Photos and artwork images with object annotations for academic use only
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002.american-flag
004.baseball-bat
010.beer-mug
016.boom-box
024.butterfly
028.camel
050.covered-wagon
052.crab-101
053.desk-globe
062.eiffel-tower
064.elephant-101
078.fried-egg
081.frying-pan
084.giraffe
087.goldfish
095.hamburger
101.head-phones
105.horse
107.hot-air-balloon
110.hourglass
112.human-skeleton
115.ice-cream-cone
123.ketch-101
127.laptop-101
131.lightbulb
136.mandolin
140.menorah
146.mountain-bike
154.palm-tree
158.penguin
159.people
167.pyramid
171.refrigerator
174.rotary-phone
201.starfish-101
204.sunflower
205.superman
207.swan
212.teapot
213.teddy-bear
214.teepee
225.tower-pisa
235.umbrella
239.washing-machine Import dataset Oct 25, 2016
240.watch
245.windmill
246.wine-bottle
250.zebra
252.car-side-101
253.faces-easy-101
gt_bb
README.md
citation.bib

README.md

#Photo-Art-50 dataset

The Photo-Art-50 dataset is a dataset of images from photos and artwork, with ground truth bounding boxes for 50 object classes. The aim is to evaluate cross-depiction object detection performance.

The dataset was originally produced by Qi Wu and Hongping Cai while working under Peter Hall at the University of Bath.

Please also see the People-Art dataset.

Description

This dataset contains 50 classes of object. There are 90 to 138 images for each class, approximately half of which are photos and the other half art. The 50 classes all appear in Caltech-256. Some of the photos are from Caltech-256; the rest are from Google searches.

The artwork images came from searching using a variety of keywords to cover a wide gamut of depiction styles, e.g. "horse cartoon", "horse drawing", "horse painting", "horse sketches", "horse kid drawing", etc. All selected images have a reasonable size of a meaningful object area and there are ground-truth bounding boxes, labelled by hand, for each object.

Files included

  • {cls_id}.{cls_name}/{cls_id}.a_{img_id}.jpg: Art images for each class
  • {cls_id}.{cls_name}/{cls_id}.p_{img_id}.jpg: Photo images for each class
  • gt_bb/{cls_id}.txt: Ground truth file for each class

The ground truth files contain bounding boxes for each object instance as a row in ccv format: image_name x0 y0 w h NB image_name might appear more than once, which means there are multiple object instances in the image.

Copyright

Many of the images are subject to copyright. These are provided only for "data mining for non-commercial research" or other "fair dealing" (UK Guidance).

Publications

The dataset appears in the following publications:

If you wish to cite the dataset, please use the following citation.