*_domain — physics-aligned augmentations (T_phys): measurement-consistent symmetries plus acquisition-driven perturbations (noise, intensity variation, reciprocal-space scaling, diffraction tilt, ...).
*_original — standard natural-image augmentations (T_orig): random crop, horizontal flip, blur, photometric perturbations.

Pretraining data:

cem500k/ — real-space cellular EM (CEM500K subset, 10k images).
4dstem/ — simulated LiNiO2 4D-STEM diffraction patterns (Scheunert et al. subset, 10k patterns).

All encoders are single-channel (grayscale) ViT-B backbones.

Available models

Path	Method	Pretraining data	Augmentations
`cem500k/dinov2_domain`	dinov2	cem500k	physics-aligned
`cem500k/dinov2_original`	dinov2	cem500k	natural-image
`cem500k/ijepa_domain`	ijepa	cem500k	physics-aligned
`cem500k/ijepa_original`	ijepa	cem500k	natural-image
`cem500k/mae_domain`	MAE	cem500k	physics-aligned
`cem500k/mae_original`	MAE	cem500k	natural-image
`cem500k/simclr_domain`	simclr	cem500k	physics-aligned
`cem500k/simclr_original`	simclr	cem500k	natural-image
`cem500k/vicregl_domain`	vicregl	cem500k	physics-aligned
`cem500k/vicregl_original`	vicregl	cem500k	natural-image
`4dstem/dinov2_domain`	dinov2	4dstem	physics-aligned
`4dstem/dinov2_original`	dinov2	4dstem	natural-image
`4dstem/ijepa_domain`	ijepa	4dstem	physics-aligned
`4dstem/ijepa_original`	ijepa	4dstem	natural-image
`4dstem/mae_domain`	MAE	4dstem	physics-aligned
`4dstem/mae_original`	MAE	4dstem	natural-image
`4dstem/simclr_domain`	simclr	4dstem	physics-aligned
`4dstem/simclr_original`	simclr	4dstem	natural-image
`4dstem/vicregl_domain`	vicregl	4dstem	physics-aligned
`4dstem/vicregl_original`	vicregl	4dstem	natural-image

Usage

With the accompanying code (https://github.com/DL4EM/physics-aligned-ssl):

python

from em_ssl.hub import load_encoder

encoder = load_encoder("cem500k/dinov2_domain", repo_id="DL4EM/physics-aligned-ssl")

import torch
images = torch.randn(4, 1, 128, 128)   # grayscale EM crops in [0, 1]
features = encoder(images)

Without the codebase, each encoder.pt is a plain PyTorch checkpoint:

python

import torch
from huggingface_hub import hf_hub_download

path = hf_hub_download("DL4EM/physics-aligned-ssl", "cem500k/dinov2_domain/encoder.pt")
ckpt = torch.load(path, map_location="cpu", weights_only=True)
state_dict = ckpt["encoder_state_dict"]     # ViT-B weights
print(ckpt["backbone"], ckpt["backbone_kwargs"])

Inputs are single-channel images normalised to [0, 1] (percentile normalisation was used during pretraining). Real-space EM models were trained on 128x128 crops; 4D-STEM models on 224x224 crops.

Citation

bibtex

@inproceedings{kazimi2026physicsaligned,
  title     = {Physics-Aligned Self-Supervised Learning for Scientific Imaging},
  author    = {Kazimi, Bashir and Sandfeld, Stefan},
  booktitle = {DAGM German Conference on Pattern Recognition (GCPR)},
  year      = {2026}
}

About

Developed by the Deep Learning for Electron Microscopy (DL4EM) group at the Institute for Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich. For questions, please open an issue on the GitHub repository.