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https://github.com/thinklab-sjtu/r3det_tensorflow

Code for AAAI 2021 paper: R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
https://github.com/thinklab-sjtu/r3det_tensorflow

dota object-detection rotation tensorflow

Last synced: about 21 hours ago
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Code for AAAI 2021 paper: R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

Host: GitHub
URL: https://github.com/thinklab-sjtu/r3det_tensorflow
Owner: Thinklab-SJTU
License: apache-2.0
Created: 2019-08-27T14:42:38.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2021-08-29T01:54:05.000Z (about 3 years ago)
Last Synced: 2024-07-05T14:03:37.491Z (4 months ago)
Topics: dota, object-detection, rotation, tensorflow
Language: Python
Homepage:
Size: 6.85 MB
Stars: 540
Watchers: 26
Forks: 122
Open Issues: 14
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

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

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

[![arXiv](http://img.shields.io/badge/cs.CV-arXiv%3A1908.05612-B31B1B.svg)](https://arxiv.org/abs/1908.05612)

## Abstract

[R³Det](https://arxiv.org/abs/1908.05612) and [R³Det++](https://arxiv.org/abs/2004.13316) are based on [Focal Loss for Dense Object Detection](https://arxiv.org/pdf/1708.02002.pdf), and it is completed by [YangXue](https://yangxue0827.github.io/).

**[mmdetection version](https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection) is released. We also recommend a tensorflow-based [rotation detection benchmark](https://github.com/yangxue0827/RotationDetection), which is led by [YangXue](https://yangxue0827.github.io/).**

## Pipeline

![5](pipeline.png)

## Latest Performance

### DOTA1.0 (Task1)

| Model |    Backbone    |    Training data    |    Val data    |    mAP   | Model Link | Anchor | Angle Pred. | Reg. Loss| Angle Range | lr schd | Data Augmentation | GPU | Image/GPU | Configs |      

|:------------:|:------------:|:------------:|:---------:|:-----------:|:----------:|:-----------:|:-----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|    

| [RetinaNet-H](https://arxiv.org/abs/1908.05612) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 65.73 | [Baidu Drive (jum2)](https://pan.baidu.com/s/19-hEtCGxLfYuluTATQJpdg) | H | Reg. | smooth L1 | 90 | 2x | × | 3X GeForce RTX 2080 Ti | 1 | [cfgs_res50_dota_v4.py](./libs/configs/DOTA1.0/baseline/cfgs_res50_dota_v4.py) |

| [RetinaNet-H](https://arxiv.org/abs/1908.05612) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 64.17 | [Baidu Drive (j5l0)](https://pan.baidu.com/s/1Qh_LE6QeGsOBYqMzjAESsA) | H | Reg. | smooth L1 | **180** | 2x | × | 3X GeForce RTX 2080 Ti | 1 | [cfgs_res50_dota_v15.py](./libs/configs/DOTA1.0/baseline/cfgs_res50_dota_v15.py) |

| [R³Det](https://arxiv.org/abs/1908.05612) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 70.66 | [Baidu Drive (30lt)](https://pan.baidu.com/s/143sGeLNjXzcpxi9GV7FVyA) | H + R | Reg. | smooth L1 | 90 | 2x | × | 3X GeForce RTX 2080 Ti | 1 | [cfgs_res50_dota_r3det_v1.py](./libs/configs/DOTA1.0/r3det/cfgs_res50_dota_r3det_v1.py) |

| [R³Det*](https://arxiv.org/abs/1908.05612) | ResNet101_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 73.79 | [Baidu Drive (d7qp)](https://pan.baidu.com/s/1GnRbedKDfpgeYB1rUlwObQ) | H + R | Reg. | iou-smooth L1 | 90 | 3x | √ | 4X GeForce RTX 2080 Ti | 1 | [cfgs_res101_dota_r3det_v19.py](./libs/configs/DOTA1.0/r3det/cfgs_res101_dota_r3det_v19.py) |

| [R³Det*](https://arxiv.org/abs/1908.05612) | ResNet152_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 74.54 | [Baidu Drive (73bc)](https://pan.baidu.com/s/1WElLJwx15Gmu_gWUj4gE3A) | H + R | Reg. | iou-smooth L1 | 90 | 3x | √ | 4X GeForce RTX 2080 Ti | 1 | [cfgs_res152_dota_r3det_v25.py](./libs/configs/DOTA1.0/r3det/cfgs_res152_dota_r3det_v25.py) |

| [R³Det](https://arxiv.org/abs/1908.05612) | ResNet152_v1d 600->MS (+Flip) | DOTA1.0 trainval | DOTA1.0 test | 76.23 (+0.24) | [model](https://drive.google.com/file/d/1GkpiSPN-cAnvDISk5d4kjrV3Tqti_mbj/view?usp=sharing) | H + R | Reg. | iou-smooth L1 | 90 | 4x | √ | 3X GeForce RTX 2080 Ti | 1 | [cfgs_res152_dota_r3det_v3.py](./libs/configs/DOTA1.0/r3det/cfgs_res152_dota_r3det_v3.py) |     

[R³Det*](https://arxiv.org/abs/1908.05612): R³Det with two refinement stages      

**Due to the improvement of the code, the performance of this repo is gradually improving, so the experimental results in other configuration files are for reference only.**        

### Visualization

![1](demo1.png)

## 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)](https://pypi.org/project/opencv-python/)       

4、[tfplot 0.2.0](https://github.com/wookayin/tensorflow-plot) (optional)            

5、tensorflow-gpu 1.13                              

## Download Model

### Pretrain weights

1、Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz), [resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz), [resnet152_v1](http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz), [efficientnet](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet), [mobilenet_v2](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) 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 (resnet_v1d), refer to [gluon2TF](https://github.com/yangJirui/gluon2TF).    

* [Baidu Drive](https://pan.baidu.com/s/1GpqKg0dOaaWmwshvv1qWGg), password: 5ht9.          

* [Google Drive](https://drive.google.com/drive/folders/1BM8ffn1WnsRRb5RcuAcyJAHX8NS2M1Gz?usp=sharing)      

## 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/label_dict.py     

(3) Add data_name to $PATH_ROOT/data/io/read_tfrecord_multi_gpu.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

```

## Test

```  

cd $PATH_ROOT/tools

python test_dota_r3det_ms.py --test_dir='/PATH/TO/IMAGES/'  

                             --gpus=0,1,2,3,4,5,6,7  

                             -ms (multi-scale testing, optional)

                             -s (visualization, optional)

``` 

**Notice: In order to set the breakpoint conveniently, the read and write mode of the file is' a+'. If the model of the same #VERSION needs to be tested again, the original test results need to be deleted.**      

## Tensorboard

```  

cd $PATH_ROOT/output/summary

tensorboard --logdir=.

``` 

![3](images.png)

![4](scalars.png)

## 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={European Conference on Computer Vision (ECCV)},

    year={2020}

    organization={Springer}

}

@article{yang2019r3det,

    title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object},

    author={Yang, Xue and Yan, Junchi and Feng, Ziming and He, Tao},

    journal={arXiv preprint arXiv:1908.05612},

    year={2019}

}

@article{yang2020scrdet++,

    title={SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing},

    author={Yang, Xue and Yan, Junchi and Yang, Xiaokang and Tang, Jin and Liao, Wenglong and He, Tao},

    journal={arXiv preprint arXiv:2004.13316},

    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 (CVPR)},

    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