Dataset preparation and description¶

DOTA Dataset¶

Download dataset¶

The DOTA dataset can be downloaded from DOTA or OpenDataLab.

We recommend using OpenDataLab to download the dataset, as the folder structure has already been arranged as needed and can be directly extracted without the need to adjust the folder structure.

Please unzip the file and place it in the following structure.

${DATA_ROOT}
├── train
│   ├── images
│   │   ├── P0000.png
│   │   ├── ...
│   ├── labelTxt-v1.0
│   │   ├── labelTxt
│   │   │   ├── P0000.txt
│   │   │   ├── ...
│   │   ├── trainset_reclabelTxt
│   │   │   ├── P0000.txt
│   │   │   ├── ...
├── val
│   ├── images
│   │   ├── P0003.png
│   │   ├── ...
│   ├── labelTxt-v1.0
│   │   ├── labelTxt
│   │   │   ├── P0003.txt
│   │   │   ├── ...
│   │   ├── valset_reclabelTxt
│   │   │   ├── P0003.txt
│   │   │   ├── ...
├── test
│   ├── images
│   │   ├── P0006.png
│   │   ├── ...

The folder ending with reclabelTxt stores the labels for the horizontal boxes and is not used when slicing.

Split DOTA dataset¶

Script tools/dataset_converters/dota/dota_split.py can split and prepare DOTA dataset.

python tools/dataset_converters/dota/dota_split.py \
    [--splt-config ${SPLIT_CONFIG}] \
    [--data-root ${DATA_ROOT}] \
    [--out-dir ${OUT_DIR}] \
    [--ann-subdir ${ANN_SUBDIR}] \
    [--phase ${DATASET_PHASE}] \
    [--nproc ${NPROC}] \
    [--save-ext ${SAVE_EXT}] \
    [--overwrite]

shapely is required, please install shapely first by pip install shapely.

Description of all parameters：

--split-config : The split config for image slicing.
--data-root: Root dir of DOTA dataset.
--out-dir: Output dir for split result.
--ann-subdir: The subdir name for annotation. Defaults to labelTxt-v1.0.
--phase: Phase of the data set to be prepared. Defaults to trainval test
--nproc: Number of processes. Defaults to 8.
--save-ext: Extension of the saved image. Defaults to png
--overwrite: Whether to allow overwrite if annotation folder exist.

Based on the configuration in the DOTA paper, we provide two commonly used split config.

./split_config/single_scale.json means single-scale split.
./split_config/multi_scale.json means multi-scale split.

DOTA dataset usually uses the trainval set for training and the test set for online evaluation, since most papers provide the results of online evaluation. If you want to evaluate the model performance locally firstly, please split the train set and val set.

Examples:

Split DOTA trainval set and test set with single scale.

python tools/dataset_converters/dota/dota_split.py
    --split-config 'tools/dataset_converters/dota/split_config/single_scale.json'
    --data-root ${DATA_ROOT} \
    --out-dir ${OUT_DIR}

If you want to split DOTA-v1.5 dataset, which have different annotation dir ‘labelTxt-v1.5’.

python tools/dataset_converters/dota/dota_split.py
    --split-config 'tools/dataset_converters/dota/split_config/single_scale.json'
    --data-root ${DATA_ROOT} \
    --out-dir ${OUT_DIR} \
    --ann-subdir 'labelTxt-v1.5'

If you want to split DOTA train and val set with single scale.

python tools/dataset_converters/dota/dota_split.py
    --split-config 'tools/dataset_converters/dota/split_config/single_scale.json'
    --data-root ${DATA_ROOT} \
    --phase train val \
    --out-dir ${OUT_DIR}

For multi scale split:

python tools/dataset_converters/dota/dota_split.py
    --split-config 'tools/dataset_converters/dota/split_config/multi_scale.json'
    --data-root ${DATA_ROOT} \
    --out-dir ${OUT_DIR}

The new data structure is as follows:

${OUT_DIR}
├── trainval
│   ├── images
│   │   ├── P0000__1024__0___0.png
│   │   ├── ...
│   ├── annfiles
│   │   ├── P0000__1024__0___0.txt
│   │   ├── ...
├── test
│   ├── images
│   │   ├── P0006__1024__0___0.png
│   │   ├── ...
│   ├── annfiles
│   │   ├── P0006__1024__0___0.txt
│   │   ├── ...

Then change data_root to ${OUT_DIR}.