Note
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Image Dataset Drift#
This notebooks provides an overview for using and understanding the image dataset drift check, used to detect drift in simple image properties between train and test datasets.
Structure:
What Is Image Dataset 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 dataset drift is a drift that occurs in more than one image property at a time, and may even affect the relationships between those properties, which are undetectable by univariate drift methods.
For more information on drift, please visit our drift guide.
How Deepchecks Detects Dataset Drift#
This check detects multivariate drift by using a domain classifier. Other methods to detect drift include univariate measures which is used in other checks, such as Image Property Drift check.
Using Properties to Detect Image Drift#
In computer vision specifically, we can’t measure drift on the 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#
import numpy as np
from deepchecks.vision.checks import ImageDatasetDrift
from deepchecks.vision.datasets.detection.coco import load_dataset
Loading the data#
train_ds = load_dataset(train=True, object_type='VisionData')
test_ds = load_dataset(train=False, object_type='VisionData')
Run the check#
without drift#
check = ImageDatasetDrift()
result = check.run(train_dataset=train_ds, test_dataset=test_ds)
result
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To display the results in an IDE like PyCharm, you can use the following code:
# result.show_in_window()
The result will be displayed in a new window.
Insert drift#
Now, we will define a custom data object that will insert a drift to the training set.
from deepchecks.vision.datasets.detection.coco import COCOData
def add_brightness(img):
reverse = 255 - img
addition_of_brightness = (reverse * 0.07).astype(int)
return img + addition_of_brightness
class DriftedCOCO(COCOData):
def batch_to_images(self, batch):
return [add_brightness(np.array(img)) for img in batch[0]]
train_dataloader = load_dataset(train=True, object_type='DataLoader')
test_dataloader = load_dataset(train=False, object_type='DataLoader')
drifted_train_ds = DriftedCOCO(train_dataloader)
test_ds_coco = COCOData(test_dataloader)
Run the check again#
check = ImageDatasetDrift()
result = check.run(train_dataset=drifted_train_ds, test_dataset=test_ds_coco)
result
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Define a Condition#
Now, we will define a condition that the maximum drift score is less than a certain threshold. In this example we will set the threshold at 0.2. In order to demonstrate the condition, we will use again the original (not drifted) train dataset.
check = ImageDatasetDrift().add_condition_drift_score_less_than(0.2)
result = check.run(train_dataset=train_ds, test_dataset=test_ds).show(show_additional_outputs=False)
result
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Total running time of the script: ( 0 minutes 18.500 seconds)