  Single particle imaging (SPI) is a promising method for native structure
determination which has undergone a fast progress with the development of X-ray
Free-Electron Lasers. Large amounts of data are collected during SPI
experiments, driving the need for automated data analysis. The necessary data
analysis pipeline has a number of steps including binary object classification
(single versus multiple hits). Classification and object detection are areas
where deep neural networks currently outperform other approaches. In this work,
we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting
transfer learning, a moderate amount of data is sufficient for training of the
neural network. We demonstrate here that a convolutional neural network (CNN)
can be successfully used to classify data from SPI experiments. We compare the
results of classification for the two different networks, with different depth
and architecture, by applying them to the same SPI data with different data
representation. The best results are obtained for YOLOv2 color images linear
scale classification, which shows an accuracy of about 97% with the precision
and recall of about 52% and 61%, respectively, which is in comparison to manual
data classification.
