Automated S/TEM-Nanoparticle-Analysis-YOLOv8-SAM

Repository for automated nanoparticle analysis of Scanning Transmission Electron Microscopy (S/TEM) images using YOLOv8 and segment anything model (SAM). This material-agnostic ML workflow successfully detects and segments nanoparticles on different catalytic substrate materials.

Sets of object detection, segmentation, and NP analysis results from BF-TEM image of Pt NPs on graphite support (above) and HAADF STEM image of Ru NPs on Alumina support materials (below).

Scripts

• Data reader
  Read ".emd", ".emi", ".dm3", ".dm4"
• Detector
  Run YOLOv8 on the S/TEM images and generate box prompts
  Segment nanoparticles using box prompts and SAM
• SAM visualize
  Visualize segmentation
• Analysis
  Particle size and area distribution
  

Installation

Install PyTorch

Install RosettaSciIO

pip install rosettasciio

Install Ultralytics for YOLOv8

pip install ultralytics

Install Segment Anything Model (SAM)

https://github.com/facebookresearch/segment-anything

weights for YOLOv8 particle detection here

AI-assisted particle analysis software nanoDetect

Please fill out the form here to gain access to nanoDetect software installer

Demo

Cite

@misc{genc2024versatilemachinelearningworkflow,
      title={A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles}, 
      author={Arda Genc and Justin Marlowe and Anika Jalil and Libor Kovarik and Phillip Christopher},
      year={2024},
      eprint={2410.01213},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2410.01213}, 
}