  Nematode worms are one of most abundant metazoan groups on the earth,
occupying diverse ecological niches. Accurate recognition or identification of
nematodes are of great importance for pest control, soil ecology,
bio-geography, habitat conservation and against climate changes. Computer
vision and image processing have witnessed a few successes in species
recognition of nematodes; however, it is still in great demand. In this paper,
we identify two main bottlenecks: (1) the lack of a publicly available imaging
dataset for diverse species of nematodes (especially the species only found in
natural environment) which requires considerable human resources in field work
and experts in taxonomy, and (2) the lack of a standard benchmark of
state-of-the-art deep learning techniques on this dataset which demands the
discipline background in computer science. With these in mind, we propose an
image dataset consisting of diverse nematodes (both laboratory cultured and
naturally isolated), which, to our knowledge, is the first time in the
community. We further set up a species recognition benchmark by employing
state-of-the-art deep learning networks on this dataset. We discuss the
experimental results, compare the recognition accuracy of different networks,
and show the challenges of our dataset. We make our dataset publicly available
at: https://github.com/xuequanlu/I-Nema
