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< Back to Alex Krizhevsky's home page

The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images
dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey
Hinton.


THE CIFAR-10 DATASET

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with
6000 images per class. There are 50000 training images and 10000 test images.

The dataset is divided into five training batches and one test batch, each with
10000 images. The test batch contains exactly 1000 randomly-selected images from
each class. The training batches contain the remaining images in random order,
but some training batches may contain more images from one class than another.
Between them, the training batches contain exactly 5000 images from each class.

Here are the classes in the dataset, as well as 10 random images from each:

airplane automobile bird cat deer dog frog horse ship truck


The classes are completely mutually exclusive. There is no overlap between
automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort.
"Truck" includes only big trucks. Neither includes pickup trucks.


DOWNLOAD

If you're going to use this dataset, please cite the tech report at the bottom
of this page.


Version Size md5sum CIFAR-10 python version 163 MB
c58f30108f718f92721af3b95e74349a CIFAR-10 Matlab version 175 MB
70270af85842c9e89bb428ec9976c926 CIFAR-10 binary version (suitable for C
programs) 162 MB c32a1d4ab5d03f1284b67883e8d87530


BASELINE RESULTS

You can find some baseline replicable results on this dataset on the project
page for cuda-convnet. These results were obtained with a convolutional neural
network. Briefly, they are 18% test error without data augmentation and 11%
with. Additionally, Jasper Snoek has a new paper in which he used Bayesian
hyperparameter optimization to find nice settings of the weight decay and other
hyperparameters, which allowed him to obtain a test error rate of 15% (without
data augmentation) using the architecture of the net that got 18%.


OTHER RESULTS

Rodrigo Benenson has been kind enough to collect results on CIFAR-10/100 and
other datasets on his website; click here to view.


DATASET LAYOUT

PYTHON / MATLAB VERSIONS

I will describe the layout of the Python version of the dataset. The layout of
the Matlab version is identical.

The archive contains the files data_batch_1, data_batch_2, ..., data_batch_5, as
well as test_batch. Each of these files is a Python "pickled" object produced
with cPickle. Here is a python2 routine which will open such a file and return a
dictionary:

def unpickle(file):
    import cPickle
    with open(file, 'rb') as fo:
        dict = cPickle.load(fo)
    return dict


And a python3 version:

def unpickle(file):
    import pickle
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict


Loaded in this way, each of the batch files contains a dictionary with the
following elements:
 * data -- a 10000x3072 numpy array of uint8s. Each row of the array stores a
   32x32 colour image. The first 1024 entries contain the red channel values,
   the next 1024 the green, and the final 1024 the blue. The image is stored in
   row-major order, so that the first 32 entries of the array are the red
   channel values of the first row of the image.
 * labels -- a list of 10000 numbers in the range 0-9. The number at index i
   indicates the label of the ith image in the array data.


The dataset contains another file, called batches.meta. It too contains a Python
dictionary object. It has the following entries:
 * label_names -- a 10-element list which gives meaningful names to the numeric
   labels in the labels array described above. For example, label_names[0] ==
   "airplane", label_names[1] == "automobile", etc.

BINARY VERSION

The binary version contains the files data_batch_1.bin, data_batch_2.bin, ...,
data_batch_5.bin, as well as test_batch.bin. Each of these files is formatted as
follows:

<1 x label><3072 x pixel>
...
<1 x label><3072 x pixel>

In other words, the first byte is the label of the first image, which is a
number in the range 0-9. The next 3072 bytes are the values of the pixels of the
image. The first 1024 bytes are the red channel values, the next 1024 the green,
and the final 1024 the blue. The values are stored in row-major order, so the
first 32 bytes are the red channel values of the first row of the image.

Each file contains 10000 such 3073-byte "rows" of images, although there is
nothing delimiting the rows. Therefore each file should be exactly 30730000
bytes long.

There is another file, called batches.meta.txt. This is an ASCII file that maps
numeric labels in the range 0-9 to meaningful class names. It is merely a list
of the 10 class names, one per row. The class name on row i corresponds to
numeric label i.


THE CIFAR-100 DATASET

This dataset is just like the CIFAR-10, except it has 100 classes containing 600
images each. There are 500 training images and 100 testing images per class. The
100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes
with a "fine" label (the class to which it belongs) and a "coarse" label (the
superclass to which it belongs).
Here is the list of classes in the CIFAR-100:



Superclass 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


Yes, I know mushrooms aren't really fruit or vegetables and bears aren't really
carnivores.



DOWNLOAD

Version Size md5sum CIFAR-100 python version 161 MB
eb9058c3a382ffc7106e4002c42a8d85 CIFAR-100 Matlab version 175 MB
6a4bfa1dcd5c9453dda6bb54194911f4 CIFAR-100 binary version (suitable for C
programs) 161 MB 03b5dce01913d631647c71ecec9e9cb8


DATASET LAYOUT

PYTHON / MATLAB VERSIONS

The python and Matlab versions are identical in layout to the CIFAR-10, so I
won't waste space describing them here.

BINARY VERSION

The binary version of the CIFAR-100 is just like the binary version of the
CIFAR-10, except that each image has two label bytes (coarse and fine) and 3072
pixel bytes, so the binary files look like this:

<1 x coarse label><1 x fine label><3072 x pixel>
...
<1 x coarse label><1 x fine label><3072 x pixel>


INDICES INTO THE ORIGINAL 80 MILLION TINY IMAGES DATASET

Sivan Sabato was kind enough to provide this file, which maps CIFAR-100 images
to images in the 80 million tiny images dataset. Sivan Writes:

The file has 60000 rows, each row contains a single index into the tiny db,
where the first image in the tiny db is indexed "1". "0" stands for an image that is not from the tiny db.
The first 50000 lines correspond to the training set, and the last 10000 lines correspond
to the test set.



REFERENCE

This tech report (Chapter 3) describes the dataset and the methodology followed
when collecting it in much greater detail. Please cite it if you intend to use
this dataset.

 * Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009.