Dev

Cryo-EM Single Particle Analysis — Dev Tier

(5 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
defocus_error -120.0 – 180.0 nm
astigmatism -24.0 – 36.0 nm
beam_tilt -0.24 – 0.36 mrad
ice_thickness_variation 42.8 – 60.8 nm

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 CryoFormer + gradient 0.755 31.13 0.933 0.85 ✓ Certified Gao et al., CVPR 2024
2 CryoSTAR + gradient 0.750 31.54 0.937 0.8 ✓ Certified Yang et al., Nat. Methods 2024
3 DiffusionCryo + gradient 0.721 29.65 0.911 0.8 ✓ Certified Luo et al., arXiv 2024
4 cryoDRGN + gradient 0.668 26.73 0.851 0.8 ✓ Certified Zhong et al., Nat. Methods 2021
5 CryoGEM + gradient 0.656 26.13 0.836 0.8 ✓ Certified He et al., NeurIPS 2023
6 cryoSPARC + gradient 0.644 25.76 0.825 0.78 ✓ Certified Punjani et al., Nat. Methods 2017
7 RELION-3D + gradient 0.559 22.27 0.701 0.78 ✓ Certified Scheres, J. Struct. Biol. 2012
8 IsoNet + gradient 0.547 21.67 0.676 0.8 ✓ Certified Liu et al., Nat. Commun. 2021
9 CTFFIND4 + gradient 0.490 19.33 0.566 0.85 ✓ Certified Rohou & Grigorieff, J. Struct. Biol. 2015

Visible Data Fields

y H_ideal spec_ranges

Dataset

Format: HDF5
Scenes: 5

Scoring Formula

0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

PSNR: 40% SSIM: 40% Consistency: 20%
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