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A novel Efficient Information Reuse Network (EIRNet) for fine-grained ship recognition in remote sensing images

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# EIRNet

This is the code of paper ["Fine-Grained Recognition for Oriented Ship Against Complex Scenes in Optical Remote in Remote Sensing Images"][4].

Introduction
--
Efficient Information Reuse Network (EIRNet) is a two-stage end-to-end network designed for fine-grained ship recognition in remote sensing images.

We tested EIRNet on two datasets: HRSC2016 and [DOSR][1]. HRSC2016 is a widely used dataset for fine-grained ship recognition. DOSR is a new fine-grained ship recognition dataset collected by ourselves with more complex scenes. Extensive experiments demonstrate our EIRNet achieves state-of-the-art performance on DOSR and HRSC2016.

Experiment Environment
--
Ubuntu / Win10

Python 3.5

Tensorflow 1.10.0

Cuda 9.0

GeForce GTX 1080Ti

How to use
--

### 1. Download the code and prepare for training.

```
git clone https://github.com/yaqihan-9898/EIRNet  # clone repo
cd EIRNet
pip install -r requirements.txt  # install
```

### 2. Compile

For linux:
```
cd ./data/coco/PythonAPI
python setup.py build_ext --inplace
python setup.py build_ext install
cd ./lib/utils　　
python setup.py build_ext --inplace
```
For windows:
```
cd /d .\data\coco\PythonAPI
python setup.py build_ext --inplace
python setup.py build_ext install
cd /d .\lib\utils　　
python setup.py build_ext --inplace
```
### 3. Prepare data and pretrained model

Put all dataset images under ./data/VOCdevkit2007/VOC2007/JPEGImages

Put all dataset annotations (VOC format) under ./data/VOCdevkit2007/VOC2007/Annotations

Put train.txt, val.txt, test.txt, and trainval.txt under ./data/VOCdevkit2007/VOC2007/ImageSets/Main

Download pretrained weight from [our pretrained model][2] (based on ResNet101) with access code **1596** or [office model][3].

### 4. Train
Modify ./lib/config/config.py:

'network' in Line10

If you want to train your own dataset, please modify:

./lib/config/config.py: 'dataset' in Line20, 'image_ext' in Line21, 'uplevel_len' in Line22

./lib/datasets/pascal_voc.py: 'CLASSES' in Line24

./lib/layer_utils/uplevel_utils.py: 'uplevelmap' in Line4

./lib/layer_utils/cls_weight.py: 'cls_weight_L2' in Line4

The default cache files in ./data/cache and ./data/VOCdevkit2007/annotations_cache is obtained from DOSR. 
If you want to train or test other datasets, please delete ./data/cache/HRSC2016/voc_2007_trainval_gt_roidb.pkl and ./data/VOCdevkit2007/annotations_cache/annots.pkl

Go to the root path and start to train:
```
$ python train.py
```

Note: At the beginning of the train, there may be some assert errors. The reason is there is an assert 'fg_num>0' in function 'iou_rotate_calculate3' of ./lib/utils/iou_rotate.py. At the beginning of the train, the network gets no foreground，thus, network cannot comput IoU between the predicted foreground box and its corresponding groundtruth bounding box. Such assert errors will **not** affect training process, you can ignore it. You can use our pretrained model to reduce such assert errors.
### 5. Demo
```
python demo.py
```

### 6. Eval
```
python eval_r.py
```

[1]:https://github.com/yaqihan-9898/DOSR
[2]: https://pan.baidu.com/s/1j-WRmj8da2yHsZP1odXfHg
[3]: https://github.com/tensorflow/models/tree/master/research/slim
[4]:https://ieeexplore.ieee.org/document/9591580/

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