Posts Tagged ‘Natural Images’

CIFAR-10 & CIFAR-100:

These two datasets are labeled images from a parent dataset called Tiny Images Dataset (which is available on http://horatio.cs.nyu.edu/mit/tiny/data/index.html).

CIFAR-10:

• Number of images in the dataset: 60,000 (50,000 images for training divided into 5 batches and 10,000 images for test in one batch)

• Image size: 32×32

• Number of classes: 10 (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)

3 different versions are available for this dataset each suitable for either Python, Matlab or C programming, and they can be downloaded from https://www.cs.toronto.edu/~kriz/cifar.html.

If you use this dataset, make sure to cite the following tech report.

Alex Krizhevsky, Learning multiple layers of features from tiny images, 2009.

CIFAR-100:

• Number of images in the dataset: 60,000 (50,000 images for training while 500 images belong to each class and 10,000 images for test while 100 images belong to each class for test)

• Image size: 32×32

• Number of classes: 100 , provided in detail in bellow (copied from http://www.cs.toronto.edu/~kriz/cifar.html)

The 100 classes belong to 20 Superclasses that determines the “coarse” label and the “fine” label refers to the class that image belongs to.

Superclass and Classes:

aquatic mammals: beaver, dolphin, otter, seal, whale

fish: aquarium fish, flatfish, ray, shark, trout

flowers: orchids, poppies, roses, sunflowers, tulips

food containers: bottles, bowls, cans, cups, plates

fruit and vegetables: apples, mushrooms, oranges, pears, sweet peppers

household electrical devices: clock, computer keyboard, lamp, telephone, television

household furniture: bed, chair, couch, table, wardrobe

insects: bee, beetle, butterfly, caterpillar, cockroach

large carnivores: bear, leopard, lion, tiger, wolf

large man-made outdoor things: bridge, castle, house, road, skyscraper

large natural outdoor scenes: cloud, forest, mountain, plain, sea

large omnivores and herbivores: camel, cattle, chimpanzee, elephant, kangaroo

medium-sized mammals: fox, porcupine, possum, raccoon, skunk

non-insect invertebrates: crab, lobster, snail, spider, worm

people: baby, boy, girl, man, woman

reptiles: crocodile, dinosaur, lizard, snake, turtle

small mammals: hamster, mouse, rabbit, shrew, squirrel

trees: maple, oak, palm, pine, willow

vehicles 1: bicycle, bus, motorcycle, pickup truck, train

vehicles 2: lawn-mower, rocket, streetcar, tank, tractor

3 different versions are available for this dataset each suitable for either Python, Matlab or C programming, and they can be downloaded from https://www.cs.toronto.edu/~kriz/cifar.html.

If you use this dataset, make sure to cite the following tech report.

Alex Krizhevsky, Learning multiple layers of features from tiny images, 2009.

Keywords: Vision, Image, Classification, Natural Images

ImageNet:

This dataset contains images which are organized according to the WordNet hierarchy (WorldNet 3.0) in which every node refers to up to thousands of images. Each concept in WorldNet is described by synonym sets (synsets) which are words and phrases. ImageNet aims to have 1000 images per synset on average.

Because the images are subject to copyright and ImageNet does not own the images, only thumbnails and URLs of the images are available by ImageNet. The original image dataset can only be provided to students and researchers under certain conditions. Also image data is available for download (only for educational purposes) through Visual Recognition Challenges by registration.

The following information is from http://image-net.org/about-stats

• Number of non-empty synsets: 21841

• Number of images: 14,197,122

• Number of images with bounding box annotations: 1,034,908

• Number of synsets with SIFT features: 1000

• Number of images with SIFT features: 1.2 million

• Number of categories: 22,000 with 500-1000 images per category

The ImageNet URLs can be downloaded from the following link:

http://image-net.org/download-imageurls

To access the WorldNet hierarchy and the WorldNet documentation, please refer to the following link:

http://image-net.org/download-API

The complete information about the dataset and contact link for download can be found at: http://image-net.org/about-overview and http://image-net.org/

Find information about the ImageNet Object Localization Challenge available on Kaggle:

https://www.kaggle.com/c/imagenet-object-localization-challenge

keywords: Vision, Image, Classification, Natural Images