Stay organized with collections Save and categorize content based on your preferences.

Creates a baseline task of image classification on GLDv2.

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.

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.

A tff.simulation.baselines.BaselineTask.