Caltech-256

Introduced by Griffin et al. in Caltech-256 object category dataset

Caltech-256 is an object recognition dataset containing 30,607 real-world images, of different sizes, spanning 257 classes (256 object classes and an additional clutter class). Each class is represented by at least 80 images. The dataset is a superset of the Caltech-101 dataset.

Source: Exploiting Non-Linear Redundancy for Neural Model Compression

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