  By applying our previously developed two-step scheme for galaxy morphology
classification, we present a catalog of galaxy morphology for H-band selected
massive galaxies in the COSMOS-DASH field, which includes 17292 galaxies with
stellar mass $M_{\star}>10^{10}~M_{\odot}$ at $0.5<z<2.5$. The classification
scheme is designed to provide a complete morphology classification for galaxies
via a combination of two machine-learning steps. We first use an unsupervised
machine learning method (i.e., bagging-based multi-clustering) to cluster
galaxies into five categories: spherical (SPH), early-type disk (ETD),
late-type disk (LTD), irregular (IRR), and unclassified (UNC). About 48\% of
galaxies (8258/17292) are successfully clustered during this step. For the
remaining sample, we adopt a supervised machine learning method (i.e.,
GoogLeNet) to classify them, during which galaxies that are well-classified in
the previous step are taken as our training set. Consequently, we obtain a
morphology classification result for the full sample. The t-SNE test shows that
galaxies in our sample can be well aggregated. We also measure the parametric
and nonparametric morphologies of these galaxies. We find that the S\'{e}rsic
index increases from IRR to SPH and the effective radius decreases from IRR to
SPH, consistent with the corresponding definitions. Galaxies from different
categories are separately distributed in the $G$--$M_{20}$ space. Such
consistencies with other characteristic descriptions of galaxy morphology
demonstrate the reliability of our classification result, ensuring that it can
be used as a basic catalog for further galaxy studies.
