tf.data.Dataset from image files in a directory.
If your directory structure is:
image_dataset_from_directory( directory, labels = "inferred", label_mode = "int", class_names = NULL, color_mode = "rgb", batch_size = 32, image_size = c(256, 256), shuffle = TRUE, seed = NULL, validation_split = NULL, subset = NULL, interpolation = "bilinear", follow_links = FALSE )
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.
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).
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).
One of "grayscale", "rgb", "rgba". Default: "rgb". Whether the images will be converted to have 1, 3, or 4 channels.
Size of the batches of data. Default: 32.
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.
Whether to shuffle the data. Default: TRUE. If set to FALSE, sorts the data in alphanumeric order.
Optional random seed for shuffling and transformations.
Optional float between 0 and 1, fraction of data to reserve for validation.
One of "training" or "validation". Only used if validation_split is set.
String, the interpolation method used when resizing images. Defaults to bilinear. Supports bilinear, nearest, bicubic, area, lanczos3, lanczos5, gaussian, mitchellcubic.
Whether to visits subdirectories pointed to by symlinks. Defaults to FALSE.