![]() Furthermore, we have two test sets, namely test-dev and test-challenge. ![]() To avoid the problem of overfitting, the proportion of training and validation set is smaller than the test set. The 11,268 images of DOTA are split into training, validation, test-dev, and test-challenge sets. Compared to DOTA-v1.5, it further adds the new categories of ”airport” and ”helipad”. There are 18 common categories, 11,268 images and 1,793,658 instances in DOTA-v2.0. This version was released for the DOAI Challenge 2019 on Object Detection in Aerial Images in conjunction with IEEE CVPR 2019.ĭOTA-v2.0 collects more Google Earth, GF-2 Satellite, and aerial images. The number of images and dataset splits are the same as DOTA-v1.0. Moreover, a new category, ”container crane” is added. The proportions of the training set, validation set, and testing set in DOTA-v1.0 are 1/2, 1/6, and 1/3, respectively.ĭOTA-v1.5 uses the same images as DOTA-v1.0, but the extremely small instances (less than 10 pixels) are also annotated. Now it has three versions:ĭOTA-v1.0 contains 15 common categories, 2,806 images and 188, 282 instances. We will continue to update DOTA, to grow in size and scope to reflect evolving real-world conditions. The instances in DOTA images are annotated by experts in aerial image interpretation by arbitrary (8 d.o.f.) quadrilateral. Each image is of the size in the range from 800 × 800 to 20,000 × 20,000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. ![]() The images are collected from different sensors and platforms. It can be used to develop and evaluate object detectors in aerial images. DOTA is a large-scale dataset for object detection in aerial images.
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