is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Like CIFAR-10 with some modifications.
Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.) and an associated competition.
natural-imageThe 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...
natural-imageGeneric image Segmentation / classificationnot terribly useful for building real-world image annotation, but great for baselines
natural-imageHouse numbers from Google Street View. Think of this as recurrent MNIST in the wild.
natural-imageA collection of 9 million URLs to images that have been annotated with labels spanning over 6,000 categories under Creative Commons.
natural-image32x32 color images with 10 / 100 categories. Not commonly used anymore, though once again, can be an interesting sanity check.
natural-imageVector data for the entire planet under a free license. It contains (an older version of) the US Census Bureaus data.
natural-image, geospatialMNIST: handwritten digits: The most commonly used sanity check. Dataset of 25x25, centered, B&W; handwritten digits. It is an easy taskjust because some...
natural-imagePictures of objects belonging to 256 categoriesPictures of objects belonging to 256 categories.
natural-image, classificationThe 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 ...
object, natural-image, centered, scene, image classificationSatellite shots of the entire Earth surface, updated every several weeks.
natural-image, geospatial