download_norb_small: Download Small NORB

In jlmelville/snedata: SNE Simulation Dataset Functions

Download Small NORB

Description

Download Small NORB database of images of toys.

Usage

download_norb_small(
  base_url = "https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/",
  verbose = FALSE
)

Arguments

base_url	Base URL that the files are located at.
verbose	If TRUE, then download progress will be logged as a message.

Format

A data frame with 18,439 variables:

c0px1, c0px2, c0px3 ... c0px9216

Integer pixel value, from 0 (white) to 255 (black) for the first image in the pair

c1px1, c1px2, c1px3 ... c1px9216

Integer pixel value, from 0 (white) to 255 (black) for the second image in the pair

Instance

The index of the toy in a particular category, represented by a factor in the range 0-9. The training set consists of instances 0, 1, 2, 3 and 5, and the test set consists of 4, 6, 7, 8 and 9.

Elevation

The elevation of the camera represented by a factor in the range 0-8. These represent elevations of 30 to 70 degrees from the horizontal, in increments of 5 degrees.

Azimuth

The azimuth, represented by a factor in the range 0, 2, 4 .. 34. Multiply by ten to get the value in degrees.

Lighting

The lighting condition, represened by a factor in the range 0-5.

Label

The toy category, represented by a factor in the range 0-4.

Split

Whether the toy in is in the training or testing set, represented by a factor

Description

The name of the toy category associated with Label, represented by a factor.

The pixel features are organized row-wise from the top left of each image. The Label levels correspond to:

0

Four-legged animal

1

Human figure

2

Airplane

3

Truck

4

Car

There are 48,600 items in the data set. The first 24,300 are the training set, and the remaining 24,300 are the testing set, but you can also use the Split column to determine which split a given row is in.

Items in the dataset can be visualized with the show_norb_object function.

For more information see https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/.

Details

Downloads the image and label files for the training and test datasets and converts them to a data frame.

The Small NORB dataset contains images of 50 toys. The toys are divided into five categories (animal, human, airplane, truck, car) with ten examples per category. Each object was then images under 6 different lighting conditions, 9 elevations and 18 different azimuths, so there are 972 images per toy. The process was then repeated with a different camera, so there are actually 972 * 2 = 1944 images per toy. This dataset stores each pair of images for a given toy, lighting, elevation and azimuth as a single row. Each image is 96 * 96 pixels, so the first 9,216 columns contain the pixels of the first image, and the second 9,216 (9217:18432) columns contain the pixels of the second image. The other information (lighting and so on) are also stored as factors.

Value

Data frame containing Small NORB dataset.

References

The Small NORB Dataset, v1.0 https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/

LeCun, Y., Huang, F. J., & Bottou, L. (2004, June). Learning methods for generic object recognition with invariance to pose and lighting. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2004 (pp. 97-104). IEEE. http://doi.ieeecomputersociety.org/10.1109/CVPR.2004.144

Examples

## Not run: 
# download the data set
norb <- download_norb_small(verbose = TRUE)

# first 24,300 instances are the training set
norb_train <- head(norb, 24300)
# the remaining 24,300 are the test set
norb_test <- tail(norb, 24300)

# Or equivalently
norb_train <- norb[norb$Split == "training", ]
norb_test <- norb[norb$Split == "testing", ]

identical(norb_train, norb_train2) # TRUE
identical(norb_test, norb_test2) # also TRUE

# PCA on 1000 examples
norb_r1000 <- norb[sample(nrow(norb), 1000), ]
pca <- prcomp(norb_r1000[, 1:(96 * 96 * 2)], retx = TRUE, rank. = 2)
# plot the scores of the first two components
plot(pca$x[, 1:2], type = "n")
text(pca$x[, 1:2],
  labels = norb_r1000$Label,
  col = rainbow(length(levels(norb$Label)))[norb_r1000$Label]
)

## End(Not run)

jlmelville/snedata documentation built on Jan. 13, 2024, 2:06 a.m.