CIFAR-10

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.

Use cases

Image classification.

Properties

• name: cifar10
• keywords: image_processing, classification
• dataset size: 170,5 MB
• is downloadable: yes
• tasks: classification (default)

Tasks

classification (default)

• How to use
• Properties
• HDF5 file structure
• Fields

How to use

>>> # import the package
>>> import dbcollection as dbc
>>>
>>> # load the dataset
>>> cifar10 = dbc.load('cifar10')
>>> cifar10
DataLoader: "cifar10" (classification task)

Properties

• primary use: image classification
• description: Contains image tensors and label annotations for image classification.
• sets: train, test
• metadata file size in disk: 178,3 MB

• has annotations: yes

  • which:

    • labels for each image class/category.

• available fields:

  • classes
  • images
  • labels
  • object_fields
  • object_ids
  • list_images_per_class

HDF5 file structure

/
├── train/
│   ├── classes        # dtype=np.uint8, shape=(10,11)  (note: string in ASCII format)
│   ├── images         # dtype=np.uint8, shape=(50000,32,32,3)
│   ├── labels         # dtype=np.uint8, shape=(50000,)
│   ├── object_fields  # dtype=np.uint8, shape=(2,8)    (note: string in ASCII format)
│   ├── object_ids     # dtype=np.int32, shape=(50000,2)
│   └── list_images_per_class   # dtype=np.int32, shape=(10,5000))
│
└── test/
    ├── classes        # dtype=np.uint8, shape=(10,11)  (note: string in ASCII format)
    ├── images         # dtype=np.uint8, shape=(10000,32,32,3)
    ├── labels         # dtype=np.uint8, shape=(10000,)
    ├── object_fields  # dtype=np.uint8, shape=(2,8)    (note: string in ASCII format)
    ├── object_ids     # dtype=np.int32, shape=(10000,2)
    └── list_images_per_class   # dtype=np.int32, shape=(10,1000))

Fields

• classes: class names

  • 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

• 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

Disclaimer

All rights reserved to the original creators of CIFAR-10.

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