CG-Net
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.
Results
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.
Experiment
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 |
