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

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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

5

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

1

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.

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=.

3

4

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

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