Awesome
Introduction
This codebase is created to build benchmarks for object detection on DIOR. It is modified from mmdetection.
The master branch works with PyTorch 1.3 to 1.6. The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage.
Major features
we do have no extra data augmentation tricks，this repo follows the original features in mmdetection.
License
This project is released under the Apache 2.0 license.
Changelog
v2.7.0 was released in 30/11/2020. Please refer to changelog.md for details and release history. A comparison between v1.x and v2.0 codebases can be found in compatibility.md.
Benchmark and model zoo
- Results are available in the Model zoo.
- You can find the detailed configs in configs/DIOR.
Installation
Please refer to get_started.md for installation.
Getting Started
Please see get_started.md for the basic usage of dior_detect. We provide colab tutorial, and full guidance for quick run with existing dataset and with new dataset for beginners. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, customizing models, customizing runtime settings and useful tools.
Please refer to FAQ for frequently asked questions.
Citing
If you use [DIOR] dataset, codebase or models in your research, please consider cite .
@article{li2020object,
title={Object detection in optical remote sensing images: A survey and a new benchmark},
author={Li, Ke and Wan, Gang and Cheng, Gong and Meng, Liqiu and Han, Junwei},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={159},
pages={296--307},
year={2020},
publisher={Elsevier}
}