This repository contains the code (in PyTorch) for the paper:
TOWARDS DOMAIN GENERALIZATION IN UNDERWATER OBJECT DETECTION Hong Liu, Pinhao Song, Runwei Ding
The code of this repository is based on PyTorch-YOLOv3
- Python 3.6
- PyTorch 1.0
- CUDA9.0 and cuDNN
- numpy
- tqdm
- tensorboardX
Download pretrained weights
$ cd weights/
$ bash download_weights.sh
Download Datasets
URPC2019: https://drive.google.com/open?id=1n8Rpgx3xF84HO6PXpfPrRtTMtVSuOaBs
Synthetic URPC2019: https://drive.google.com/open?id=1FzIuZJuCHna4Dn_FLBeR5IFztCJBJ6VD
checkpoint: https://drive.google.com/file/d/1n3e9R1zeJjOtNSMpNY0cVsfHqucmTOVe/view?usp=sharing
After downloading all datasets, create URPC2019 document.
$ cd data
$ mkdir URPC2019
It is recommended to symlink the dataset root to $DG-YOLO/data/URPC2019.
DG-YOLO
├── data
│ ├── URPC2019
│ │ ├── type1
│ │ ├── type2
│ │ ├── type3
│ │ ├── type4
│ │ ├── type5
│ │ ├── type7
│ │ ├── val_type1
│ │ ├── val_type2
│ │ ├── val_type3
│ │ ├── val_type4
│ │ ├── val_type5
│ │ ├── val_type6
│ │ ├── val_type7
│ │ ├── val_type8
│ │ ├── train2017
│ │ ├── val2017
$ python DG_train.py --pretrained_weights ./weights/darknet53.conv.74 --batch_size 8
Test in original validation set
$ python test.py --weights_path <path/to/checkpoints> --batch_size 32
Test in type8 validation set
$ python test.py --weights_path <path/to/checkpoints> --batch_size 32 --augment True
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