1. CALTECH 256 Greg Griffin, Alex Holub and Pietro Perona

2. Overview 256 Object Categories + Clutter At least 80 images per category 30608 images instead of 9144

3. Smallest category size is 31 images: Too easy? –left-right aligned –Rotation artifacts –Soon will saturate performance Caltech-101: Drawbacks

4. 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

5. 101 clutter256 clutter

6. Collection Procedure Similar to Caltech-101 (Li, Fergus, Perona) Four sorters rate the images 1.good: a clear example 2.bad: confusing, occluded, cluttered, or artistic 3.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)

9. Diminishing returns from Google Images

10. Try to find: blimp, clutter, grasshopper, picnic-table, refrigerator, watermelon Test for Antonio Torralba

11. blimpclutter watermelonrefrigerator picnic-table grasshopper Test for Antonio Torralba

12. blimpclutter watermelonrefrigerator picnic-table grasshopper Localization? Caltech-101/256 are not recommended for object localization tests

13. Benchmarks Expect roughly half the performance

14. Clutter: 827 Background Images Stephen Shore, Uncommon Places

15. 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