zcdliuwei / CSL_RetinaNet_Tensorflow

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Arbitrary-Oriented Object Detection with Circular Smooth Label

Abstract

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

Pipeline

2

Circular Smooth Label

5

Performance

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

DOTA1.0 (Task1)

Model Backbone Training data Val data mAP Model Link Anchor Label Mode Reg. Loss Angle Range lr schd Data Augmentation GPU Image/GPU Configs
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 39.52 - H Pulse smooth L1 90 1x × 1X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v20.py
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 58.86 - H Rectangular smooth L1 90 1x × 1X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v21.py
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 60.15 - H Triangle smooth L1 90 1x × 1X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v22.py
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 63.51 - H Gaussian smooth L1 90 2x × 2X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v18.py
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 42.06 - H Pulse smooth L1 180 2x × 4X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v28.py
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 61.98 - H Rectangular smooth L1 180 2x × 2X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v23.py
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 57.94 - H Triangle smooth L1 180 2x × 4X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v26.py
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 64.50 - H Gaussian smooth L1 180 2x × 2X Quadro RTX 8000 1 cfgs_res50_dota_v27.py
CSL ResNet50_v1 600->800 DOTA1.0 trainval DOTA1.0 test 65.09 - H Gaussian smooth L1 + atan(theta) 180 2x × 2X Quadro RTX 8000 1 cfgs_res50_dota_v31.py
CSL ResNet152_v1 MS DOTA1.0 trainval DOTA1.0 test 70.29 model H Gaussian smooth L1 + atan(theta) 180 2x 2X Quadro RTX 8000 1 cfgs_res152_dota_v36.py

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) 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.py

Eval

cd $PATH_ROOT/tools
python test_dota.py --test_dir='/PATH/TO/IMAGES/'  
                    --gpus=0,1,2,3,4,5,6,7  
                    --s (visualization, optional)
                    --ms (multi-scale test, optional)

Tensorboard

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

3

4

Citation

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

@article{yang2020arbitrary,
    title={Arbitrary-Oriented Object Detection with Circular Smooth Label},
    author={Yang, Xue and Yan, Junchi},
    journal={arXiv preprint arXiv:2003.05597},
    year={2020}
}

@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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License:MIT License


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