tff.simulation.baselines.landmark.create_landmark_classification_task

Creates a baseline task of image classification on GLDv2.

tff.simulation.baselines.landmark.create_landmark_classification_task(
    train_client_spec: tff.simulation.baselines.ClientSpec,
    eval_client_spec: Optional[tff.simulation.baselines.ClientSpec] = None,
    use_gld23k: bool = False,
    cache_dir: Optional[str] = None,
    use_synthetic_data: bool = False,
    debug_seed: Optional[int] = None
) -> tff.simulation.baselines.BaselineTask

The goal of the task is to minimize the sparse categorical cross entropy between the output labels of the model and the true label of the image. A MobilenetV2 model is created that expects input image data with a shape of [128, 128, 3] and group normalization layers with a group number of 8.

Args
train_client_spec	A tff.simulation.baselines.ClientSpec specifying how to preprocess train client data.
eval_client_spec	An optional tff.simulation.baselines.ClientSpec specifying how to preprocess evaluation client data. If set to None, the evaluation datasets will use a batch_size of 64.
use_gld23k	An optional boolean. When true, a smaller version of the GLDv2 landmark dataset will be loaded. This gld23k dataset is used for faster prototyping.
cache_dir	An optional directory to cache the downloadeded datasets. If non-specified, they will be cached to the default cache directory cache.
use_synthetic_data	An optional boolean indicating whether to use synthetic GLDv2 data. This option should only be used for testing purposes, in order to avoid downloading the entire GLDv2 dataset.
debug_seed	An optional integer seed to force deterministic model initialization. This is intended for unittesting.

Returns
A tff.simulation.baselines.BaselineTask.