Satellite shots of the entire Earth surface, updated every several weeks.
Doppler radar scans of atmospheric conditions in the US.
Vector data for the entire planet under a free license. It contains (an older version of) the US Census Bureaus data.
Different objects imaged at every angle in a 360 rotation.
COIL100 : Different objects imaged at every angle in a 360 rotation.
House numbers from Google Street View. Think of this as recurrent MNIST in the wild.
Binocular images of toy figurines under various illumination and pose.
Generic image understanding / captioning, with an associated competition.
A collection of 9 million URLs to images that have been annotated with labels spanning over 6,000 categories under Creative Commons.
32x32 color images with 10 / 100 categories. Not commonly used anymore, though once again, can be an interesting sanity check.
is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Like CIFAR-10 with some mo...
MNIST: handwritten digits: The most commonly used sanity check. Dataset of 25x25, centered, B&W; handwritten digits. It is an easy taskjust because some...
Pictures of objects belonging to 256 categoriesPictures of objects belonging to 256 categories.
The CALTECH 101 dataset by Li Fei-Fei contains images for 101 categories with about 40 to 800 images per category. Most categories have about 50 images ...
A large dataset of annotated images.
Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.) and an associated competition.
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 c...
Generic image Segmentation / classificationnot terribly useful for building real-world image annotation, but great for baselines