  Cryo-electron tomography (cryo-ET) is an imaging technique that allows
three-dimensional visualization of macro-molecular assemblies under near-native
conditions. Cryo-ET comes with a number of challenges, mainly low
signal-to-noise and inability to obtain images from all angles. Computational
methods are key to analyze cryo-electron tomograms.
  To promote innovation in computational methods, we generate a novel simulated
dataset to benchmark different methods of localization and classification of
biological macromolecules in tomograms. Our publicly available dataset contains
ten tomographic reconstructions of simulated cell-like volumes. Each volume
contains twelve different types of complexes, varying in size, function and
structure.
  In this paper, we have evaluated seven different methods of finding and
classifying proteins. Seven research groups present results obtained with
learning-based methods and trained on the simulated dataset, as well as a
baseline template matching (TM), a traditional method widely used in cryo-ET
research. We show that learning-based approaches can achieve notably better
localization and classification performance than TM. We also experimentally
confirm that there is a negative relationship between particle size and
performance for all methods.
