Working prodigy (http://prodi.gy) recipe for image binary classification, using Ines' suggestions in https://support.prodi.gy/t/whats-a-recipe-for-dead-simple-binary-or-multiclass-image-classification/2136

image_classification.py

import prodigy
from prodigy.components.loaders import Images
from prodigy.util import split_string

def add_label_to_stream(stream, label):
    for eg in stream:
        # The 'label' you get from the command line is a list
        # so let's just assume it's always one and take the first
        eg["label"] = label[0]
        yield eg

@prodigy.recipe('image-classification',
    dataset=("The dataset to use", "positional", None, str),
    source=("Path to a directory of images", "positional", None, str),
    label=("One or more comma-separated labels", "option", "l", split_string)
)
def image_classification(dataset, source, label=None):
    """
    Stream images from directory and apply first label among the ones in input 
    """

    stream = Images(source)
    stream = add_label_to_stream(stream, label)

    print(stream)

    return {
        'dataset': dataset,        # Name of dataset to save annotations
        'stream': stream,          # Incoming stream of examples
        'view_id': 'classification'
    }

# Example usage (works in prodigy 1.8.5): 
# prodigy image-classification your-dataset-name local-directory-with-images -l spam,eggs -F image_classification.py