  Automated identification of insects is a tough task where many challenges
like data limitation, imbalanced data count, and background noise needs to be
overcome for better performance. This paper describes such an image dataset
which consists of a limited, imbalanced number of images regarding six genera
of subfamily Cicindelinae (tiger beetles) of order Coleoptera. The diversity of
image collection is at a high level as the images were taken from different
sources, angles and on different scales. Thus, the salient regions of the
images have a large variation. Therefore, one of the main intentions in this
process was to get an idea about the image dataset while comparing different
unique patterns and features in images. The dataset was evaluated on different
classification algorithms including deep learning models based on different
approaches to provide a benchmark. The dynamic nature of the dataset poses a
challenge to the image classification algorithms. However transfer learning
models using softmax classifier performed well on current dataset. The tiger
beetle classification can be challenging even to a trained human eye,
therefore, this dataset opens a new avenue for the classification algorithms to
develop, to identify features which human eyes have not identified.
