CIFAR-100

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).

Use cases

Image classification.

Properties

  • name: cifar100
  • keywords: image_processing, classification
  • dataset size: 355,3 MB
  • is downloadable: yes
  • tasks: classification (default)

Tasks

classification (default)

How to use

>>> # import the package
>>> import dbcollection as dbc
>>>
>>> # load the dataset
>>> cifar100 = dbc.load('cifar100')
>>> cifar100
DataLoader: "cifar100" (classification task)

Properties

HDF5 file structure

/
├── train/
│   ├── classes        # dtype=np.uint8, shape=(100,18)  (note: string in ASCII format)
│   ├── superclasses   # dtype=np.uint8, shape=(20,31)   (note: string in ASCII format)
│   ├── images         # dtype=np.uint8, shape=(50000,32,32,3)
│   ├── labels         # dtype=np.uint8, shape=(50000,)
│   ├── coarse_labels  # dtype=np.uint8, shape=(50000,)
│   ├── object_fields  # dtype=np.uint8, shape=(3,13)    (note: string in ASCII format)
│   ├── object_ids     # dtype=np.int32, shape=(50000,3)
│   ├── list_images_per_class        # dtype=np.int32, shape=(100,500))
│   └── list_images_per_superclass   # dtype=np.int32, shape=(20,2500))
│
└── test/
    ├── classes        # dtype=np.uint8, shape=(100,18)  (note: string in ASCII format)
    ├── superclasses   # dtype=np.uint8, shape=(20,31)   (note: string in ASCII format)
    ├── images         # dtype=np.uint8, shape=(10000,32,32,3)
    ├── labels         # dtype=np.uint8, shape=(10000,)
    ├── coarse_labels  # dtype=np.uint8, shape=(10000,)
    ├── object_fields  # dtype=np.uint8, shape=(3,13)    (note: string in ASCII format)
    ├── object_ids     # dtype=np.int32, shape=(10000,3)
    ├── list_images_per_class        # dtype=np.int32, shape=(100,100))
    └── list_images_per_superclass   # dtype=np.int32, shape=(20,500))

Fields

  • classes: class descriptions
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • superclasses: super class names. It is composed of groups of classes per super class
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • images: images tensor
    • available in: train, test
    • dtype: np.uint8
    • is padded: False
    • fill value: -1
  • labels: class ids
    • available in: train, test
    • dtype: np.uint8
    • is padded: False
    • fill value: -1
  • coarse_labels: superclass ids
    • available in: train, test
    • dtype: np.uint8
    • is padded: False
    • fill value: -1
  • object_fields: list of field names of the object id list
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
    • note: key field (field name aggregator)
  • object_ids: list of field ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
    • note: key field (field id aggregator)
  • list_images_per_class: list of image ids per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_images_per_superclass: list of image ids per superclass
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list

Disclaimer

All rights reserved to the original creators of CIFAR-100.

For information about the dataset and its terms of use, please see this link.