tf.keras.preprocessing.image_dataset_from_directory

Generates a tf.data.Dataset from image files in a directory.

Used in the notebooks

Used in the tutorials

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.

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), 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 explict 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 ima