set by ADR-160: the fix is to make the labels TRUE, not to fabricate a capability. Grading vocabulary (from ADR-152 / ADR-160):

• MEASURED — reproduced in this worktree; the file/absence was directly inspected.
• DATA-GATED — a real code path exists; honestly flagged where the accuracy is not validated.
• NO-ACTION (already-honest) — audited, found correct, cited as a positive.

What the investigation actually found (MEASURED in this worktree)

The situation is more nuanced than either issue implies — worse in one place, and distinctly better in others. Forcing a uniformly negative narrative would itself be dishonest. The findings:

1. archive/v1 — the issue reporter is correct here.

• archive/v1/src/models/densepose_head.py defines DensePoseHead (segmentation + UV-regression heads). Its _initialize_weights() uses kaiming_normal_ random initialization only — there is no checkpoint-loading path in the class.
• Glob archive/v1/**/*.{pth,onnx,safetensors,pt,ckpt,bin} → zero files. There are no trained weights anywhere under archive/v1/. The "architecture defined, no weights" claim is TRUE for this tree.
• archive/v1/README.md calls the tree "the legacy Python implementation" in a single closing note but does not loudly warn users off it, and there is no archive/v1/DEPRECATED.md. This is the dead-but-present code that shows up in greps and search and reads as if it were the live implementation.
• Per ADR-117, this exact tree is the source of the tombstoned pip package wifi-densepose 1.x (1.99.0 raises an ImportError telling users to migrate). The code is already tombstoned on PyPI but not in the repo.

2. v2 (the current, maintained system) — real weights DO exist; the "no weights anywhere" reading is FALSE at the project level. Git-tracked, committed checkpoints:

• v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors (507 KB) + pose_v1.onnx (12 KB) + train_results.json — a real committed 17-keypoint model, trained with Candle on an RTX 5080.
• v2/crates/cog-person-count/cog/artifacts/count_v1.{safetensors,onnx} — a committed person-count model.
• Externally published on Hugging Face (not committed, but real and released): ruvnet/wifi-densepose-pretrained (CSI encoder + presence head, honestly re-labeled at 82.3% held-out temporal-triplet accuracy — the older "100% presence" figure was already retracted, an existing honest-labeling win) and ruvnet/wifi-densepose-mmfi-pose (a pose model reporting 82.69% torso-PCK@20 on the MM-Fi random_split protocol).
• ADR-152 measurement (a): the external WiFlow-STD (DY2434) model was reproduced at 96.09% PCK@20 on an RTX 5080 (graded MEASURED-EQUIVALENT). That is an external baseline, not RuView's own weights.

3. The honest gap is narrow and specific — the live, on-device ESP32 17-keypoint pose path. Per v2/crates/cog-pose-estimation/cog/README.md (already an exemplary "Honest reading" section):

• The committed pose_v1 scores PCK@20 = 3.0% / PCK@50 = 18.5% on a 217-sample holdout — below the ADR-079 target of PCK@20 ≥ 35%. It learns coarse structure (r_hip 77% PCK@50) but distal/face joints are near-random. encoder_init was random; it was trained on a single 30-min seated-at-desk recording (1,077 samples, avg confidence 0.44).
• The cog's runtime inference path is still a centred-skeleton stub returning confidence=0 — the pose_v1.safetensors weights are not yet wired into src/inference.rs.
• ADR-079 records the proxy-supervised baseline at PCK@20 = 2.5%, and ADR-152 retracted the internal camera-supervised 92.9% PCK@20 figure (it was a constant-output model scored under an absolute threshold on near-static frames; a mean predictor scores 100% under the same broken protocol).

The real problem to fix

Not "the project has no weights" (false) and not "there is a validated pretrained DensePoseHead" (false for the live ESP32 path). The real problem is a labeling and navigation gap:

1. archive/v1's random-init DensePoseHead is indistinguishable, to a grepping reader, from the live implementation, and carries no deprecation notice.
2. Nowhere is the split stated plainly: which checkpoints are real and validated (presence 82.3%, MM-Fi pose 82.69% torso-PCK@20), which are real-but-weak and honestly labeled (pose_v1 3% PCK@20, runtime stubbed), and which are architecture-only with no weights at all (archive/v1 DensePoseHead).

Decision

Two coordinated honest-labeling actions. Neither invents a capability; both make the public surface match what the code and checkpoints actually deliver.

(a) Formally deprecate archive/v1 in the repo — MEASURED gap, proposed fix

• Add archive/v1/DEPRECATED.md — a loud tombstone stating that archive/v1 is the original pure-Python implementation, is unmaintained and superseded, that its DensePoseHead is architecture-only with random-initialized weights and ships no trained checkpoint, and that the maintained path is the v2/ Rust workspace + the wifi-densepose 2.x / ruview pip wheel (ADR-117). Mirror the disclaimer tone of ADR-160's //! headers and the pip 1.99.0 tombstone text.
• Prepend a loud notice to archive/v1/README.md (the file exists) — a > ⚠️ DEPRECATED block at the very top pointing to DEPRECATED.md, v2/, and the pip wheel, before any of the existing "how to install v1" content.
• Rule: no doc outside archive/v1/ may reference archive/v1 code (other than the ADR-028 deterministic proof at archive/v1/data/proof/verify.py, which is a legitimately live signal-pipeline witness and stays) as if it were current. The two README references verified (README.md lines 139/198/204; docs/user-guide.md proof/swift-compile lines) are all proof/utility invocations, not implementation claims — they are acceptable and out of scope.

(b) Model-weights honest labeling — state the three tiers explicitly

Add a "Model weights: what's real, what's not" subsection to README.md and docs/user-guide.md that names the three tiers verified above, so no reader can infer "a pretrained 17-keypoint DensePoseHead produces real pose accuracy on my ESP32":

Tier	Checkpoint(s)	Honest status
Real & validated	ruvnet/wifi-densepose-pretrained (encoder + presence, 82.3% held-out temporal-triplet); ruvnet/wifi-densepose-mmfi-pose (82.69% torso-PCK@20, MM-Fi random_split); count_v1	MEASURED / published; keep current honest labels
Real but weak (honestly labeled)	committed pose_v1.safetensors in cog-pose-estimation	PCK@20 = 3.0%, below the ADR-079 ≥35% target; runtime path is a confidence=0 stub until weights are wired into src/inference.rs. Already disclosed in the cog README; surface the same caveat wherever the live ESP32 pose feature is advertised
Architecture only, no weights	archive/v1 DensePoseHead	random-init, no checkpoint; deprecated per (a)

• The existing MM-Fi/presence honest labels (retraction of "100% presence", the cog "Honest reading") are NO-ACTION positives — cite them, do not weaken them.
• The live ESP32 17-keypoint claim stays DATA-GATED: the path to a first reproducible on-device baseline is ADR-079 (multi-session, full-body-framed, camera-supervised, ≥30K paired samples at conf ≥0.7, target PCK@20 ≥35%), tracked in [#645]. Do not advertise the live ESP32 pose feature without the "first-cut / below target / runtime stub" caveat until that baseline is MEASURED.
• Directly answer #509's ESP32-SISO question in the docs, honestly: single-antenna 56- subcarrier CSI at a 20-frame window does not carry the fine-grained spatial information the multi-antenna NIC research relies on (the cog README already shows distal/face joints near-random) — the shippable pose accuracy the project can stand behind today is the MM-Fi benchmark number, not a live single-ESP32 number.

Phase ledger

Phase	Action	State
P0	This ADR (investigation + decision)	DONE (this file)
P1	Add archive/v1/DEPRECATED.md + loud notice atop archive/v1/README.md	DONE (1fb5397dd)
P2	Add "Model weights: what's real, what's not" tier table to README.md + docs/user-guide.md; add the caveat wherever the live ESP32 17-keypoint feature is advertised	DONE (1fb5397dd; follow-up caveated the hardware table, hero caption, and live-pipeline note)
P3	Answer #509's SISO/no-weights question and #1125's "does it run" in docs/user-guide.md (point to the reproducible proofs: MM-Fi arena, archive/v1/data/proof/verify.py, cog train_results.json)	DONE (1fb5397dd)
P4	Close the DATA-GATED live-pose gap via ADR-079 first reproducible on-device baseline (PCK@20 ≥35%) + wire pose_v1.safetensors into cog-pose-estimation/src/inference.rs	ACCEPTED-FUTURE ([#645])

Acceptance criteria

• archive/v1/DEPRECATED.md exists and names v2/ + the pip wheel as the maintained path.
• archive/v1/README.md opens with a > ⚠️ DEPRECATED block before any install instructions.
• README.md and docs/user-guide.md no longer let a reader infer that archive/v1 or an untrained/random-init DensePoseHead produces real pose accuracy without the caveats added here.
• The live ESP32 17-keypoint pose feature is nowhere advertised without its "first-cut, PCK@20 = 3.0%, below ADR-079 target, runtime stub" caveat.
• The three real/published checkpoints (presence 82.3%, MM-Fi pose 82.69% torso-PCK@20, count_v1) keep their existing honest labels — nothing is weakened or overclaimed.
• No claim is added that is not MEASURED or explicitly DATA-GATED.

Consequences

Positive

• A grepping reader can no longer mistake archive/v1's random-init DensePoseHead for the live system; the dead code is loudly tombstoned in the repo, matching its PyPI 1.99.0 tombstone.
• #509 and #1125 get an honest, verifiable answer: real trained weights do exist (presence + MM-Fi pose are published and benchmarked), the specific file the reporter found is architecture-only, and the live ESP32 pose path is honestly weak-and-in-progress.
• Reinforces the ADR-160 honest-labeling discipline: the project's credibility comes from precise labels, not from a suppressed or inflated narrative.

Negative

• The docs must openly state that the live single-ESP32 17-keypoint pose is not yet at a citable accuracy — a short-term "looks less finished" cost, paid for by not overclaiming.
• Two more files to keep in sync (DEPRECATED.md, the tier table) as the checkpoints evolve.

Neutral

• No code or model behavior changes; archive/v1 stays in the tree as a research archive (ADR-117 §1.3) and its ADR-028 proof witness is untouched.
• Purely documentation/labeling; no crate, wheel, or firmware rebuild required.

References

• archive/v1/src/models/densepose_head.py — DensePoseHead, random _initialize_weights(), no checkpoint load.
• archive/v1/README.md — legacy note; no loud deprecation (target of P1).
• v2/crates/cog-pose-estimation/cog/README.md — the "Honest reading" precedent (PCK@20 = 3.0%, runtime stub).
• v2/crates/cog-pose-estimation/cog/artifacts/{pose_v1.safetensors,pose_v1.onnx,train_results.json} — committed first-cut pose model.
• v2/crates/cog-person-count/cog/artifacts/count_v1.{safetensors,onnx} — committed count model.
• ruvnet/wifi-densepose-pretrained, ruvnet/wifi-densepose-mmfi-pose — published, benchmarked checkpoints.
• ADR-079 §Target (PCK@20 ≥35%), ADR-152 measurement (a) (96.09% PCK@20 external; internal 92.9% retracted), ADR-160 (honest-labeling method), ADR-117 (pip 1.99.0 tombstone).