  With the rapid development of deep learning, many deep learning-based
approaches have made great achievements in object detection task. It is
generally known that deep learning is a data-driven method. Data directly
impact the performance of object detectors to some extent. Although existing
datasets have included common objects in remote sensing images, they still have
some limitations in terms of scale, categories, and images. Therefore, there is
a strong requirement for establishing a large-scale benchmark on object
detection in high-resolution remote sensing images. In this paper, we propose a
novel benchmark dataset with more than 1 million instances and more than 15,000
images for Fine-grAined object recognItion in high-Resolution remote sensing
imagery which is named as FAIR1M. All objects in the FAIR1M dataset are
annotated with respect to 5 categories and 37 sub-categories by oriented
bounding boxes. Compared with existing detection datasets dedicated to object
detection, the FAIR1M dataset has 4 particular characteristics: (1) it is much
larger than other existing object detection datasets both in terms of the
quantity of instances and the quantity of images, (2) it provides more rich
fine-grained category information for objects in remote sensing images, (3) it
contains geographic information such as latitude, longitude and resolution, (4)
it provides better image quality owing to a careful data cleaning procedure. To
establish a baseline for fine-grained object recognition, we propose a novel
evaluation method and benchmark fine-grained object detection tasks and a
visual classification task using several State-Of-The-Art (SOTA) deep
learning-based models on our FAIR1M dataset. Experimental results strongly
indicate that the FAIR1M dataset is closer to practical application and it is
considerably more challenging than existing datasets.
