R³Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

Abstract

R³Det is based on Focal Loss for Dense Object Detection, and it is completed by YangXue.

Pipeline

Performance

More results and trained models are available in the MODEL_ZOO.md.

DOTA1.0

Model	Backbone	Training data	Val data	mAP	GPU	Image/GPU	Anchor	Reg. Loss	lr schd	Data Augmentation	Configs
RetinaNet (baseline)	ResNet50_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	62.22	1X GeForce RTX 2080 Ti	1	H	smooth L1	1x	No	cfgs_res50_dota_v4.py
RetinaNet (baseline)	ResNet50_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	62.79	8X GeForce RTX 2080 Ti	1	H	smooth L1	2x	No	cfgs_res50_dota_v8.py
RetinaNet (baseline)	ResNet101_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	64.19	1X GeForce RTX 2080 Ti	1	H	smooth L1	1x	No	cfgs_res101_dota_v9.py
RetinaNet (baseline)	ResNet152_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	65.79	8X GeForce RTX 2080 Ti	1	H	smooth L1	2x	No	cfgs_res152_dota_v12.py
RetinaNet (baseline)	ResNet50_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	61.94	1X GeForce RTX 2080 Ti	1	R	smooth L1	1x	No	cfgs_res50_dota_v1.py
RetinaNet (baseline)	ResNet50_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	62.25	8X GeForce RTX 2080 Ti	1	R	smooth L1	2x	No	cfgs_res50_dota_v10.py
RetinaNet	ResNet50_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	68.65	1X GeForce RTX 2080 Ti	1	R	iou-smooth L1	1x	No	cfgs_res50_dota_v5.py
R3Det	ResNet50_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	65.73	8X GeForce RTX 2080 Ti	1	H + R	smooth L1	2x	No	cfgs_res50_dota_r3det_v1.py
R3Det*	ResNet50_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	67.20	8X GeForce RTX 2080 Ti	1	H + R	smooth L1	2x	No	cfgs_res50_dota_r3det_v2.py
R3Det	ResNet101_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	71.69	8X GeForce RTX 2080 Ti	1	H + R	smooth L1	3x	Yes	-
R3Det	ResNet152_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	72.81	8X GeForce RTX 2080 Ti	1	H + R	smooth L1	4x	Yes	-
R3Det*	ResNet152_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	73.74	8X GeForce RTX 2080 Ti	1	H + R	smooth L1	4x	Yes	-
R3Det++	ResNet50_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	68.54	8X GeForce RTX 2080 Ti	1	H + R	smooth L1	2x	No	cfgs_res50_dota_r3det_plusplus_v2.py
R3Det++	ResNet152_v1d 600->800	DOTA1.0 trainval	DOTA1.0 test	74.41	8X GeForce RTX 2080 Ti	1	H + R	smooth L1	4x	Yes	-
R3Det++	ResNet152_v1d MS	DOTA1.0 trainval	DOTA1.0 test	76.56	4X GeForce RTX 2080 Ti	1	H + R + more	smooth L1	6x	Yes	cfgs_res152_dota_r3det_plusplus_v1.py

R³Det*: R³Det with two refinement stages The performance of all models comes from the source paper.

Visualization

My Development Environment

docker images: docker pull yangxue2docker/yx-tf-det:tensorflow1.13.1-cuda10-gpu-py3
1、python3.5 (anaconda recommend)
2、cuda >= 10.0
3、opencv(cv2)
4、tfplot 0.2.0 (optional)
5、tensorflow 1.13

Download Model

Pretrain weights

1、Please download resnet50_v1, resnet101_v1 pre-trained models on Imagenet, put it to data/pretrained_weights.
2、(Recommend in this repo) Or you can choose to use a better backbone, refer to gluon2TF.

• Baidu Drive, password: 5ht9.
• Google Drive

Compile

cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace (or make)

cd $PATH_ROOT/libs/box_utils/
python setup.py build_ext --inplace

Train

1、If you want to train your own data, please note:

(1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py
(2) Add category information in $PATH_ROOT/libs/label_name_dict/lable_dict.py     
(3) Add data_name to $PATH_ROOT/data/io/read_tfrecord.py 

2、Make tfrecord
For DOTA dataset:

cd $PATH_ROOT\data\io\DOTA
python data_crop.py

cd $PATH_ROOT/data/io/  
python convert_data_to_tfrecord.py --VOC_dir='/PATH/TO/DOTA/' 
                                   --xml_dir='labeltxt'
                                   --image_dir='images'
                                   --save_name='train' 
                                   --img_format='.png' 
                                   --dataset='DOTA'

3、Multi-gpu train

cd $PATH_ROOT/tools
python multi_gpu_train_r3det.py

Eval

cd $PATH_ROOT/tools
python test_dota_r3det.py --test_dir='/PATH/TO/IMAGES/'  
                          --gpus=0,1,2,3,4,5,6,7          

Tensorboard

cd $PATH_ROOT/output/summary
tensorboard --logdir=.

Citation

If this is useful for your research, please consider cite.

@article{yang2019r3det,
    title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object},
    author={Yang, Xue and Liu, Qingqing and Yan, Junchi and Li, Ang and Zhiqiang, Zhang and Gang, Yu},
    journal={arXiv preprint arXiv:1908.05612},
    year={2019}
}

@inproceedings{xia2018dota,
    title={DOTA: A large-scale dataset for object detection in aerial images},
    author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={3974--3983},
    year={2018}
}

Reference

1、https://github.com/endernewton/tf-faster-rcnn
2、https://github.com/zengarden/light_head_rcnn
3、https://github.com/tensorflow/models/tree/master/research/object_detection
4、https://github.com/fizyr/keras-retinanet