he dataset contains a total of 6772 images of military trucks, military tanks, military aircraft, military helicopters, civilian cars and civilian aircraft. Out of which, 6642 are positive images and 130 are negative images. Positive images are those which contain one or more of the defined objects (i.e. Military Truck, Military Tank, Military Aircraft, Military Helicopter, Civilian Car, Civilian Aircraft). Negative images are those which contain anything else except the defined objects. All the positive images contains a total of 11528 objects. The use of negative images has a specific purpose, which is, to make the model learn about such an environment when there are no detectable objects in the image. The extension of images was converted from .jpeg and .png to .jpg, since, it is difficult to process the models with the different extensions. After the dataset collection and pre-processing, the formation of specific format files is to be carried out for dealing further with the object detection models. The Labelling is done in .txt, .csv, .xml, and tf record formats.

Original Data

Citation

>Gupta, Priyanka; Pareek, Bhavya; Singal, Gaurav; Rao, D Vijay (2021), “Military and Civilian Vehicles Classification”, Mendeley Data, V1, doi: 10.17632/njdjkbxdpn.1

Acknowlegement

Foto von Dan Kb auf Unsplash