Benchmark and Model Zoo

Environment

Hardware

4 NVIDIA Tesla V100 GPUs
Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz

Software environment

Python 3.6 / 3.7
PyTorch 1.1.0
CUDA 10.0.176
CUDNN 7.4.1

Common settings

All baselines were trained using 4 GPU with a batch size of 8 (2 images per GPU).
We adopt the same training schedules as Detectron. 1x indicates 12 epochs and 2x indicates 24 epochs, which corresponds to slightly less iterations than Detectron and the difference can be ignored.
We report the inference time as the overall time including data loading, network forwarding and post processing in chips with size of 1024.

Baselines

The folowing shows the baseline results. For more results, see our paper.

Baseline results on DOTA (R-FPN-50, without data augmentations)
Baseline results of different backbones on DOTA-v2.0 (without data augmentations).

SOTA on DOTA-v1.0.
SOTA on DOTA-v1.5.
Class-wise AP on DOTA-v1.0.
Class-wise AP on DOTA-v1.5.
Class-wise AP on DOTA-v2.0.