| ## Environment Setup for data preparation and preprocessing |
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|
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
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| ## CDSI Dataset preparation |
| For using the VME dataset, both OBB (YOLO) and HBB (MS-COCO) formats are available with the desired splits. VME images and annotations are available at XXX. |
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| In order to build the CDSI dataset, you need to follow the steps below: |
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| 1. Download each dataset from its repository: |
| * xView (https://challenge.xviewdataset.org/) |
| * DOTA-v2.0 (https://captain-whu.github.io/DOTA/index.html) |
| * VEDAI (https://downloads.greyc.fr/vedai/) |
| * DIOR (https://gcheng-nwpu.github.io/) |
| * FAIR1M-2.0 (http://gaofen-challenge.com/benchmark) |
|
|
| 2. Convert each dataset to MS-COCO (JSON) format with original/ car-related + other small objects(car-other)/ car-related (car) setup, utilizing each dataset scripts under main directory: |
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| ### Instructions before starting: |
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| a. After installing some datasets, it's recommended to put all images under one directory and all annotations under one directory, to ease the MS-COCO conversion |
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| b. To generate SPLIT_FILE.txt for each setup (original|CDSI_other|CDSI), run this command under each dataset directory (example for car setup): |
|
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| ```bash |
| grep 'xview' CDSI_train.txt > xview_car_train.txt |
| |
| SPLIT_FILE == xview_car_train.txt |
| ``` |
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| ** For DOTA-v2.0: please check ``dota2`` directory |
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| ### Converting each dataset to COCO format |
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| Under each dataset directory, DATANAME_to_coco.py script is available to convert the specified dataset |
| ```bash |
| python DATANAME_to_coco.py IMG_DIR SPLIT_FILE.txt OUT_DIR MODE |
| ``` |
|
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| ### Combining train-val-test splits |
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| To combine the train-val-test splits from each dataset to construct CDSI splits, please use the following command/package. The command takes 3 arguments:File1.json File2.json OutputFile.json. T$ |
| ```bash |
| pyodi coco merge SPLIT_A.json SPLIT_B.json SPLIT_A_B.json |
| ``` |
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|
| >[!NOTE] |
| __The benchmark scripts will be released soon! Stay Tuned!__ |
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|
