metadata
license: apache-2.0
language: en
tags:
- industrial-inspection
- metrology
- crack-detection
- segmentation
- measurement
pipeline_tag: image-segmentation
GaugeAnything — task heads for promptable quantitative inspection
Masks in, millimeters out. These are the trained task heads of GaugeAnything — a promptable quantitative inspection pipeline for industrial micro-vision (SAM 3 backbone + metrology core).
🌐 Project page: https://falcons-eyes.github.io/GaugeAnything/ · 📊 All numbers below are audited (held-out splits, multi-seed where applicable, protocols in the repo).
Checkpoints
| File | Task | Audited result | Training data | Use |
|---|---|---|---|---|
profile_width_cnn.pt |
1-D crack-width regression from a 501-px brightness profile (the "signal for HOW WIDE" head) | table test MAE ≈18.6 μm (~1 μm GPU run variance); end-to-end promptable 39.9 μm MAE / 23.2 μm median (localization-gated) | krkCMd, 14,424 profiles (CC BY 4.0 — license-clean) | ✅ commercial OK |
gaugehead_tiny_width.pkl |
Tiny owned crack-width specialist over SAM-mask/image statistics | held-out source rel.err 0.472 vs 5-bin quantile 0.480 and old neural M2 0.564; worst source still 0.720 | CrackSeg9k M2 cache | ⚠️ research (subset licenses vary) |
gaugehead_tiny_width_conformal.pkl |
GaugeHead-Tiny + 90% conformal interval (log cross-conformal; μ + σ-diagnostic + q) | keeps rel.err 0.4724 with per-source coverage 0.91/1.00/0.95 @90%; adaptive variants collapse on the worst source (0.21/0.11) — see repo experiments/results/m2_uncertainty_conformal.json |
CrackSeg9k M2 cache | ⚠️ research (subset licenses vary) |
m2_refiner.pt |
Measurement-aware crack mask refiner (UNet, 1.9M) | superseded baseline: a logit-threshold + quantile calibration beats it (0.437 vs 0.564 rel. err) — kept for reproducibility | CrackSeg9k train sources | ⚠️ research (subset licenses vary) |
matte_fray_directional.pt |
Alpha matting head for fuzzy-boundary (fray) defects, directional synthesis v2 | real MT-fray preservation IoU 0.949 vs classical guided filter 0.860 | synthetic compositing over Magnetic-Tile free images | ⚠️ research (MT license unstated) |
matte_fray.pt |
v1 (blob synthesis) — kept as the honest negative: real-transfer failure 0.483 | see repo progress logs | same | ⚠️ research |
draem_uneven.pt |
DRAEM-lite reconstruction head for boundaryless (uneven/mura) defects | test AUC 0.636 (classical illumination-residual baseline: 0.669) | synthetic mura over Magnetic-Tile free images | ⚠️ research |
The SAM 3 backbone is not redistributed here — get it at facebook/sam3 (separate license, gated).
Usage (profile width head)
import torch, numpy as np
ckpt = torch.load("profile_width_cnn.pt", map_location="cpu")
# architecture: see experiments/krkcmd_signal_width.py::build_1d_net in the GitHub repo
from gaugeanything_repo.experiments.krkcmd_signal_width import build_1d_net, norm_profile
net = build_1d_net(); net.load_state_dict(ckpt["model"]); net.eval()
profile = np.asarray(...) # 501 samples of image brightness across the crack
x = torch.from_numpy(norm_profile(profile)).view(1, 1, -1)
width_um = float(net(x)) # crack width in micrometers
Full pipeline (prompt → SAM 3 localization → perpendicular profile → width) lives in the
GitHub repo — see
experiments/krkcmd_signal_width.py and docs/WIDTH_BOTTLENECK_ANALYSIS.md for why
width is read from the signal, not from mask geometry.
Honest limitations
profile_width_cnnis trained on 6400-dpi scanner profiles of concrete (krkCMd); transfer to other resolutions/materials is not yet validated — scale-normalize inputs.- End-to-end accuracy is localization-gated: coverage 46–66% on the scanner domain; points failing the gate are reported as "not measurable", not guessed.
- Heads marked research await upstream dataset license clarification before commercial use.
Citation
@misc{gaugeanything2026,
title = {GaugeAnything: Promptable Quantitative Inspection for Industrial Micro-Vision},
author = {Joo, Hyunwoo},
year = {2026},
url = {https://github.com/falcons-eyes/GaugeAnything}
}