Natural-Image Datasets

Natural-Image Datasets

• MNIST: handwritten digits: The most commonly used sanity check. Dataset of 25x25, centered, B&W handwritten digits. It is an easy task — just because something works on MNIST, doesn’t mean it works.
• CIFAR10 / CIFAR100: 32x32 color images with 10 / 100 categories. Not commonly used anymore, though once again, can be an interesting sanity check.
• Caltech 101: Pictures of objects belonging to 101 categories.
• Caltech 256: Pictures of objects belonging to 256 categories.
• STL-10 dataset: is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Like CIFAR-10 with some modifications.
• The Street View House Numbers (SVHN): House numbers from Google Street View. Think of this as recurrent MNIST in the wild.
• NORB: Binocular images of toy figurines under various illumination and pose.
• Pascal VOC: Generic image Segmentation / classification — not terribly useful for building real-world image annotation, but great for baselines
• Labelme: A large dataset of annotated images.
• ImageNet: The de-facto image dataset for new algorithms. Many image API companies have labels from their REST interfaces that are suspiciously close to the 1000 category; WordNet; hierarchy from ImageNet.
• LSUN: Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.) and an associated competition.
• MS COCO: Generic image understanding / captioning, with an associated competition.
• COIL 20: Different objects imaged at every angle in a 360 rotation.
• COIL100 : Different objects imaged at every angle in a 360 rotation.
• Google’s Open Images: A collection of 9 million URLs to images “that have been annotated with labels spanning over 6,000 categories” under Creative Commons.