Fashionpedia is a new dataset which consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology.
Find out more information about Fashionpedia at below links:
CVDF hosts the images and annotations in the Fashionpedia dataset.
Detection: apparel object instance segmentation with localized attributes prediction:
Global attributes prediction:
We follow the annotation format of the COCO dataset with additonal fields, such as attributes. The annotations are stored in the JSON format and are organized as follows:
{
"info": info,
"categories": [category],
"attributes": [attribute],
"images": [image],
"annotations": [annotation],
"licenses": [license]
}
info{
"year" : int,
"version" : str,
"description" : str,
"contributor" : str,
"url" : str,
"date_created" : datetime,
}
category{
"id" : int,
"name" : str,
"supercategory" : str, # parent of this label
"level": int, # levels in the taxonomy
"taxonomy_id": string,
}
attribute{
"id" : int,
"name" : str,
"supercategory" : str, # parent of this label
"level": int, # levels in the taxonomy
"taxonomy_id": string,
}
image{
"id" : int,
"width" : int,
"height" : int,
"file_name" : str,
"license" : int,
"time_captured": string,
"original_url": string,
"isstatic": int, 0: the original_url is not a static url,
"kaggle_id": str,
}
annotation{
"id" : int,
"image_id" : int,
"category_id" : int,
"attribute_ids": [int],
"segmentation" : [polygon] or [rle]
"bbox" : [x,y,width,height], # int
"area" : int
"iscrowd": int (1 or 0)
}
polygon: [x1, y1, x2, y2, ...], where x, y are the coordinates of vertices, int
rle: {"size", (height, widht), "counts": str}
license{
"id" : int,
"name" : str,
"url" : str
}
{
"info": info,
"attributes": [attribute],
"images": [image],
"annotations": [annotation],
"licenses": [license]
}
annotation{
"image_id" : int,
"attribute_ids": [int],
}
# other fields follow the same format as detection task