Note
Click here to download the full example code
Image Property Drift#
This notebooks provides an overview for using and understanding the image property drift check.
Structure:
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#
from deepchecks.vision.checks import ImagePropertyDrift
from deepchecks.vision.datasets.detection import 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
Validating Input:
| | 0/1 [Time: 00:00]
Validating Input:
|#####| 1/1 [Time: 00:00]
Ingesting Batches - Train Dataset:
| | 0/2 [Time: 00:00]
Ingesting Batches - Train Dataset:
|##5 | 1/2 [Time: 00:01]
Ingesting Batches - Train Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Train Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Test Dataset:
| | 0/2 [Time: 00:00]
Ingesting Batches - Test Dataset:
|##5 | 1/2 [Time: 00:01]
Ingesting Batches - Test Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Test Dataset:
|#####| 2/2 [Time: 00:02]
Computing Check:
| | 0/1 [Time: 00:00]
Computing Check:
|#####| 1/1 [Time: 00:00]
Computing Check:
|#####| 1/1 [Time: 00:00]
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': 0.06673251751083462, 'Area': 0.05020210991879349, 'Brightness': 0.07114552630082199, 'RMS Contrast': 0.021987071714629717, 'Mean Red Relative Intensity': 0.03433695520859712, 'Mean Green Relative Intensity': 0.06265872287013945, 'Mean Blue Relative Intensity': 0.04916001505220028}
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
Validating Input:
| | 0/1 [Time: 00:00]
Validating Input:
|#####| 1/1 [Time: 00:00]
Ingesting Batches - Train Dataset:
| | 0/2 [Time: 00:00]
Ingesting Batches - Train Dataset:
|##5 | 1/2 [Time: 00:01]
Ingesting Batches - Train Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Train Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Test Dataset:
| | 0/2 [Time: 00:00]
Ingesting Batches - Test Dataset:
|##5 | 1/2 [Time: 00:01]
Ingesting Batches - Test Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Test Dataset:
|#####| 2/2 [Time: 00:02]
Computing Check:
| | 0/1 [Time: 00:00]
Computing Check:
|#####| 1/1 [Time: 00:00]
Computing Check:
|#####| 1/1 [Time: 00:00]
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)
Validating Input:
| | 0/1 [Time: 00:00]
Validating Input:
|#####| 1/1 [Time: 00:00]
Ingesting Batches - Train Dataset:
| | 0/2 [Time: 00:00]
Ingesting Batches - Train Dataset:
|##5 | 1/2 [Time: 00:01]
Ingesting Batches - Train Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Train Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Test Dataset:
| | 0/2 [Time: 00:00]
Ingesting Batches - Test Dataset:
|##5 | 1/2 [Time: 00:01]
Ingesting Batches - Test Dataset:
|#####| 2/2 [Time: 00:02]
Ingesting Batches - Test Dataset:
|#####| 2/2 [Time: 00:02]
Computing Check:
| | 0/1 [Time: 00:00]
Computing Check:
|#####| 1/1 [Time: 00:00]
Computing Check:
|#####| 1/1 [Time: 00:00]
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
Validating Input:
| | 0/1 [Time: 00:00]
Validating Input:
|#####| 1/1 [Time: 00:00]
Ingesting Batches - Train Dataset:
| | 0/2 [Time: 00:00]
Ingesting Batches - Train Dataset:
|##5 | 1/2 [Time: 00:00]
Ingesting Batches - Train Dataset:
|#####| 2/2 [Time: 00:00]
Ingesting Batches - Train Dataset:
|#####| 2/2 [Time: 00:00]
Ingesting Batches - Test Dataset:
| | 0/2 [Time: 00:00]
Ingesting Batches - Test Dataset:
|##5 | 1/2 [Time: 00:00]
Ingesting Batches - Test Dataset:
|#####| 2/2 [Time: 00:00]
Ingesting Batches - Test Dataset:
|#####| 2/2 [Time: 00:00]
Computing Check:
| | 0/1 [Time: 00:00]
Computing Check:
|#####| 1/1 [Time: 00:00]
Total running time of the script: ( 0 minutes 19.685 seconds)