dataeval.utils.data.datasets.MNIST¶
-
class dataeval.utils.data.datasets.MNIST(root, download=
False, image_set='train', size=-1, classes=None, unit_interval=False, dtype=None, channels='channels_first', flatten=False, crop=None, normalize=None, corruption=None, balance=True, slice_back=False, verbose=False)¶ MNIST Dataset and Corruptions.
There are 15 different styles of corruptions. This class downloads differently depending on if you need just the original dataset or if you need corruptions. If you need both a corrupt version and the original version then choose corruption=”identity” as this downloads all of the corrupt datasets and provides the original as identity. If you just need the original, then using corruption=None will download only the original dataset to save time and space.
- Parameters:¶
- root : str or pathlib.Path¶
Root directory of dataset where the
mnistfolder exists.- download : bool, default False¶
If True, downloads the dataset from the internet and puts it in root directory. Class checks to see if data is already downloaded to ensure it does not create a duplicate download.
- image_set : "train", "test" or "base", default "train"¶
If “base”, returns all of the data to allow the user to create their own splits.
- size : int, default -1¶
Limit the dataset size, must be a value greater than 0.
- classes : "zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", int, list, or None, default None¶
Option to select specific classes from dataset. Classes are 0-9, any other number is ignored.
- unit_interval : bool, default False¶
Shift the data values to the unit interval [0-1].
- dtype : type | None, default None¶
Change the NumPy dtype - data is loaded as np.uint8
- channels : "channels_first" or "channels_last", default "channels_first"¶
Location of channel axis, default is channels first (N, 1, 28, 28)
- flatten : bool, default False¶
Flatten data into single dimension (N, 784) - cannot use both channels and flatten. If True, channels parameter is ignored.
- normalize : tuple[mean, std] or None, default None¶
Normalize images acorrding to provided mean and standard deviation
- corruption : "identity", "shot_noise", "impulse_noise", "glass_blur", "motion_blur", "shear", "scale", "rotate", "brightness", "translate", "stripe" "fog", "spatter", "dotted_line", "zigzag", "canny_edges" or None, default None¶
The desired corruption style or None.
- balance : bool, default True¶
If True, returns equal number of samples for each class.
- slice_back : bool, default False¶
If True and size has a value greater than 0, then grabs selection starting at the last image.
- verbose : bool, default False¶
If True, outputs print statements.
- crop : int | None¶
- index2label¶
Dictionary which translates from class integers to the associated class strings.
- Type:¶
dict
- label2index¶
Dictionary which translates from class strings to the associated class integers.
- Type:¶
dict
- dataset_dir¶
Location of the folder containing the data. Different from root if downloading data.
- Type:¶
Path
- metadata¶
Dictionary containing Dataset metadata, such as id which returns the dataset class name.
- Type:¶
dict