Использование технологии CUDA в обучении сверточной нейросети для распознавания пыльцевых зерен
In this work, we describe the problem of automated pollen recognition using images from lighting microscope. Automated pollen recognition related to such important tasks as honey quality control, air quality control for helping to asthma and allergy patients, paleopalynology, forensic palynology. We describe the problem solution based on machine learning and CUDA. Extracted features and preprocessing steps are described. Results are compared on dataset of 5 specie. The best model is convolutional neural network with 89% of accuracy. Its performance was particularly up twice using CUDA.