Dev
Magnetic Force Microscopy (MFM) — Dev Tier
(3 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| lift_height | 14.0 – 104.0 | nm |
| tip_magnetization_model | -0.15 – 0.15 | - |
| electrostatic_coupling | -2.4 – 3.6 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SPM-Former + gradient | 0.682 | 26.72 | 0.851 | 0.87 | ✓ Certified | Chen et al., NanoLett 2024 |
| 2 | U-Net-SPM + gradient | 0.623 | 24.48 | 0.785 | 0.82 | ✓ Certified | SPM U-Net variant |
| 3 | E2E-BTR + gradient | 0.592 | 23.39 | 0.746 | 0.8 | ✓ Certified | Kossler et al., Sci. Rep. 2022 |
| 4 | DeepSPM + gradient | 0.592 | 23.15 | 0.737 | 0.83 | ✓ Certified | Alldritt et al., Commun. Phys. 2020 |
| 5 | MLE Reconstruction + gradient | 0.569 | 22.27 | 0.701 | 0.83 | ✓ Certified | Classical statistical method |
| 6 | DiffusionSPM + gradient | 0.545 | 21.09 | 0.65 | 0.87 | ✓ Certified | Zhang et al., 2024 |
| 7 | BTR + gradient | 0.538 | 20.71 | 0.632 | 0.89 | ✓ Certified | Villarrubia, JRNIST 1997 |
| 8 | Reg-Deconv + gradient | 0.524 | 20.57 | 0.626 | 0.84 | ✓ Certified | Dongmo et al., 2000 |
| 9 | TV-Deconvolution + gradient | 0.518 | 19.96 | 0.597 | 0.9 | ✓ Certified | TV regularization for SPM |
| 10 | ScoreSPM + gradient | 0.485 | 19.09 | 0.554 | 0.86 | ✓ Certified | Wei et al., 2025 |
Visible Data Fields
y
H_ideal
spec_ranges
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%