vision.datasets

Gluon provides pre-defined vision datasets functions in the mxnet.gluon.data.vision.datasets module.

Dataset container.

Classes

MNIST([root, train, transform])

MNIST handwritten digits dataset from http://yann.lecun.com/exdb/mnist

FashionMNIST([root, train, transform])

A dataset of Zalando’s article images consisting of fashion products,

CIFAR10([root, train, transform])

CIFAR10 image classification dataset from https://www.cs.toronto.edu/~kriz/cifar.html

CIFAR100([root, fine_label, train, transform])

CIFAR100 image classification dataset from https://www.cs.toronto.edu/~kriz/cifar.html

ImageRecordDataset(filename[, flag, transform])

A dataset wrapping over a RecordIO file containing images.

ImageFolderDataset(root[, flag, transform])

A dataset for loading image files stored in a folder structure.

class mxnet.gluon.data.vision.datasets.MNIST(root='/home/jenkins_slave/.mxnet/datasets/mnist', train=True, transform=None)[source]

Bases: mxnet.gluon.data.dataset._DownloadedDataset

MNIST handwritten digits dataset from http://yann.lecun.com/exdb/mnist

Each sample is an image (in 3D NDArray) with shape (28, 28, 1).

Parameters
  • root (str, default $MXNET_HOME/datasets/mnist) – Path to temp folder for storing data.

  • train (bool, default True) – Whether to load the training or testing set.

  • transform (function, default None) –

    A user defined callback that transforms each sample. For example:

    transform=lambda data, label: (data.astype(np.float32)/255, label)
    

class mxnet.gluon.data.vision.datasets.FashionMNIST(root='/home/jenkins_slave/.mxnet/datasets/fashion-mnist', train=True, transform=None)[source]

Bases: mxnet.gluon.data.vision.datasets.MNIST

A dataset of Zalando’s article images consisting of fashion products, a drop-in replacement of the original MNIST dataset from https://github.com/zalandoresearch/fashion-mnist

Each sample is an image (in 3D NDArray) with shape (28, 28, 1).

Parameters
  • root (str, default $MXNET_HOME/datasets/fashion-mnist') – Path to temp folder for storing data.

  • train (bool, default True) – Whether to load the training or testing set.

  • transform (function, default None) –

    A user defined callback that transforms each sample. For example:

    transform=lambda data, label: (data.astype(np.float32)/255, label)
    

class mxnet.gluon.data.vision.datasets.CIFAR10(root='/home/jenkins_slave/.mxnet/datasets/cifar10', train=True, transform=None)[source]

Bases: mxnet.gluon.data.dataset._DownloadedDataset

CIFAR10 image classification dataset from https://www.cs.toronto.edu/~kriz/cifar.html

Each sample is an image (in 3D NDArray) with shape (32, 32, 3).

Parameters
  • root (str, default $MXNET_HOME/datasets/cifar10) – Path to temp folder for storing data.

  • train (bool, default True) – Whether to load the training or testing set.

  • transform (function, default None) –

    A user defined callback that transforms each sample. For example:

    transform=lambda data, label: (data.astype(np.float32)/255, label)
    

class mxnet.gluon.data.vision.datasets.CIFAR100(root='/home/jenkins_slave/.mxnet/datasets/cifar100', fine_label=False, train=True, transform=None)[source]

Bases: mxnet.gluon.data.vision.datasets.CIFAR10

CIFAR100 image classification dataset from https://www.cs.toronto.edu/~kriz/cifar.html

Each sample is an image (in 3D NDArray) with shape (32, 32, 3).

Parameters
  • root (str, default $MXNET_HOME/datasets/cifar100) – Path to temp folder for storing data.

  • fine_label (bool, default False) – Whether to load the fine-grained (100 classes) or coarse-grained (20 super-classes) labels.

  • train (bool, default True) – Whether to load the training or testing set.

  • transform (function, default None) –

    A user defined callback that transforms each sample. For example:

    transform=lambda data, label: (data.astype(np.float32)/255, label)
    

class mxnet.gluon.data.vision.datasets.ImageRecordDataset(filename, flag=1, transform=None)[source]

Bases: mxnet.gluon.data.dataset.RecordFileDataset

A dataset wrapping over a RecordIO file containing images.

Each sample is an image and its corresponding label.

Parameters
  • filename (str) – Path to rec file.

  • flag ({0, 1}, default 1) – If 0, always convert images to greyscale. If 1, always convert images to colored (RGB).

  • transform (function, default None) –

    A user defined callback that transforms each sample. For example:

    transform=lambda data, label: (data.astype(np.float32)/255, label)
    

class mxnet.gluon.data.vision.datasets.ImageFolderDataset(root, flag=1, transform=None)[source]

Bases: mxnet.gluon.data.dataset.Dataset

A dataset for loading image files stored in a folder structure.

like:

root/car/0001.jpg
root/car/xxxa.jpg
root/car/yyyb.jpg
root/bus/123.jpg
root/bus/023.jpg
root/bus/wwww.jpg
Parameters
  • root (str) – Path to root directory.

  • flag ({0, 1}, default 1) – If 0, always convert loaded images to greyscale (1 channel). If 1, always convert loaded images to colored (3 channels).

  • transform (callable, default None) –

    A function that takes data and label and transforms them:

    transform = lambda data, label: (data.astype(np.float32)/255, label)
    

synsets

List of class names. synsets[i] is the name for the integer label i

Type

list

items

List of all images in (filename, label) pairs.

Type

list of tuples