• API Reference - ImagePropertyDrift

Image Property Drift

This notebooks provides an overview for using and understanding the image property drift check.

Structure:

• What Is Image Drift?

• Which Image Properties Are Used?

• Prepare data

• Run the check

• Define a condition

• Check Parameters

What Is Image Drift?

Drift is simply a change in the distribution of data over time, and it is also one of the top reasons why machine learning model’s performance degrades over time.

Image drift is a data drift that occurs in images in the dataset.

For more information on drift, please visit our drift guide.

How Deepchecks Detects Image Drift

This check detects image property drift by using univariate measures on each image property separately. Another possible method for drift detection is by a domain classifier which is used in the Image Dataset Drift check.

Using Properties to Detect Image Drift

In computer vision specifically, we can’t measure drift on images directly, as the individual pixel has little value when estimating drift. Therefore, we calculate drift on different properties of the image, on which we can directly measure drift.

Which Image Properties Are Used?

Property name	What is it
Aspect Ratio	Ratio between height and width of image (height / width)
Area	Area of image in pixels (height * width)
Brightness	Average intensity of image pixels. Color channels have different weights according to RGB-to-Grayscale formula
RMS Contrast	Contrast of image, calculated by standard deviation of pixels
Mean Red Relative Intensity	Mean over all pixels of the red channel, scaled to their relative intensity in comparison to the other channels [r / (r + g + b)].
Mean Green Relative Intensity	Mean over all pixels of the green channel, scaled to their relative intensity in comparison to the other channels [g / (r + g + b)].
Mean Blue Relative Intensity	Mean over all pixels of the blue channel, scaled to their relative intensity in comparison to the other channels [b / (r + g + b)].

Imports

Note

In this example, we use the pytorch version of the coco dataset and model. In order to run this example using tensorflow, please change the import statements to:

from deepchecks.vision.datasets.detection import coco_tensorflow as coco

from deepchecks.vision.checks import ImagePropertyDrift
from deepchecks.vision.datasets.detection import coco_torch as coco

Prepare data

from deepchecks.vision.utils import image_properties

train_dataset = coco.load_dataset(train=True, object_type='VisionData')
test_dataset = coco.load_dataset(train=False, object_type='VisionData')

Run the check

check_result = ImagePropertyDrift().run(train_dataset, test_dataset)
check_result

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To display the results in an IDE like PyCharm, you can use the following code:

#  check_result.show_in_window()

The result will be displayed in a new window.

Observe the check’s output

The result value is a pandas DataFrame that contains drift score for each image property.

check_result.value

{'Aspect Ratio': {'Drift score': 0.109375, 'Method': 'Kolmogorov-Smirnov'}, 'Area': {'Drift score': 0.140625, 'Method': 'Kolmogorov-Smirnov'}, 'Brightness': {'Drift score': 0.203125, 'Method': 'Kolmogorov-Smirnov'}, 'RMS Contrast': {'Drift score': 0.078125, 'Method': 'Kolmogorov-Smirnov'}, 'Mean Red Relative Intensity': {'Drift score': 0.109375, 'Method': 'Kolmogorov-Smirnov'}, 'Mean Green Relative Intensity': {'Drift score': 0.21875, 'Method': 'Kolmogorov-Smirnov'}, 'Mean Blue Relative Intensity': {'Drift score': 0.109375, 'Method': 'Kolmogorov-Smirnov'}}

We can also pass the check a list of classes we wish to inspect, and the check will calculate the properties only for images either belonging to the classes or containing annotations belonging to the classes. (We’ll lower the min_samples to 5 to tell the check to calculate drift despite having only a few images left after the class filtration)

check_result = ImagePropertyDrift(classes_to_display=['person', 'traffic light'], min_samples=5
                                  ).run(train_dataset, test_dataset)
check_result

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Define a condition

We can define a condition that make sure that image properties drift scores do not exceed allowed threshold.

check_result = (
    ImagePropertyDrift()
    .add_condition_drift_score_less_than(0.001)
    .run(train_dataset, test_dataset)
)
check_result.show(show_additional_outputs=False)

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Check Parameters

Image Property Drift Check accepts two parameters that allows us to control the look of the output:

• vision_properties - list of image properties that we are interested in

• max_num_categories - Maximal number of categories to use for the calculation of drift using PSI (Population Stability Index)

Only next string values are allowed for the vision_properties parameter:

• aspect_ratio

• area

• brightness

• mean_red_relative_intensity

• mean_green_relative_intensity

• mean_blue_relative_intensity

from typing import List

import numpy as np


def area(images: List[np.ndarray]) -> List[int]:
    # Return list of integers of image areas (height multiplied by width)
    return [img.shape[0] * img.shape[1] for img in images]


def aspect_ratio(images: List[np.ndarray]) -> List[float]:
    # Return list of floats of image height to width ratio
    return [img.shape[0] / img.shape[1] for img in images]


properties = [
    {'name': 'Area', 'method': area, 'output_type': 'numerical'},
    {'name': 'Aspect Ratio', 'method': aspect_ratio, 'output_type': 'numerical'}
]

check_result = ImagePropertyDrift(
    image_properties=properties,
    max_num_categories_for_drift=20
).run(train_dataset, test_dataset)

check_result

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Image Dataset Drift

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Label Drift