What is the pair_id for each items ?

[lamhoangtung]

I'm trying to work with the dataset, let's say I have this annotation file

{
  'item2': {
    'segmentation': [
      [1, 2, 1, 17, 94, 58, 128, 2, 163, 2, 180, 86, 203, 173, 370, 149, 490, 81, 463, 1, 1, 2],
      [1, 2, 1, 17, 94, 58, 128, 2, 1, 2]
    ],
    'scale': 2,
    'viewpoint': 2,
    'zoom_in': 3,
    'landmarks': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 94, 58, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 180, 86, 2, 203, 173, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'style': 0,
    'bounding_box': [0, 0, 495, 179],
    'category_id': 1,
    'occlusion': 1,
    'category_name': 'short sleeve top'
  },
  'source': 'shop',
  'pair_id': 1811,
  'item1': {
    'segmentation': [
      [237, 160, 378, 153, 461, 92, 519, 214, 535, 348, 440, 428, 292, 420, 247, 309, 237, 160]
    ],
    'scale': 2,
    'viewpoint': 2,
    'zoom_in': 1,
    'landmarks': [237, 160, 1, 378, 153, 2, 461, 92, 1, 247, 309, 2, 292, 420, 2, 440, 428, 2, 535, 348, 2, 519, 214, 2],
    'style': 1,
    'bounding_box': [227, 86, 543, 455],
    'category_id': 9,
    'occlusion': 2,
    'category_name': 'skirt'
  }
}

Which pair_id will belong to which items? There are many cases when I saw 5,6 item in an image, it's really confusing to find the correct pair_id with the correct item.

Hope you can clarify this for me. Thanks

[geyuying]

Please note that we do not provide data in pairs. In training dataset, images are organized with continuous 'pair_id' including images from consumers and images from shops. (For example: 000001.jpg(pair_id:1; from consumer), 000002.jpg(pair_id:1; from shop),000003.jpg(pair_id:2; from consumer),000004.jpg(pair_id:2; from consumer),000005.jpg(pair_id:2; from consumer), 000006.jpg(pair_id:2; from consumer),000007.jpg(pair_id:2; from shop),000008.jpg(pair_id:2; from shop)...) A clothing item from shop images and a clothing item from consumer image are positive commercial-consumer pair if they have the same style number which is greater than 0 and they are from images with the same pair id, otherwise they are negative pairs. In this way, you can consruct training positive pairs and negative pairs in instance-level.

As is shown in the figure below, the first three images are from consumers and the last two images are from shops. These five images have the same 'pair_id'. Clothing items in orange bounding box have the same 'style':1. Clothing items in green bounding box have the same 'style': 2. 'Style' of other clothing items whose bouding boxes are not drawn in the figure is 0 and they can not construct positive commercial-consumer pairs. One positive commercial-consumer pair is the annotated short sleeve top in the first image and the annotated short sleeve top in the last image. Our dataset makes it possbile to construct instance-level pairs in a flexible way.
https://github.com/switchablenorms/DeepFashion2/blob/master/images/pair.jpg

Hope the above explanation will be helpful.

[geyuying]

'pair_id' and 'style' makes it convenient to construct instance-level pairs. 'pair_id' is an image-level label and 'style' is an instance-level label. All items in an image share the same 'pair_id'.

[lamhoangtung]

Thank you @geyuying for the clarification. If anyone wanna create item_id for each item, <pair_id>_<style> might be a good format :P