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https://github.com/drryanhuang/dal-paddle
https://github.com/drryanhuang/dal-paddle
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- Host: GitHub
- URL: https://github.com/drryanhuang/dal-paddle
- Owner: DrRyanHuang
- License: apache-2.0
- Created: 2022-08-06T01:33:56.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-02-01T16:08:07.000Z (almost 2 years ago)
- Last Synced: 2024-11-26T11:14:10.866Z (2 months ago)
- Language: Python
- Size: 2.12 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DAL
This project hosts the official implementation for our AAAI 2021 paper:
**Dynamic Anchor Learning for Arbitrary-Oriented Object Detection** [[arxiv](https://arxiv.org/abs/2012.04150)] [[comments](https://zhuanlan.zhihu.com/p/337272217)].
## Abstract
In this paper, we propose a dynamic anchor learning (DAL) method, which utilizes the newly defined matching degree to comprehensively evaluate the localization potential of the anchors and carry out a more efficient label assignment process. In this way, the detector can dynamically select high-quality anchors to achieve accurate object detection, and the divergence between classification and regression will be alleviated.
## Getting Started
The codes build Rotated RetinaNet with the proposed DAL method for rotation object detection. The supported datasets include: DOTA, HRSC2016, ICDAR2013, ICDAR2015, UCAS-AOD, NWPU VHR-10, VOC.
### Installation
Insatll requirements:
```
pip install -r requirements.txt
pip install git+git://github.com/lehduong/torch-warmup-lr.git
```
Build the Cython and CUDA modules:
```
cd $ROOT/utils
sh make.sh
cd $ROOT/utils/overlaps_cuda
python setup.py build_ext --inplace
```
Installation for DOTA_devkit:
```
cd $ROOT/datasets/DOTA_devkit
sudo apt-get install swig
swig -c++ -python polyiou.i
python setup.py build_ext --inplace
```
### Inference
You can use the following command to test a dataset. Note that `weight`, `img_dir`, `dataset`,`hyp` should be modified as appropriate.
```
python demo.py
```
### Train
1. Move the dataset to the `$ROOT` directory.
2. Generate imageset files for daatset division via:
```
cd $ROOT/datasets
python generate_imageset.py
```
3. Modify the configuration file `hyp.py` and arguments in `train.py`, then start training:
```
python train.py
```
### Evaluation
Different datasets use different test methods. For UCAS-AOD/HRSC2016/VOC/NWPU VHR-10, you need to prepare labels in the appropriate format in advance. Take evaluation on HRSC2016 for example:
```
cd $ROOT/datasets/evaluate
python hrsc2gt.py
```
then you can conduct evaluation:
```
python eval.py
```
Note that :
- the script needs to be executed **only once**, but testing on different datasets needs to be executed again.
- the imageset file used in `hrsc2gt.py` is generated from `generate_imageset.py`.
## Main Results
| Method | Dataset | Bbox | Backbone | Input Size | mAP/F1 |
| ------ | ----------- | ---- | ---------- | ---------- | ------ |
| DAL | DOTA | OBB | ResNet-101 | 800 x 800 | 71.78 |
| DAL | UCAS-AOD | OBB | ResNet-101 | 800 x 800 | 89.87 |
| DAL | HRSC2016 | OBB | ResNet-50 | 416 x 416 | 88.60 |
| DAL | ICDAR2015 | OBB | ResNet-101 | 800 x 800 | 82.4 |
| DAL | ICDAR2013 | HBB | ResNet-101 | 800 x 800 | 81.3 |
| DAL | NWPU VHR-10 | HBB | ResNet-101 | 800 x 800 | 88.3 |
| DAL | VOC 2007 | HBB | ResNet-101 | 800 x 800 | 76.1 |
## Detections
## Citation
If you find our work or code useful in your research, please consider citing:
```
@inproceedings{ming2021dynamic,
title={Dynamic Anchor Learning for Arbitrary-Oriented Object Detection},
author={Ming, Qi and Zhou, Zhiqiang and Miao, Lingjuan and Zhang, Hongwei and Li, Linhao},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={3},
pages={2355--2363},
year={2021}
}
```
If you have any questions, please contact me via issue or [email]([email protected]).