tempo4D

Transmission Electron Microscope Pattern Observation

This repository provides an automated pipeline for analyzing 4D-STEM datasets using YOLOv8n.
The workflow enables end-to-end processing of large-scale 4D-STEM datasets for phase identification, orientation mapping (coming soon!), and strain analysis.

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🧬 Phase Mapping

Phase mapping of complex phase-transformed Ti-50Nb alloy using object detection-based pattern recognition.

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🧪 Strain Mapping

Strain mapping of Si/SiGe multilayers demonstrating coherent lattice mismatch analysis.

Supported file formats:

• Thermo Fisher Scientific: .emi, .xml (EMPAD)
• GATAN: .dm3, .dm4
• Dectris: .h5
• NanoMegas: .blo
• Direct Electron: .de5
• Standard: .h5, .hdf5

🛠️ Installation

Python ≥ 3.9 is required.

We recommend creating a new virtual environment:

conda create -n tempo4d python=3.9
conda activate tempo4d

⚡ Install PyTorch (Recommended First)

If you have a CUDA-capable GPU, install a CUDA-compatible version of PyTorch before installing tempo4d.

👉 Install PyTorch

📦 Install tempo4d

pip install tempo4d

This will install all required dependencies, including:

• PyQt5
• pyqtgraph
• OpenCV
• matplotlib
• Ultralytics (for YOLOv8)
• rosettasciio[all] (for TEM file support)

Demo

Please also see the tempo4d_demo.ipynb notebook in the demo folder.

Download example data from GATAN

Cite

@misc{genc2025neuralobjectdetection4d,
      title={Neural Object Detection for 4D STEM: High-Throughput Sub-Pixel Electron Diffraction Pattern Recognition}, 
      author={Arda Genc and Ravit Silverstein},
      year={2025},
      eprint={2506.04477},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2506.04477}, 
}