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Using JDet with FAIR

Data Preparing

Downloading FAIR at http://sw.chreos.org/challenge , and save to $FAIR_PATH$ as:

$FAIR_PATH$
├── train
|     ├──images
|     └──labelXmls
├── val
|     ├──images
|     └──labelXmls
└── test
      └──images

Data Preprocessing

Images in FAIR 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 FAIR is preprocessed by editing the configs/preprocess/fair_preprocess_config.py:

type='FAIR'
source_fair_dataset_path='/home/cxjyxx_me/workspace/JAD/datasets/FAIR/fair'
convert_tasks=['train','val','test']
source_dataset_path='/home/cxjyxx_me/workspace/JAD/datasets/FAIR/fair_DOTA'
target_dataset_path='/home/cxjyxx_me/workspace/JAD/datasets/FAIR/processed'

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

We need to set source_dataset_path to $FAIR_PATH$, and set target_dataset_path to $PROCESSED_FAIR_PATH$. 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/fair_preprocess_config.py

For the way of configuring the processed FAIR dataset in the model config file, please refer to $JDet_PATH$/configs/retinanet_r50v1d_fpn_fair.py:

dataset = dict(
    ...
)

Data Postprocessing

The Runner.test() in JDet will automatically merge results of each sub-images in the test set, and generates zip file of the output results in the submit_zips directory.