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Data Preparation

1. SSOD: Semi-Supervised Object Detection

We support 5 popular settings in SSOD research as listed below：

Labeled Data Unlabeled Data Test Data
COCO2017-train-1% COCO2017-train-99% COCO2017-test
COCO2017-train-5% COCO2017-train-95% COCO2017-test
COCO2017-train-10% COCO2017-train-90% COCO2017-test
COCO2017-train COCO2017-unlabeled COCO2017-test
VOC07-trainval VOC12-trainval VOC07-test

  1. Download VOC and COCO from the website and organize them as follows:

    # ====coco====                      |    # ====voc====
/data/coco/                         |    /data/voc/
  - images                          |     - 12
    - train2017                     |       - VOCdevkit
    - unlabeled2017                 |         - VOC2012
	   - ...                           |                
  - annotations                     |     - 07
	   - instances_train2017.json      |       - VOCdevkit
	   - image_info_unlabeled2017.json |         - VOC2007
	   - ...						   |

  2. Run scripts to create the soft symlink：

    # * please change the "prefix_coco", "prefix_coco_ul", "prefix_voc" in the scripts to fit your environment.
# * you can also create symlink by yourself.
cd tools/datasets
xonsh create_dataset_link.sh

  3. Create coco-standard, coco-additional, voc (it will cost several minutes):

    cd tools/datasets
xonsh preprocess_dataset.sh

2. DAOD: Domain Adaptive Object Detection

We support 4 popular settings in DAOD research as listed below:

Labeled Data Unlabeled Data Test Data
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) cityscapes (train) cityscapes (val)
synthetic$\to$real (S2C) Sim10K cityscapes (train) cityscapes (val)

  1. Download cityscapes, cityscapes-foggy, KITTI, Sim10K and BDD100K from the website and organize them as follows:

    # cityscapes          |    # cityscapes-foggy      |   # BDD
/data/city            |    /data/foggycity         |   /data/BDD
  - VOC2007_citytrain |      - VOC2007_foggytrain  |     - VOC2007_bddtrain
    - ImageSets       |        - ImageSets         |       - ImageSets
    - JPEGImages      |        - JPEGImages        |       - JPEGImages
    - Annotations     |        - Annotations       |       - Annotations 
  - VOC2007_cityval   |      - VOC2007_foggyval    |     - VOC2007_bddval 
    - ImageSets       |        - ImageSets         |       - ImageSets
    - JPEGImages      |        - JPEGImages        |       - JPEGImages
    - Annotations     |        - Annotations       |       - Annotations 
# =========================================================================
# KITTI               |   # Sim10K
/data/kitti           |   /data/sim
   - ImageSets        |     - ImageSets
   - JPEGImages       |     - JPEGImages
   - Annotations      |     - Annotations

    PS: please refer to ProbabilisticTeacher for the detailed dataset pre-processing.

  2. Run scripts to create the soft symlink：

    cd tools/datasets_uda
xonsh create_dataset_link.sh

  3. Convert to coco format:

    cd tools/datasets_uda
xonsh preprocess_dataset.sh