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”Īnd ”helipad”. There are 18 common categories,ġ1,268 images and 1,793,658 instances in DOTA-v2.0. DOTA-v2.0 collects more Google Earth, GF-2 Satellite, and aerial images.This version was released for the DOAI ChallengeĢ019 on Object Detection in Aerial Images in conjunction with IEEE CVPR 2019. The number of images and dataset splits are the same as DOTA-v1.0. Moreover, a new category, ”container crane” is added.
DOTA-v1.5 uses the same images as DOTA-v1.0, but the extremely small instances (less than 10 pixels)Īre also annotated.Set, validation set, and testing set in DOTA-v1.0 are 1/2, 1/6, and 1/3, respectively. DOTA-v1.0 contains 15 common categories, 2,806 images and 188, 282 instances.
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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 rangeĢ0,000 × 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
The new benchmark DOTA-v2.0, including dataset, code library and 70 baselines, is released. LUAI, a contest of object in aerial images on ICCV 2021, is now open.