Download and convert MIDV-500 annotations to COCO instance segmentation format
Download and convert MIDV-500 datasets into COCO instance segmentation format
Then, dataset can be directly used in the training of Yolact, Detectron type of models.
MIDV-500 consists of 500 video clips for 50 different identity document types including 17 ID cards, 14 passports, 13 driving licences and 6 other identity documents of different countries with ground truth which allows to perform research in a wide scope of various document analysis problems. Additionally, MIDV-2019 dataset contains distorted and low light images in it.
You can find more detail on papers:
pip install midv500
- Import package:
- Download and unzip desired version of the dataset:
# set directory for dataset to be downloaded dataset_dir = 'midv500_data/' # download and unzip the base midv500 dataset dataset_name = "midv500" midv500.download_dataset(dataset_dir, dataset_name) # or download and unzip the midv2019 dataset that includes low light images dataset_name = "midv2019" midv500.download_dataset(dataset_dir, dataset_name) # or download and unzip both midv500 and midv2019 datasets dataset_name = "all" midv500.download_dataset(dataset_dir, dataset_name)
- Convert downloaded dataset to coco format:
# set directory for coco annotations to be saved export_dir = 'midv500_data/' # set the desired name of the coco file, coco file will be exported as "filename + '_coco.json'" filename = 'midv500' # convert midv500 annotations to coco format midv500.convert_to_coco(dataset_dir, export_dir, filename)
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|Filename, size midv500-0.2.1-py3-none-any.whl (9.6 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
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