A New Benchmark for Instance-Level Image Classification

Although fine-grained image classification is able to classify more fine-grained sub-categories compared to its coarse-grained counterpart, it often fails to identify individual instances. Therefore, we propose a new instance-level image classification task which further refines the granularity of fine-grained classification in order to identify unique instances rather than a sub-category containing multiple instances. In addition, we introduce an instance-level image classification dataset, AircraftCarrier, which contains 20 global aircraft carrier classes, as the first publically available dataset for instance-level image classification. The classification of instance-level aircraft carriers can prove to be a challenging task due to large intra-category differences as well as variations in the camera view, illumination, scale, and the presence of complex backgrounds. The AircraftCarrier dataset put forward here has the potential to improve the development of instance-level image classification. At the same time, we provide a Simple Classification Head (SCH) technique for the classification of aircraft carriers, with classical convolutional neural network models as the backbone network. The SCH has better performance than a direct classification head, and these results provide a benchmark performance result for researchers. Furthermore, we evaluate several fine-grained image classification methods and give their benchmark results. Finally, we present the challenges of instance-level classification and discuss further directions. This study provides the first publicly available instance-level image classification dataset and a performance benchmark for further research. The dataset and codes can be downloaded at https://github.com/tsingqsu/AircraftCarrier_Dataset/.