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imagenette

Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:

imagenette is configured with tfds.image.imagenette.ImagenetteConfig and has the following configurations predefined (defaults to the first one):

  • full-size (v0.1.0) (Size: 1.45 GiB): Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:

  • Full size
  • 320 px
  • 160 px This dataset consists of the Imagenette dataset full-size variant.

  • 320px (v0.1.0) (Size: 325.48 MiB): Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:

  • Full size
  • 320 px
  • 160 px This dataset consists of the Imagenette dataset 320px variant.

  • 160px (v0.1.0) (Size: 94.18 MiB): Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:

  • Full size
  • 320 px
  • 160 px This dataset consists of the Imagenette dataset 160px variant.

imagenette/full-size

Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:

  • Full size
  • 320 px
  • 160 px This dataset consists of the Imagenette dataset full-size variant.

Versions:

  • 0.1.0 (default):

Statistics

Split Examples
ALL 13,394
TRAIN 12,894
VALIDATION 500

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
})

Homepage

Supervised keys (for as_supervised=True)

('image', 'label')

imagenette/320px

Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:

  • Full size
  • 320 px
  • 160 px This dataset consists of the Imagenette dataset 320px variant.

Versions:

  • 0.1.0 (default):

Statistics

Split Examples
ALL 13,394
TRAIN 12,894
VALIDATION 500

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
})

Homepage

Supervised keys (for as_supervised=True)

('image', 'label')

imagenette/160px

Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:

  • Full size
  • 320 px
  • 160 px This dataset consists of the Imagenette dataset 160px variant.

Versions:

  • 0.1.0 (default):

Statistics

Split Examples
ALL 13,394
TRAIN 12,894
VALIDATION 500

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
})

Homepage

Supervised keys (for as_supervised=True)

('image', 'label')

Citation