Data source

Data from Hugging Face Datasets (detection-datasets), delivered clean.

2 datasets pulled from Hugging Face Datasets (detection-datasets)'s releases, checked field by field and shipped the way you want them — daily, weekly, or hourly, your call.

  • 2 datasets
  • 1 industry
  • Real rows on request

What Datadory delivers from Hugging Face Datasets (detection-datasets)

2
Apparel, Accessories & Luxury Goods Web-sourced fashion photography from six phot… · Fixed research corpus published at ECCV 2020

Fashionpedia — Detection Datasets on Hugging Face

Apparel, Accessories & Luxury Goods Zalando article imagery (company product cata… · Single snapshot released August 2017

Fashion-MNIST - Zalando Research Apparel Image Dataset

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Name any Hugging Face Datasets (detection-datasets) dataset and we send real rows from it — not a screenshot of rows. 1,744 datasets. Pick your catch.

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Straight answers about Hugging Face Datasets (detection-datasets) data

What does Datadory deliver from Hugging Face Datasets (detection-datasets)?

One Apparel, Accessories & Luxury Goods collection - the Fashionpedia mirror, 46,781 fashion photographs carrying 342,182 bounding boxes with segmentation masks over 46 garment-and-accessory categories - shipped with a verified eight-field dictionary, sample rows and a coverage statement attached.

How many images and annotations does Fashionpedia hold?

46,781 images in total, split 45,623 train and 1,158 validation, annotated with 342,182 bounding boxes - 333,401 in train and 8,781 in validation. Each box carries a category index, Pascal VOC coordinates and a measured mask area; the test split is empty.

Which categories can the labels name?

Forty-six values covering garments (shirt/blouse, sweater, cardigan, jacket, dress, jumpsuit), accessories (glasses, hat, tie, glove, watch, belt, bag/wallet, scarf, umbrella, shoe, sock) and garment parts (hood, collar, sleeve, pocket, neckline, zipper) down to trim details such as bead, sequin and tassel.

Does the corpus change over time?

No - the contents are a research corpus published at ECCV 2020 and held as a fixed conversion since. That permanence is the feature: accuracy recorded today remains comparable with numbers reported years ago, and any re-cut of the corpus happens on your schedule, not anyone else's.