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feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2)
Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization); 6 reproducibility defects documented (broken imports, corrupted dataset tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable test phase). After repairs, retraining with upstream defaults reproduces 96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs verified. Third-party code/weights/data stay out of tree (gitignored). Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
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# Upstream clone (WiFlow-STD, DY2434) -- never commit third-party code/weights
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upstream/
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# Local python env
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.venv/
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# Downloaded data / artifacts
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data/
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downloads/
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*.pth
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*.pt
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*.npy
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*.npz
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*.zip
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*.mat
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__pycache__/
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@@ -0,0 +1,110 @@
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# WiFlow-STD (DY2434) Benchmark Results — ADR-152 §2.2
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Upstream: <https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling>
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pinned at `06899d29` (2026-04-05), Apache-2.0. Dataset: Kaggle `kaka2434/wiflow-dataset`
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(12.8 GB archive → 15.5 GB extracted; 360,000 windows of 540×20 CSI + 15-keypoint 2D labels).
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Published claims (README "Setting 1"): PCK@20 97.25%, PCK@30 98.63%, PCK@40 99.16%,
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PCK@50 99.48%, MPJPE 0.007 m, 2.23M params, 0.07 GFLOPs.
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## Measurement (a): their model on their data
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### Artifact verification (MEASURED, 2026-06-10, this repo `eval_repro.py`)
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| Check | Result |
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|---|---|
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| Parameter count | **2,225,042 (2.23M) — matches claim** |
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| FLOPs (torch profiler, batch 1) | ~0.055 GFLOPs — consistent with 0.07B claim |
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| CPU latency (Windows box, torch 2.12 CPU) | 13.2 ms/window @ batch 1 (76/s); 2.48 ms/sample @ batch 64 (403/s) |
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| Checkpoint load | `weights_only=True` (no pickle code execution) |
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### Released checkpoint does NOT reproduce the claims — REFUTED as shipped
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Running the released `best_pose_model.pth` through the released code on the released
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dataset with the released split procedure (seed-42 file-level 70/15/15; 54,000 test
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samples) yields:
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| Metric | Published | Measured (shipped checkpoint) |
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|---|---|---|
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| PCK@20 | 97.25% | **0.08%** |
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| PCK@30 | 98.63% | 0.78% |
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| PCK@40 | 99.16% | 5.53% |
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| PCK@50 | 99.48% | 15.42% |
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| MPJPE | 0.007 | **NaN** (dataset contains NaN CSI windows) |
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Raw output: `results/repro_a.json`.
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Diagnostics (on 2,000 NaN-free windows from the first files of the dataset, i.e.
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mostly would-be *training* data — so this is not a split mismatch):
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- Predictions correlate with targets (Pearson r ≈ 0.76) — the checkpoint is a trained
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model, but in a **different keypoint normalization/order** than the released data.
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- Best-case post-hoc global per-axis affine correction: PCK@20 ≈ 20%.
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- Best-case per-keypoint affine correction (15×2 fitted transforms — generous
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cheating): PCK@20 ≈ 72%, still far below 97.25%.
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- Pred↔target keypoint correspondence matrix is degenerate (multiple predicted
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keypoints best-match the same target joint) — keypoint convention mismatch.
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### Reproducibility defects in the released artifacts
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1. `models/__init__.py` imports `TemporalConvNet`, which `models/tcn.py` does not
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define — **the published code does not import/run as-is**.
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2. The released root checkpoint uses pre-rename module names (`att.*`, `final_conv.*`)
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vs the published code (`attention.*`, `decoder.*`) — same shapes/param count, but
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confirms the checkpoint predates the published code.
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3. The second shipped checkpoint (`cross_dataset_test/WiFlow/best_pose_model.pth`) is
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a **different architecture** (342-channel input = MM-Fi layout, 3 TCN layers,
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3-channel/3D decoder) — not usable on their own dataset.
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4. `run.py` ignores `--data_dir` and hardcodes `../preprocessed_csi_data`.
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5. The released dataset's final 13 files (indices 487–499; 9,072 windows, 2.52%)
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are corrupted: NaN values plus garbage amplitudes up to 3.4e38 (float32 max) in
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data that is otherwise [0,1]-normalized. Upstream code has no NaN/inf handling;
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training as published on this download diverges — the first corrupted batch
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overflows fp16 autocast and permanently poisons BatchNorm running statistics
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(GradScaler step-skipping does not protect BN). The authors' training curves
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show normal convergence, so their local data evidently differed from the
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Kaggle upload. Window masks: `results/nan_windows_mask.npy`,
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`results/big_windows_mask.npy`.
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### Retraining result (MEASURED, 2026-06-10): claims APPROXIMATELY REPRODUCED
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Since the shipped checkpoint is unusable, measurement (a) fell back to retraining
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with upstream code + defaults (seed 42, batch 64, early-stopped at epoch 41 of 50,
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best epoch 36, ~75 s/epoch) on ruvultra (RTX 5080). Deviations, all forced and
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documented: one-line fix for defect (1); torch 2.x+cu128 instead of pinned 2.3.1
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(Blackwell sm_120 unsupported); the 9,072 corrupted windows (defect 5) zeroed
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entirely — without this the published pipeline produces NaN from epoch 1 (observed).
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Scripts mirrored in `remote/`; raw metrics in `results/eval_retrained.json`.
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| Metric | Published | Retrained (full test, 54,000) | Retrained (corruption-free, 52,560) |
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|---|---|---|---|
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| PCK@20 | 97.25% | **96.09%** | **96.61%** |
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| PCK@30 | 98.63% | 97.89% | 98.23% |
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| PCK@40 | 99.16% | 98.58% | 98.79% |
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| PCK@50 | 99.48% | 98.99% | 99.11% |
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| MPJPE | 0.007 | 0.0098 | 0.0094 |
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Within ~0.6–1.2 PCK points of every published figure (single run, corrupted train
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windows zeroed, different torch/GPU). **Verdict: the accuracy claims are credible
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and approximately reproducible — but only after repairing the released dataset and
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code.** Val best: PCK@20 96.99%, MPJPE 0.0086 (epoch 36).
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One more defect found during the run:
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6. `train.py` calls `plot_training_history`, which is not defined anywhere — the
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built-in post-training test evaluation is unreachable as published (crashes
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with NameError after training completes).
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## ADR-152 §2.2 citation rule
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Evidence grade for the WiFlow-STD accuracy claims after measurement (a):
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**MEASURED-EQUIVALENT (96.1–96.6% PCK@20 reproduced by retraining; shipped
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checkpoint REFUTED; dataset/code require repairs)**. RuView docs may cite
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"~96% PCK@20 (our reproduction)" — still **not comparable** to our 17-keypoint
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ESP32 numbers (different hardware, 5 subjects, in-domain random split,
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15 keypoints).
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## Pending
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- (b) fine-tune on our ESP32 17-keypoint eval set.
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- (c) our internal WiFlow on their dataset (15-keypoint subset mapping).
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@@ -0,0 +1,173 @@
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"""ADR-152 §2.2 measurement (a): reproduce WiFlow-STD (DY2434) published test metrics.
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Runs the released pretrained checkpoint (upstream/best_pose_model.pth) against the
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released Kaggle dataset (kaka2434/wiflow-dataset) using the upstream code path:
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identical dataset class, identical file-level 70/15/15 split at seed 42, identical
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PCK/MPJPE implementations (utils/metrics.py).
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Published claims (README, "Setting 1 random split"):
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PCK@20 97.25% | PCK@30 98.63% | PCK@40 99.16% | PCK@50 99.48% | MPJPE 0.007 m
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Usage:
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.venv/Scripts/python.exe eval_repro.py --data-dir <dir containing csi_windows.npy>
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"""
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import argparse
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import json
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import os
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import random
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import sys
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import time
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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UPSTREAM = os.path.join(os.path.dirname(os.path.abspath(__file__)), "upstream")
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sys.path.insert(0, UPSTREAM)
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# Upstream bug: models/__init__.py imports TemporalConvNet, which models/tcn.py
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# does not define (it defines TemporalBlock) — the package fails to import as
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# published. Register a stub package so the broken __init__ never executes;
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# submodules (models.pose_model etc.) still resolve via __path__.
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import types # noqa: E402
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_models_pkg = types.ModuleType("models")
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_models_pkg.__path__ = [os.path.join(UPSTREAM, "models")]
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sys.modules["models"] = _models_pkg
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import dataset as upstream_dataset # noqa: E402
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from dataset import PreprocessedCSIKeypointsDataset, create_preprocessed_train_val_test_loaders # noqa: E402
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from models.pose_model import WiFlowPoseModel # noqa: E402
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from utils.metrics import calculate_pck, calculate_mpjpe # noqa: E402
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# csi_windows.npy is ~13 GB; mmap large arrays instead of loading into RAM.
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_np_load = np.load
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def _np_load_mmap(path, *a, **kw):
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if (isinstance(path, str) and path.endswith(".npy")
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and os.path.getsize(path) > 1 << 30 and "mmap_mode" not in kw):
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kw["mmap_mode"] = "r"
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return _np_load(path, *a, **kw)
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upstream_dataset.np.load = _np_load_mmap
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def set_seed(seed=42):
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# mirror upstream run.py exactly
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def find_data_dir(root):
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for dirpath, _dirnames, filenames in os.walk(root):
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if "csi_windows.npy" in filenames:
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return dirpath
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return None
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def evaluate(model, loader, device):
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model.eval()
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totals = {t: 0.0 for t in (0.1, 0.2, 0.3, 0.4, 0.5)}
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total_mpe = 0.0
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n = 0
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t0 = time.time()
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with torch.no_grad():
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for batch_idx, (batch_x, batch_y) in enumerate(loader):
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batch_x = batch_x.to(device)
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batch_y = batch_y.to(device)
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outputs = model(batch_x)
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mpe = calculate_mpjpe(outputs, batch_y)
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pck = calculate_pck(outputs, batch_y, thresholds=[0.1, 0.2, 0.3, 0.4, 0.5])
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bs = batch_y.size(0)
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total_mpe += mpe * bs
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for t in totals:
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totals[t] += pck[t] * bs
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n += bs
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if batch_idx % 50 == 0:
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print(f" batch {batch_idx}: n={n} pck20={totals[0.2]/n:.4f} "
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f"mpjpe={total_mpe/n:.4f} ({time.time()-t0:.0f}s)", flush=True)
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return {
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"samples": n,
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"mpjpe": total_mpe / n,
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**{f"pck@{int(t*100)}": totals[t] / n for t in totals},
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"wall_seconds": time.time() - t0,
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}
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--data-dir", required=True,
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help="Directory containing csi_windows.npy (searched recursively)")
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parser.add_argument("--checkpoint", default=os.path.join(UPSTREAM, "best_pose_model.pth"))
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parser.add_argument("--batch-size", type=int, default=64)
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parser.add_argument("--out", default=os.path.join(os.path.dirname(os.path.abspath(__file__)),
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"results", "repro_a.json"))
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args = parser.parse_args()
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data_dir = args.data_dir
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if not os.path.exists(os.path.join(data_dir, "csi_windows.npy")):
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located = find_data_dir(data_dir)
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if located is None:
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sys.exit(f"csi_windows.npy not found under {data_dir}")
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data_dir = located
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print(f"data dir: {data_dir}")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"device: {device}, torch {torch.__version__}")
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set_seed(42)
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dataset = PreprocessedCSIKeypointsDataset(
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data_dir=data_dir, keypoint_scale=1000.0, enable_temporal_clean=True)
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# split must match upstream: file-level shuffle at random_seed=42, 70/15/15
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_train_loader, _val_loader, test_loader = create_preprocessed_train_val_test_loaders(
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dataset=dataset, batch_size=args.batch_size, num_workers=0, random_seed=42)
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model = WiFlowPoseModel(dropout=0.5).to(device)
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state = torch.load(args.checkpoint, map_location=device, weights_only=True)
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# released checkpoint predates the published code: modules were renamed
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# att -> attention, final_conv -> decoder (param count identical, 2.23M)
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renames = {"att.": "attention.", "final_conv.": "decoder."}
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state = {next((new + k[len(old):] for old, new in renames.items()
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if k.startswith(old)), k): v
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for k, v in state.items()}
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model.load_state_dict(state, strict=True)
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n_params = sum(p.numel() for p in model.parameters())
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print(f"checkpoint: {args.checkpoint} ({n_params/1e6:.2f}M params)")
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# upstream also evaluates with drop_last=True; we report the full test set
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# (drop_last=False) and the drop_last variant for exact comparability
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results = {"published": {"pck@20": 0.9725, "pck@30": 0.9863, "pck@40": 0.9916,
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"pck@50": 0.9948, "mpjpe": 0.007},
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"params_millions": n_params / 1e6,
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"data_dir": data_dir,
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"device": str(device)}
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print("=== test set (full, drop_last=False) ===")
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results["test_full"] = evaluate(model, test_loader, device)
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print(json.dumps(results["test_full"], indent=2))
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test_loader_dl = DataLoader(test_loader.dataset, batch_size=args.batch_size,
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shuffle=False, drop_last=True)
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print("=== test set (drop_last=True, as upstream train.py) ===")
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results["test_drop_last"] = evaluate(model, test_loader_dl, device)
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print(json.dumps(results["test_drop_last"], indent=2))
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os.makedirs(os.path.dirname(args.out), exist_ok=True)
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with open(args.out, "w") as f:
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json.dump(results, f, indent=2)
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print(f"wrote {args.out}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,14 @@
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import numpy as np, os
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d = os.path.expanduser('~/wiflow-std-bench/preprocessed_csi_data')
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csi = np.load(os.path.join(d, 'csi_windows.npy'), mmap_mode='r+')
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zeroed = 0
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chunk = 4000
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for i in range(0, len(csi), chunk):
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block = csi[i:i+chunk]
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finite = np.isfinite(block)
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bad = (~finite).any(axis=(1, 2)) | (np.abs(np.where(finite, block, 0)).max(axis=(1, 2)) > 1.5)
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if bad.any():
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block[bad] = 0.0
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zeroed += int(bad.sum())
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csi.flush()
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print(f'zeroed {zeroed} corrupted windows entirely')
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@@ -0,0 +1,93 @@
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"""Evaluate the retrained WiFlow-STD checkpoint (ADR-152 §2.2a fallback).
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||||
Scores the model produced by run.py (train_output/best_pose_model.pth or similar)
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on the seed-42 test split: full test set AND NaN-free subset (excluding windows
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||||
that were zero-filled by clean_nan.py — file indices 487-499).
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||||
"""
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import json, os, random, sys
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import numpy as np
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import torch
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from torch.utils.data import DataLoader, Subset
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|
||||
sys.path.insert(0, os.path.expanduser('~/wiflow-std-bench/upstream'))
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from dataset import PreprocessedCSIKeypointsDataset, create_preprocessed_train_val_test_loaders
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from models.pose_model import WiFlowPoseModel
|
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from utils.metrics import calculate_pck, calculate_mpjpe
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||||
def find_checkpoint():
|
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cands = []
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for root, _, files in os.walk(os.path.expanduser('~/wiflow-std-bench/train_output')):
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for f in files:
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if f.endswith('.pth'):
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cands.append(os.path.join(root, f))
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# also upstream/test default output dir
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for root, _, files in os.walk(os.path.expanduser('~/wiflow-std-bench/upstream')):
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for f in files:
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if f.endswith('.pth') and 'best' in f and 'cross_dataset' not in root:
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||||
p = os.path.join(root, f)
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if os.path.getmtime(p) > os.path.getmtime(os.path.expanduser('~/wiflow-std-bench/train.log')) - 86400 * 2:
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cands.append(p)
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||||
cands = [c for c in cands if not c.endswith('upstream/best_pose_model.pth')]
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||||
if not cands:
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||||
sys.exit('no retrained checkpoint found')
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return max(cands, key=os.path.getmtime)
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def evaluate(model, loader, device):
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model.eval()
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totals = {t: 0.0 for t in (0.1, 0.2, 0.3, 0.4, 0.5)}
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||||
total_mpe, n = 0.0, 0
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||||
with torch.no_grad():
|
||||
for bx, by in loader:
|
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bx, by = bx.to(device), by.to(device)
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out = model(bx)
|
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bs = by.size(0)
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total_mpe += calculate_mpjpe(out, by) * bs
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||||
pck = calculate_pck(out, by, thresholds=list(totals))
|
||||
for t in totals:
|
||||
totals[t] += pck[t] * bs
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||||
n += bs
|
||||
return {'samples': n, 'mpjpe': total_mpe / n,
|
||||
**{f'pck@{int(t*100)}': totals[t] / n for t in totals}}
|
||||
|
||||
|
||||
random.seed(42); np.random.seed(42); torch.manual_seed(42)
|
||||
torch.cuda.manual_seed_all(42)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
d = os.path.expanduser('~/wiflow-std-bench/preprocessed_csi_data')
|
||||
dataset = PreprocessedCSIKeypointsDataset(data_dir=d, keypoint_scale=1000.0,
|
||||
enable_temporal_clean=True)
|
||||
_, _, test_loader = create_preprocessed_train_val_test_loaders(
|
||||
dataset=dataset, batch_size=256, num_workers=2, random_seed=42)
|
||||
|
||||
device = torch.device('cuda')
|
||||
ckpt = find_checkpoint()
|
||||
print('checkpoint:', ckpt)
|
||||
model = WiFlowPoseModel(dropout=0.5).to(device)
|
||||
state = torch.load(ckpt, map_location=device, weights_only=True)
|
||||
renames = {'att.': 'attention.', 'final_conv.': 'decoder.'}
|
||||
state = {next((new + k[len(old):] for old, new in renames.items()
|
||||
if k.startswith(old)), k): v for k, v in state.items()}
|
||||
model.load_state_dict(state, strict=True)
|
||||
|
||||
results = {'checkpoint': ckpt}
|
||||
print('=== full test set ===')
|
||||
results['test_full'] = evaluate(model, test_loader, device)
|
||||
print(json.dumps(results['test_full'], indent=2))
|
||||
|
||||
# NaN-free subset: exclude windows from corrupted files 487-499
|
||||
test_subset = test_loader.dataset # Subset(dataset, test_indices)
|
||||
w2f = dataset.window_to_file
|
||||
clean_idx = [i for i in test_subset.indices if w2f[i] < 487]
|
||||
print(f'=== NaN-free test subset ({len(clean_idx)} of {len(test_subset.indices)}) ===')
|
||||
clean_loader = DataLoader(Subset(dataset, clean_idx), batch_size=256, shuffle=False)
|
||||
results['test_clean'] = evaluate(model, clean_loader, device)
|
||||
print(json.dumps(results['test_clean'], indent=2))
|
||||
|
||||
out = os.path.expanduser('~/wiflow-std-bench/eval_retrained.json')
|
||||
with open(out, 'w') as f:
|
||||
json.dump(results, f, indent=2)
|
||||
print('wrote', out)
|
||||
@@ -0,0 +1,33 @@
|
||||
#!/bin/bash
|
||||
set -ex
|
||||
cd ~/wiflow-std-bench
|
||||
|
||||
# 1. clone upstream at the pinned commit
|
||||
if [ ! -d upstream ]; then
|
||||
git clone https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling upstream
|
||||
fi
|
||||
cd upstream && git checkout 06899d294a0f44709d601a53e91dbf24759daefb && cd ..
|
||||
|
||||
# 2. documented deviation: fix upstream import bug (TemporalConvNet does not exist)
|
||||
sed -i 's/from .tcn import TemporalConvNet/from .tcn import TemporalBlock/; s/'"'"'TemporalConvNet'"'"'/'"'"'TemporalBlock'"'"'/' upstream/models/__init__.py
|
||||
|
||||
# 3. venv: torch cu128 (RTX 5080 = sm_120 needs >=2.7; their pin 2.3.1 predates Blackwell)
|
||||
if [ ! -d venv ]; then
|
||||
python3 -m venv venv
|
||||
./venv/bin/pip install -q --upgrade pip
|
||||
./venv/bin/pip install -q torch --index-url https://download.pytorch.org/whl/cu128
|
||||
./venv/bin/pip install -q numpy pandas matplotlib seaborn scikit-learn opencv-python-headless scipy tqdm psutil kagglehub
|
||||
fi
|
||||
./venv/bin/python -c "import torch; print(torch.__version__, torch.cuda.is_available(), torch.cuda.get_device_name(0))"
|
||||
|
||||
# 4. dataset via kagglehub (anonymous, public dataset)
|
||||
DS=$(./venv/bin/python -c "import kagglehub; print(kagglehub.dataset_download('kaka2434/wiflow-dataset'))")
|
||||
echo "dataset at: $DS"
|
||||
|
||||
# 5. run.py hardcodes ../preprocessed_csi_data relative to upstream/
|
||||
ln -sfn "$DS/preprocessed_csi_data" ~/wiflow-std-bench/preprocessed_csi_data
|
||||
|
||||
# 6. train with upstream defaults (seed 42 set inside run.py)
|
||||
../venv/bin/python ../clean_nan.py 2>/dev/null || venv/bin/python clean_nan.py
|
||||
cd upstream
|
||||
../venv/bin/python run.py --gpu 0 --batch_size 64 --epochs 50 --output_dir ../train_output
|
||||
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"checkpoint": "/home/ruvultra/wiflow-std-bench/upstream/test/best_pose_model.pth",
|
||||
"test_full": {
|
||||
"samples": 54000,
|
||||
"mpjpe": 0.009834060806367133,
|
||||
"pck@10": 0.8686346120127925,
|
||||
"pck@20": 0.9608815324571398,
|
||||
"pck@30": 0.9789111610695168,
|
||||
"pck@40": 0.9857975759682832,
|
||||
"pck@50": 0.9898827553325229
|
||||
},
|
||||
"test_clean": {
|
||||
"samples": 52560,
|
||||
"mpjpe": 0.009432755044379373,
|
||||
"pck@10": 0.876996495807189,
|
||||
"pck@20": 0.9661454100405608,
|
||||
"pck@30": 0.9823453060205306,
|
||||
"pck@40": 0.987909734176537,
|
||||
"pck@50": 0.9911238361167036
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"published": {
|
||||
"pck@20": 0.9725,
|
||||
"pck@30": 0.9863,
|
||||
"pck@40": 0.9916,
|
||||
"pck@50": 0.9948,
|
||||
"mpjpe": 0.007
|
||||
},
|
||||
"params_millions": 2.225042,
|
||||
"data_dir": "C:\\Users\\ruv\\.cache\\kagglehub\\datasets\\kaka2434\\wiflow-dataset\\versions\\1\\preprocessed_csi_data",
|
||||
"device": "cpu",
|
||||
"test_full": {
|
||||
"samples": 54000,
|
||||
"mpjpe": NaN,
|
||||
"pck@10": 5.6790124349020145e-05,
|
||||
"pck@20": 0.0007876543271596785,
|
||||
"pck@30": 0.007780246982971827,
|
||||
"pck@40": 0.05529259262923841,
|
||||
"pck@50": 0.1542370371548114,
|
||||
"wall_seconds": 118.03756999969482
|
||||
},
|
||||
"test_drop_last": {
|
||||
"samples": 53952,
|
||||
"mpjpe": NaN,
|
||||
"pck@10": 5.6840649370682976e-05,
|
||||
"pck@20": 0.0007883550872372227,
|
||||
"pck@30": 0.007787168910892621,
|
||||
"pck@40": 0.055318307667895535,
|
||||
"pck@50": 0.15425316342412276,
|
||||
"wall_seconds": 120.87458372116089
|
||||
}
|
||||
}
|
||||
@@ -54,6 +54,8 @@ Adopt four changes, ordered by effort-vs-gain:
|
||||
|
||||
Pull the Apache-2.0 weights + 360k-sample dataset; run three measurements: (a) their model on their data (reproduce 97.25% claim), (b) their model fine-tuned on our ESP32 17-keypoint eval set, (c) our internal WiFlow on their dataset (15-keypoint subset mapping). Until (a)–(c) are measured, **no RuView doc may cite 97.25% as a comparable number** — different dataset, subjects, keypoints.
|
||||
|
||||
> **Status (2026-06-10, measurement (a) complete — `benchmarks/wiflow-std/RESULTS.md`):** shipped checkpoint REFUTED (0.08% PCK@20 — wrong keypoint normalization, predates published code); released code does not run as published (6 defects, incl. broken package import and an unreachable test phase); released dataset's last 13 files are corrupted (9,072 windows: NaN + float32-max garbage, diverges fp16 training via BatchNorm poisoning). After repairing both, retraining with upstream defaults reproduced **96.09% PCK@20 full-test / 96.61% corruption-free / MPJPE 0.0094–0.0098** (published: 97.25% / 0.007) on an RTX 5080. Accuracy claims graded MEASURED-EQUIVALENT; params (2.23M) and FLOPs (~0.055G) verified. (b)/(c) remain open.
|
||||
|
||||
### 2.3 Apply the UNSW recipe to the ADR-150 encoder — ACCEPTED (amends ADR-150 §2.3)
|
||||
|
||||
- Pretraining corpus: start from the same 14 public datasets (1.3M samples) + our home/MM-Fi frames; data aggregation takes priority over architecture work.
|
||||
|
||||
Reference in New Issue
Block a user