Data Preparation
July 26, 2022 ยท View on GitHub
We support 4 popular settings in DAOD as listed below:
| Source | Target | Test | |
|---|---|---|---|
| normal to foggy (C2F) | cityscapes (train) | cityscapes-foggy (train) | cityscapes-foggy (val) |
| small to large (C2B) | cityscapes (train) | BDD100K (train) | BDD100K (val) |
| across cameras (K2C) | KITTI (train-car) | cityscapes (train-car) | cityscapes (val-car) |
| synthetic to real (S2C) | Sim10K (train-car) | cityscapes (train-car) | cityscapes (val-car) |
All datasets are aranged in the format of PASCAL VOC as follows:
# cityscapes
- VOC2007_city
- ImageSets
- JPEGImages
- Annotations
Here are datasets used in this paper.
SPLITS = [
("VOC2007_citytrain", 'data/VOC2007_citytrain', "train", 8),
("VOC2007_foggytrain", 'data/VOC2007_foggytrain', "train", 8),
("VOC2007_foggyval", 'data/VOC2007_foggyval', "val", 8),
("VOC2007_citytrain1", 'data/VOC2007_citytrain1', "train", 1),
("VOC2007_cityval1", 'data/VOC2007_cityval1', "val", 1),
("VOC2007_bddtrain", 'data/VOC2007_bddtrain', "train", 8),
("VOC2007_bddval", 'data/VOC2007_bddval', "val", 8),
("VOC2007_kitti1", 'data/kitti', "train", 1),
("VOC2007_sim1", 'data/sim', "train", 1),
]
CitysScape and FoggyCityscape
- register and download from CitysScape to
data/ - transform segmentation annotations to detection formats
- For multi classes,
python tools/trans_seg_to_det_multi.py - For single class,
python tools/trans_seg_to_det_multi.py
- For multi classes,
- check the annotations and make txt,
python tools/make_VOC_txt.py