Intro CALTECH 256 Greg Griffin, Alex Holub and Pietro Perona.
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Transcript of Intro CALTECH 256 Greg Griffin, Alex Holub and Pietro Perona.
Overview
• 256 Object Categories + Clutter
• At least 80 images per category
• 30608 images instead of 9144
• Smallest category size is 31 images:
• Too easy?
– left-right aligned
– Rotation artifacts
– Soon will saturate performance
Caltech-101: Drawbacks
€
N train ≤ 30
Caltech-256 : New Features
• Smallest category size now 80 images
• Harder
– Not left-right aligned
– No artifacts
– Performance is halved
– More categories
• New and larger clutter category
Collection Procedure• Similar to Caltech-101 (Li, Fergus, Perona)
• Four sorters rate the images1. good: a clear example2. bad: confusing, occluded, cluttered, or artistic3. not applicable: object category not present
• 92,652 Images from Google and Picsearch– 32.1% were rated good and kept
• Some images borrowed from 29 of the largest Caltech-101 categories (green)
Try to find: blimp, clutter, grasshopper, picnic-table, refrigerator, watermelon
Test for Antonio Torralba
blimp clutter
watermelon refrigerator
picnic-table
grasshopper
Localization?
Caltech-101/256 are not recommended for object localization tests
Acknowledgements• Rob Fergus and Fei Fei Li, Pierre Moreels for
code and procedures developed for the Caltech-101 image set
• Marco Ranzato and Claudio Fanti for miscellaneous help
• Sorters: Lis Fano, Nick Lo, Julie May, Weiyu Xu for making this image set possible with their hard work
Download:http://vision.caltech.edu/Image_Datasets/Caltech256