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What is the method for increasing data in a specific subdirectory using image_dataset_from_directory?

asked 2022-05-07 11:00:00 +0000

plato gravatar image

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answered 2021-12-05 19:00:00 +0000

djk gravatar image

The method for increasing data in a specific subdirectory using imagedatasetfromdirectory is to use data augmentation techniques such as rotation, flipping, shearing, zooming, and adjusting brightness and contrast. This can be done by passing the data augmentation options as arguments to the ImageDataGenerator class and then using this class to generate augmented images. The augmented images can then be fed to the imagedatasetfromdirectory method to create a new dataset with increased data. For example, the following code generates augmented images using rotation and horizontal flipping:

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Define data augmentation options
datagen = ImageDataGenerator(rotation_range=20, horizontal_flip=True)

# Generate augmented images
augmented_images = datagen.flow_from_directory(
    'data/subdirectory',
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary'
)

# Use ImageDatasetFromDirectory for new dataset
new_dataset = tf.keras.preprocessing.image_dataset_from_directory(
    'data/subdirectory',
    image_size=(224, 224),
    batch_size=32
)

Note that it's important to balance the use of data augmentation with the risk of overfitting to the training data.

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Asked: 2022-05-07 11:00:00 +0000

Seen: 8 times

Last updated: Dec 05 '21