Identifying atomic features in aberration-corrected scanning transmission electron microscopy (STEM) data is critical to understanding structures and properties of materials. Machine learning (ML) models have been applied to accelerate these tasks. The training sets for these ML models are typically constructed with codes that provide simulations of STEM images alongside desired labels. However, these simulated images are often limited by the oversimplified model and deviate from the experimental images, limiting the accuracy and precision of ML training. We present an approach to generating realistic STEM images by employing a cycleGAN to automatically add realistic microscopy features and noise profiles to simulated data. We also train a defect-identification neural network using these generated images and evaluate the model on real STEM images to locate atomic defects within them. The application of CycleGAN provides other machine learning models with more realistic training data for any type of supervised learning.