I am using a dictionary for my dataset, here is two of the items:
metaitem {'imname': 'voc_dataset\\VOCdevkit\\VOC2007\\JPEGImages\\000003.jpg', 'anno_id': 2, 'impath': 'voc_dataset\\VOCdevkit\\VOC2007\\JPEGImages\\000003.jpg', 'xml_parsed': {'annotation': {'folder': 'VOC2007', 'filename': '000003.jpg', 'source': {'database': 'The VOC2007 Database', 'annotation': 'PASCAL VOC2007', 'image': 'flickr', 'flickrid': '138563409'}, 'owner': {'flickrid': 'RandomEvent101', 'name': '?'}, 'size': {'width': '500', 'height': '375', 'depth': '3'}, 'segmented': '0', 'object': [{'name': 'sofa', 'pose': 'Unspecified', 'truncated': '0', 'difficult': '0', 'bndbox': {'xmin': '123', 'ymin': '155', 'xmax': '215', 'ymax': '195'}}, {'name': 'chair', 'pose': 'Left', 'truncated': '0', 'difficult': '0', 'bndbox': {'xmin': '239', 'ymin': '156', 'xmax': '307', 'ymax': '205'}}]}}}
(375, 500, 3)
metaitem {'imname': 'voc_dataset\\VOCdevkit\\VOC2007\\JPEGImages\\000002.jpg', 'anno_id': 1, 'impath': 'voc_dataset\\VOCdevkit\\VOC2007\\JPEGImages\\000002.jpg', 'xml_parsed': {'annotation': {'folder': 'VOC2007', 'filename': '000002.jpg', 'source': {'database': 'The VOC2007 Database', 'annotation': 'PASCAL VOC2007', 'image': 'flickr', 'flickrid': '329145082'}, 'owner': {'flickrid': 'hiromori2', 'name': 'Hiroyuki Mori'}, 'size': {'width': '335', 'height': '500', 'depth': '3'}, 'segmented': '0', 'object': [{'name': 'train', 'pose': 'Unspecified', 'truncated': '0', 'difficult': '0', 'bndbox': {'xmin': '139', 'ymin': '200', 'xmax': '207', 'ymax': '301'}}]}}}
(500, 335, 3)
I am looping through the dataset items like this:
for imname, metaitem in metadata_test.items():
but this approach doesn’t take the ‘name’ key or label which is what I am trying to predict for training/eval. I believe I would need to convert these names to indices too.
Any idea how to extract the label from this dictionary to be use in the for loop above? I am sorry to ask its my first time dealing with dictionaries.