Hidden
Machine Vision / AOI — Hidden Tier
(3 scenes)Fully blind server-side evaluation — no data download.
What you get
No data downloadable. Algorithm runs server-side on hidden measurements.
How to use
Package algorithm as Docker container / Python script. Submit via link.
What to submit
Containerized algorithm accepting y + H, outputting x_hat + corrected spec.
Parameter Specifications
🔒
True spec hidden — blind evaluation, only ranges available.
| Parameter | Spec Range | Unit |
|---|---|---|
| focus_distance_error | -0.7 – 2.3 | mm |
| lens_distortion_k1 | -0.014 – 0.046 | - |
| exposure_time_drift | 9.72 – 10.92 | ms |
| white_balance_gain | 0.986 – 1.046 | - |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | UniAD + gradient | 0.695 | 27.7 | 0.874 | 0.84 | ✓ Certified | You et al., NeurIPS 2022 |
| 2 | PatchCore + gradient | 0.601 | 23.58 | 0.753 | 0.82 | ✓ Certified | Roth et al., CVPR 2022 |
| 3 | Template Match + gradient | 0.581 | 22.66 | 0.717 | 0.84 | ✓ Certified | Brunelli, Template Matching, 2009 |
| 4 | PnP-ADMM + gradient | 0.568 | 22.39 | 0.706 | 0.81 | ✓ Certified | Venkatakrishnan et al., 2013 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%