# FSODM

**Repository Path**: cgsdfc/FSODM

## Basic Information

- **Project Name**: FSODM
- **Description**: 小样本遥感目标检测经典项目
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-05-18
- **Last Updated**: 2024-05-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Few-shot YOLOv3: Few-shot Object Detection on Optical Remote Sensing Images


Our code is based on  [https://github.com/marvis/pytorch-yolo2](https://github.com/marvis/pytorch-yolo2) and developed with  Python 2.7 & PyTorch 0.3.1.

## Detection Examples

<div align=center>
<img src="https://user-images.githubusercontent.com/47288017/85243067-5051d380-b473-11ea-9a8e-17e19692746c.png" width="740">
</div>

<div align=center>
Samples of novel class detection result on NWPU VHR-10 and DIOR with 10-shot training bounding boxes.
</div> 

## Model
<div align=center>
<img src="https://user-images.githubusercontent.com/47288017/85242838-ab36fb00-b472-11ea-9ec8-418d06d057a0.png" width="740">
</div>


## Training our model on NWPU VHR-10

- ``` $PROJ_ROOT : project root ```
- ``` $DATA_ROOT : dataset root ```

### Prepare dataset
Get the NWPU VHR-10 data from https://1drv.ms/u/s!AmgKYzARBl5cczaUNysmiFRH4eE

Preprocess data
```
cd $PROJ_ROOT
python scripts/ImgSplit_nwpu.py $DATA_ROOT
```

Generate labels for NWPU VHR-10
```
python scripts/label_nwpu.py $DATA_ROOT
```

Generate per-class labels for NWPU VHR-10 (used for the Reweighting Module input)
```
python scripts/label_1c_nwpu.py $DATA_ROOT
```

Generate few-shot datasets   
Change the ''DROOT'' varibale in scripts/gen_fewlist_nwpu.py to $DATA_ROOT
```
python scripts/gen_fewlist_nwpu.py
```

Generate training dictionary
```
python scripts/gen_dict_file.py $DATA_ROOT nwpu
```

### Base Training
Modify Config for NWPU VHR-10 Data   
Change the cfg/fewyolov3_nwpu.data file 
```
metayolo = 1
metain_type = 2
data = nwpu
neg = 1
rand = 0
novel = data/nwpu_novels.txt
novelid = 0
learning_rate = 0.001
steps = -1,64000
scales = 1,0.1
meta = data/nwpu_traindict_full.txt
train = $DATA_ROOT/training.txt
valid = $DATA_ROOT/evaluation.txt
backup = backup/fewyolov3_nwpu
gpus = 0,1,2,3
```

Train the Model
```
python train.py cfg/fewyolov3_nwpu.data cfg/darknet_yolov3_spp.cfg cfg/reweighting_net.cfg
```

Evaluate the Model
```
python valid.py cfg/fewyolov3_nwpu.data cfg/darknet_yolov3_spp.cfg cfg/reweighting_net.cfg path/toweightfile
python scripts/voc_eval.py results/path/to/comp4_det_test_ cfg/metayolo.data
```

### Few-shot Tuning
Modify Config for NWPU VHR-10 Data   
Change the cfg/fewtunev3_nwpu_10shot.data file (change the shot number to try different shots)
```
metayolo = 1
metain_type = 2
data = nwpu
tuning = 1
neg = 0
rand = 0
novel = data/nwpu_novels.txt
novelid = 0
max_epoch = 2000
repeat = 200
dynamic = 0
scale = 1
learning_rate = 0.0001
steps = -1,64000
scales = 1,0.1
train = $DATA_ROOT/training.txt
meta = data/nwpu_traindict_bbox_10shot.txt
valid = $DATA_ROOT/evaluation.txt
backup = backup/fewtunetestv3_nwpu_10shot
gpus = 0,1,2,3
```


Train the Model with 10 shot
```
python train.py cfg/fewtunev3_nwpu_10shot.data cfg/darknet_yolov3_spp.cfg cfg/reweighting_net.cfg path/to/base/weightfile
```

Evaluate the Model
```
python valid.py cfg/fewtunev3_nwpu_10shot.data cfg/darknet_yolov3_spp.cfg cfg/reweighting_net.cfg path/to/tuned/weightfile
python scripts/voc_eval.py results/path/to/comp4_det_test_ cfg/fewtunev3_nwpu_10shot.data
```

## Acknowledgements
Large part of the code is borrowed from [YOLO-Low-Shot](https://github.com/bingykang/Fewshot_Detection)


## Citation

If you find this useful in your research, please consider citing:

  @article{deng2020few,
  title={Few-shot Object Detection on Remote Sensing Images},
  author={Deng, Jingyu and Li, Xiang and Fang, Yi},
  journal={arXiv preprint arXiv:2006.07826},
  year={2020}
}