This codebase is created to build benchmarks for object detection in aerial images. It is modified from mmdetection. The master branch works with PyTorch 1.1 or higher. If you would like to use PyTorch 0.4.1, please checkout to the pytorch-0.4.1 branch.


Visualization results for oriented object detection on the test set of DOTA.


Comparison to the baseline on DOTA for oriented object detection with ResNet-101. The figures with blue boxes are the results of the baseline and pink boxes are the results of our proposed CG-Net.



ImageNet Pretrained Model from Pytorch

The effectiveness of our proposed methods with different backbone network on the test of DOTA.

Backbone +CG Weight mAP(%)
ResNet-50 download 73.26
ResNet-50 + download 74.21
ResNet-101 download 73.06
ResNet-101 + download 74.30
ResNet-152 download 72.78
ResNet-152 + download 73.53

CG-Net Results in DOTA.

Backbone Aug Rotate Task Weight mAP(%)
ResNet-101 + Oriented download 77.89
ResNet-101 + Horizontal download 78.26