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

Code for ECCV 2020 paper: Arbitrary-Oriented Object Detection with Circular Smooth Label
https://github.com/thinklab-sjtu/csl_retinanet_tensorflow

angle-classification object-detection smooth-label tensorflow

Last synced: 27 days ago
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Code for ECCV 2020 paper: Arbitrary-Oriented Object Detection with Circular Smooth Label

Host: GitHub
URL: https://github.com/thinklab-sjtu/csl_retinanet_tensorflow
Owner: Thinklab-SJTU
License: apache-2.0
Created: 2020-03-07T08:48:15.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2022-03-21T08:53:42.000Z (almost 3 years ago)
Last Synced: 2024-12-06T19:04:00.511Z (about 1 month ago)
Topics: angle-classification, object-detection, smooth-label, tensorflow
Language: Python
Homepage: https://arxiv.org/abs/2003.05597v2
Size: 25.1 MB
Stars: 191
Watchers: 7
Forks: 32
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Arbitrary-Oriented Object Detection with Circular Smooth Label

[![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%3A2003.05597-B31B1B.svg)](https://arxiv.org/abs/2003.05597v2)

 :rocket::rocket::rocket:	**News:** CSL is supported at [MMRotate](https://github.com/open-mmlab/mmrotate)  :rocket::rocket::rocket:

## Abstract

This repo is 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/).

**We also recommend a tensorflow-based [rotation detection benchmark](https://github.com/yangxue0827/RotationDetection), which is led by [YangXue](https://yangxue0827.github.io/).**

## Pipeline

![2](pipeline.png)

## Circular Smooth Label

![5](CSL.png)

## Latest Performance

### 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](https://arxiv.org/abs/2003.05597) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 67.38 | [Baidu Drive (g3wt)](https://pan.baidu.com/s/1nrIs-oYA53qQzlPjqYkMJQ) | H | **Gaussian (r=1, w=10)** | smooth L1 | **180** | 2x | × | 3X GeForce RTX 2080 Ti | 1 | [cfgs_res50_dota_v45.py](./libs/configs/DOTA1.0/CSL/cfgs_res50_dota_v45.py) |

| [CSL](https://arxiv.org/abs/2003.05597) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 68.73 | [Baidu Drive (3a4t)](https://pan.baidu.com/s/1yC-b9Y4ZVgVkQvpPRRLmhw) | H | **Pulse (w=1)** | smooth L1 | **180** | 2x | × | 2X GeForce RTX 2080 Ti | 1 | [cfgs_res50_dota_v41.py](./libs/configs/DOTA1.0/CSL/cfgs_res50_dota_v41.py) |

**Notice:**     

**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.**        

**Please refer to [new repo](https://github.com/Thinklab-SJTU/R3Det_Tensorflow) for the latest progress.**

### 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 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) 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](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.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

```

## Test

```  

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)

``` 

**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)

## Object Heading Detection

- [OHD-SJTU]((https://yangxue0827.github.io/OHD-SJTU.html)): Download from [here](https://yangxue0827.github.io/OHD-SJTU.html).     

- [OHDet](https://github.com/SJTU-Thinklab-Det/OHDet_Tensorflow): The blue border in the bounding box represents the predicted head of the object. More detail trfer to [here](https://yangxue0827.github.io/CSL_GCL_OHDet.html).          

![6](ohdet.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{yang2020on,

    title={On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited},

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

    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