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Using JDet with DOTA

Data Preparing

Downloading DOTA at https://captain-whu.github.io/DOTA/ , and save to $DOTA_PATH$ as:

$DOTA_PATH$
├── train
|     ├──images
|     └──labelTxt
├── val
|     ├──images
|     └──labelTxt
└── test
      └──images

Data Preprocessing

Images in DOTA is relatively big, we need to crop each image into several sub-images before training and testing.

cd $JDet_PATH$

We can set how the DOTA is preprocessed by editing the configs/preprocess/dota_preprocess_config.py:

type='DOTA'
source_dataset_path='/mnt/disk/cxjyxx_me/JAD/datasets/test/DOTA/'
target_dataset_path='/mnt/disk/cxjyxx_me/JAD/datasets/test/processed_DOTA/'

# available labels: train, val, test, trainval
tasks=[
    dict(
        label='trainval',
        config=dict(
            subimage_size=600,
            overlap_size=150,
            multi_scale=[1., 1.5],
            horizontal_flip=False,
            vertical_flip=False,
            rotation_angles=[0.] 
        )
    ),
    dict(
        label='test',
        config=dict(
            subimage_size=600,
            overlap_size=150,
            multi_scale=[1.],
            horizontal_flip=False,
            vertical_flip=False,
            rotation_angles=[0.] 
        )
    )
]

The type means the type of dataset which cloud be selected in ['DOTA', 'DOTA1_5', 'DOTA2', 'FAIR']. We need to set source_dataset_path to $DOTA_PATH$, and set target_dataset_path to $PROCESSED_DOTA_PATH$. Then we can set the cropping paramters through subimage_size and overlap_size, and set multi_scale for multi scale training or testing, the tool will first resize the origin image by different scale fators, and cropping each scaled image by subimage_size and overlap_size. Finally, run the following script for preprocessing：

python tools/preprocess.py --config-file configs/preprocess/dota_preprocess_config.py

For the way of configuring the processed DOTA dataset in the model config file, please refer to $JDet_PATH$/configs/retinanet_r50v1d_fpn_dota.py and $JDet_PATH$/configs/retinanet_r50v1d_fpn_dota1_5.py:

dataset = dict(
    ...
)

Data Postprocessing

The Runner.test() in JDet will automatically merge results of each sub-images in the test set, and generates submitable zip file in the submit_zips directory. We can directly submit this file to https://captain-whu.github.io/DOTA/ .