Datasets

NORB: the NYU Object Recognition Benchmark

Click here for more details and for downloading the dataset.

This dataset is intended for experiments in 3D generic object recognition independently of the pose, illumination, and backgroun clutter.

It contains thousands of images of 50 toys belonging to 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. The objects were imaged by two cameras under 6 lighting conditions, 9 elevations (30 to 70 degrees every 5 degrees), and 18 azimuths (0 to 340 every 20 degrees).

The dataset is composed of 12 sets of 29,160 images (10 sets for training, 2 sets for testing). Each set is generated by randomly perturbing the size, in-plane rotation, position, contrast, and brightness of original object images and placing them on random backgrounds.

The training sets contain 5 instances of each category (instances 4, 6, 7, 8 and 9), and the test sets contain the remaining 5 instances (instances 0, 1, 2, 3, and 5).

The MNIST Handwritten Digit Dataset

Click here for details, and to download the MNIST dataset.

The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.

It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.