Understanding `width_shift_range` and `height_shift_range` arguments in Keras’s ImageDataGenerator class

Understanding `width_shift_range` and `height_shift_range` in Keras’s ImageDataGenerator

In deep learning, data augmentation is a crucial technique for improving model performance and preventing overfitting. Keras’s `ImageDataGenerator` class offers various data augmentation techniques, including image shifting. This article focuses on the `width_shift_range` and `height_shift_range` arguments and how they enhance image datasets for training.

Image Shifting: A Key Data Augmentation Technique

Image shifting involves horizontally or vertically shifting images during the training process. This simple yet effective technique helps your model learn to recognize objects despite minor position variations.

Horizontal Shifting (`width_shift_range`)

The `width_shift_range` argument in `ImageDataGenerator` controls the degree of horizontal image shifting. It’s a fractional value between 0 and 1, representing the maximum fraction of the image width to shift. For example:

• `width_shift_range=0.2` means images can be shifted horizontally by a maximum of 20% of their width.

Vertical Shifting (`height_shift_range`)

Similarly, `height_shift_range` controls vertical image shifting. It accepts a fractional value between 0 and 1, defining the maximum fraction of the image height to shift. For instance:

• `height_shift_range=0.1` allows for vertical shifts of up to 10% of the image height.

How Image Shifting Benefits Model Training

By applying horizontal and vertical shifts during training, your model learns to generalize better to images with varying object positions. Here’s how it benefits training:

• **Increased Robustness:** Models become less sensitive to minor changes in object locations.
• **Improved Generalization:** The model learns to recognize objects regardless of their position within the image.
• **Reduced Overfitting:** Augmenting data with shifts prevents the model from overfitting to specific object positions in the training set.

Practical Example: Implementing Image Shifting

Let’s see how to use `width_shift_range` and `height_shift_range` in `ImageDataGenerator` to augment images. Assume you have a directory of images named ‘images’ and you want to train a model. Here’s how you’d create a generator:


<pre>
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Flow from directory
train_generator = train_datagen.flow_from_directory(
'images',
target_size=(150, 150),
batch_size=32,
class_mode='binary'
)
</pre>


In this code:

• We initialize an `ImageDataGenerator` object with `rescale` for normalization, `width_shift_range` set to 0.2, and `height_shift_range` set to 0.1.
• The `flow_from_directory` method generates batches of augmented images from the ‘images’ directory, resizing them to 150×150 and setting the `class_mode` to ‘binary’ for binary classification.

Key Considerations

When using image shifting, it’s essential to consider the following:

• **Magnitude of Shifts:** Choose a reasonable `width_shift_range` and `height_shift_range` value based on the characteristics of your dataset and the objects you want to recognize. Excessive shifts can distort the image or make it difficult to identify objects.
• **Data Distribution:** Ensure that the augmented images preserve the original data distribution and don’t introduce unintended biases.

Conclusion

The `width_shift_range` and `height_shift_range` arguments in Keras’s `ImageDataGenerator` offer a straightforward yet powerful method for data augmentation, significantly improving model robustness, generalization, and reducing overfitting. By understanding these arguments and applying them effectively, you can enhance the performance of your deep learning models, leading to more accurate predictions and improved results.