pradiptapradhanang/fashionpedia

Fashionpedia Dataset

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:

• Fashionpedia python API for reading, visualizing annotations, and result evaluation
• Fashionpedia project page

Download

CVDF hosts the images and annotations in the Fashionpedia dataset.

Images

• Training images
• Validation and test images

Annotations

Detection: apparel object instance segmentation with localized attributes prediction:

• instances_attributes_train2020
• instances_attributes_val2020
• test_images_info2020

Global attributes prediction:

• attributes_train2020
• attributes_val2020
• test_images_info2020: same as detection task

Annotation format

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:

Detection task (instances_attributes)

{
 "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
}

Global attribute prediction task (attributes)

{
 "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