tfds.folder_dataset.ImageFolder

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Generic image classification dataset created from manual directory.

Inherits From: DatasetBuilder

tfds.folder_dataset.ImageFolder(
    root_dir: str,
    *,
    shape: Optional[type_utils.Shape] = None,
    dtype: Optional[tf.DType] = None
)

ImageFolder creates a tf.data.Dataset reading the original image files.

The data directory should have the following structure:

path/to/image_dir/
  split_name/  # Ex: 'train'
    label1/  # Ex: 'airplane' or '0015'
      xxx.png
      xxy.png
      xxz.png
    label2/
      xxx.png
      xxy.png
      xxz.png
  split_name/  # Ex: 'test'
    ...

To use it:

builder = tfds.ImageFolder('path/to/image_dir/')
print(builder.info)  # num examples, labels... are automatically calculated
ds = builder.as_dataset(split='train', shuffle_files=True)
tfds.show_examples(ds, builder.info)

Methods

as_dataset

View source

as_dataset(
    split: Optional[Tree[splits_lib.SplitArg]] = None,
    *,
    batch_size: tfds.typing.Dim = None,
    shuffle_files: bool = False,
    decoders: Optional[TreeDict[decode.partial_decode.DecoderArg]] = None,
    read_config: Optional[tfds.ReadConfig] = None,
    as_supervised: bool = False
)

Constructs a tf.data.Dataset.

Callers must pass arguments as keyword arguments.

The output types vary depending on the parameters. Examples:

builder = tfds.builder('imdb_reviews')
builder.download_and_prepare()

# Default parameters: Returns the dict of tf.data.Dataset
ds_all_dict = builder.as_dataset()
assert isinstance(ds_all_dict, dict)
print(ds_all_dict.keys())  # ==> ['test', 'train', 'unsupervised']

assert isinstance(ds_all_dict['test'], tf.data.Dataset)
# Each dataset (test, train, unsup.) consists of dictionaries
# {'label': <tf.Tensor: .. dtype=int64, numpy=1>,
#  'text': <tf.Tensor: .. dtype=string, numpy=b"I've watched the movie ..">}
# {'label': <tf.Tensor: .. dtype=int64, numpy=1>,
#  'text': <tf.Tensor: .. dtype=string, numpy=b'If you love Japanese ..'>}

# With as_supervised: tf.data.Dataset only contains (feature, label) tuples
ds_all_supervised = builder.as_dataset(as_supervised=True)
assert isinstance(ds_all_supervised, dict)
print(ds_all_supervised.keys())  # ==> ['test', 'train', 'unsupervised']

assert isinstance(ds_all_supervised['test'], tf.data.Dataset)
# Each dataset (test, train, unsup.) consists of tuples (text, label)
# (<tf.Tensor: ... dtype=string, numpy=b"I've watched the movie ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)
# (<tf.Tensor: ... dtype=string, numpy=b"If you love Japanese ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)

# Same as above plus requesting a particular split
ds_test_supervised = builder.as_dataset(as_supervised=True, split='test')
assert isinstance(ds_test_supervised, tf.data.Dataset)
# The dataset consists of tuples (text, label)
# (<tf.Tensor: ... dtype=string, numpy=b"I've watched the movie ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)
# (<tf.Tensor: ... dtype=string, numpy=b"If you love Japanese ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)

download_and_prepare

View source

download_and_prepare(
    **kwargs
)

Downloads and prepares dataset for reading.

{

}

{

}