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mediapipe_model_maker.gesture_recognizer.dataset.classification_dataset.ClassificationDataset

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Dataset Loader for classification models.

Inherits From: Dataset

mediapipe_model_maker.gesture_recognizer.dataset.classification_dataset.ClassificationDataset(
    dataset: tf.data.Dataset, size: int, label_names: List[str]
)

tf_dataset A tf.data.Dataset object that contains a potentially large set of elements, where each element is a pair of (input_data, target). The input_data means the raw input data, like an image, a text etc., while the target means the ground truth of the raw input data, e.g. the classification label of the image etc.
size The size of the dataset. tf.data.Dataset donesn't support a function to get the length directly since it's lazy-loaded and may be infinite.

label_names

num_classes

size Returns the size of the dataset.

Note that this function may return None becuase the exact size of the dataset isn't a necessary parameter to create an instance of this class, and tf.data.Dataset donesn't support a function to get the length directly since it's lazy-loaded and may be infinite. In most cases, however, when an instance of this class is created by helper functions like 'from_folder', the size of the dataset will be preprocessed, and this function can return an int representing the size of the dataset.

Methods

gen_tf_dataset

View source

gen_tf_dataset(
    batch_size: int = 1,
    is_training: bool = False,
    shuffle: bool = False,
    preprocess: Optional[Callable[..., bool]] = None,
    drop_remainder: bool = False
) -> tf.data.Dataset

Generates a batched tf.data.Dataset for training/evaluation.

Args
batch_size An integer, the returned dataset will be batched by this size.
is_training A boolean, when True, the returned dataset will be optionally shuffled and repeated as an endless dataset.
shuffle A boolean, when True, the returned dataset will be shuffled to create randomness during model training.
preprocess A function taking three arguments in order, feature, label and boolean is_training.
drop_remainder boolean, whether the finaly batch drops remainder.

Returns
A TF dataset ready to be consumed by Keras model.

split

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split(
    fraction: float
) -> Tuple[ds._DatasetT, ds._DatasetT]

Splits dataset into two sub-datasets with the given fraction.

Primarily used for splitting the data set into training and testing sets.

Args
fraction float, demonstrates the fraction of the first returned subdataset in the original data.

Returns
The splitted two sub datasets.

__len__

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__len__()

Returns the number of element of the dataset.