Generates a tf.data.Dataset
from image files in a directory.
tf.keras.utils.image_dataset_from_directory(
directory,
labels='inferred',
label_mode='int',
class_names=None,
color_mode='rgb',
batch_size=32,
image_size=(256, 256),
shuffle=True,
seed=None,
validation_split=None,
subset=None,
interpolation='bilinear',
follow_links=False,
crop_to_aspect_ratio=False,
**kwargs
)
If your directory structure is:
main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg
Then calling image_dataset_from_directory(main_directory, labels='inferred')
will return a tf.data.Dataset
that yields batches of images from
the subdirectories class_a
and class_b
, together with labels
0 and 1 (0 corresponding to class_a
and 1 corresponding to class_b
).
Supported image formats: jpeg, png, bmp, gif.
Animated gifs are truncated to the first frame.
Args |
directory
|
Directory where the data is located.
If labels is "inferred", it should contain
subdirectories, each containing images for a class.
Otherwise, the directory structure is ignored.
|
labels
|
Either "inferred"
(labels are generated from the directory structure),
None (no labels),
or a list/tuple of integer labels of the same size as the number of
image files found in the directory. Labels should be sorted according
to the alphanumeric order of the image file paths
(obtained via os.walk(directory) in Python).
|
label_mode
|
- 'int': means that the labels are encoded as integers
(e.g. for `sparse_categorical_crossentropy` loss).
- 'categorical' means that the labels are
encoded as a categorical vector
(e.g. for
categorical_crossentropy loss).
- 'binary' means that the labels (there can be only 2)
are encoded as
float32 scalars with values 0 or 1
(e.g. for binary_crossentropy ).
- None (no labels).
|
class_names
|
Only valid if "labels" is "inferred". This is the explicit
list of class names (must match names of subdirectories). Used
to control the order of the classes
(otherwise alphanumerical order is used).
|
color_mode
|
One of "grayscale", "rgb", "rgba". Default: "rgb".
Whether the images will be converted to
have 1, 3, or 4 channels.
|
batch_size
|
Size of the batches of data. Default: 32.
|
image_size
|
Size to resize images to after they are read from disk.
Defaults to (256, 256) .
Since the pipeline processes batches of images that must all have
the same size, this must be provided.
|
shuffle
|
Whether to shuffle the data. Default: True.
If set to False, sorts the data in alphanumeric order.
|
seed
|
Optional random seed for shuffling and transformations.
|
validation_split
|
Optional float between 0 and 1,
fraction of data to reserve for validation.
|
subset
|
One of "training" or "validation".
Only used if validation_split is set.
|
interpolation
|
String, the interpolation method used when resizing images.
Defaults to bilinear . Supports bilinear , nearest , bicubic ,
area , lanczos3 , lanczos5 , gaussian , mitchellcubic .
|
follow_links
|
Whether to visits subdirectories pointed to by symlinks.
Defaults to False.
|
crop_to_aspect_ratio
|
If True, resize the images without aspect
ratio distortion. When the original aspect ratio differs from the target
aspect ratio, the output image will be cropped so as to return the largest
possible window in the image (of size image_size ) that matches
the target aspect ratio. By default (crop_to_aspect_ratio=False ),
aspect ratio may not be preserved.
|
**kwargs
|
Legacy keyword arguments.
|
Returns |
A tf.data.Dataset object.
- If
label_mode is None, it yields float32 tensors of shape
(batch_size, image_size[0], image_size[1], num_channels) ,
encoding images (see below for rules regarding num_channels ).
- Otherwise, it yields a tuple
(images, labels) , where images
has shape (batch_size, image_size[0], image_size[1], num_channels) ,
and labels follows the format described below.
|
Rules regarding labels format:
- if
label_mode
is int
, the labels are an int32
tensor of shape
(batch_size,)
.
- if
label_mode
is binary
, the labels are a float32
tensor of
1s and 0s of shape (batch_size, 1)
.
- if
label_mode
is categorial
, the labels are a float32
tensor
of shape (batch_size, num_classes)
, representing a one-hot
encoding of the class index.
Rules regarding number of channels in the yielded images:
- if
color_mode
is grayscale
,
there's 1 channel in the image tensors.
- if
color_mode
is rgb
,
there are 3 channel in the image tensors.
- if
color_mode
is rgba
,
there are 4 channel in the image tensors.