Note
Click here to download the full example code
Class Performance#
This notebooks provides an overview for using and understanding the class performance check.
Structure:
What Is the Purpose of the Check?#
The class performance check evaluates several metrics on the given model and data and returns all of the results in a single check. The check uses the following default metrics:
Task Type |
Property name |
|---|---|
Classification |
Precision |
Classification |
Recall |
Object Detection |
|
Object Detection |
In addition to the default metrics, the check supports custom metrics that should be implemented using the torch.ignite.Metric API. These can be passed as a list using the alternative_metrics parameter of the check, which will override the default metrics.
Imports#
from deepchecks.vision.checks import ClassPerformance
from deepchecks.vision.datasets.classification import mnist
Classification Performance Report#
Generate data and model:#
mnist_model = mnist.load_model()
train_ds = mnist.load_dataset(train=True, object_type='VisionData')
test_ds = mnist.load_dataset(train=False, object_type='VisionData')
Run the check#
check = ClassPerformance()
result = check.run(train_ds, test_ds, mnist_model)
result
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Ingesting Batches - Train Dataset:
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Ingesting Batches - Train Dataset:
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Ingesting Batches - Test Dataset:
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Computing Check:
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Computing Check:
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If you have a GPU, you can speed up this check by calling: .. code:: python
check.run(train_ds, test_ds, mnist_model, device=<your GPU>)
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.
Object Detection Class Performance#
For object detection tasks - the default metric that is being calculated it the Average Precision. The definition of the Average Precision is identical to how the COCO dataset defined it - mean of the average precision per class, over the range [0.5, 0.95, 0.05] of IoU thresholds.
from deepchecks.vision.datasets.detection import coco
Generate Data and Model#
We generate a sample dataset of 128 images from the COCO dataset, and using the YOLOv5 model.
yolo = coco.load_model(pretrained=True)
train_ds = coco.load_dataset(train=True, object_type='VisionData')
test_ds = coco.load_dataset(train=False, object_type='VisionData')
Run the check#
check = ClassPerformance(show_only='best')
result = check.run(train_ds, test_ds, yolo)
result
Validating Input:
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Validating Input:
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Ingesting Batches - Train Dataset:
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Ingesting Batches - Train Dataset:
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Computing Check:
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Computing Check:
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Computing Check:
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If you have a GPU, you can speed up this check by calling:
# check.run(train_ds, test_ds, yolo, device=<your GPU>)
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.
Define a Condition#
We can also define a condition to validate that our model performance is above a certain threshold. The condition is defined as a function that takes the results of the check as input and returns a ConditionResult object.
check = ClassPerformance(show_only='worst')
check.add_condition_test_performance_greater_than(0.2)
result = check.run(train_ds, test_ds, yolo)
result
Validating Input:
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Validating Input:
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Validating Input:
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Ingesting Batches - Train Dataset:
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Ingesting Batches - Train Dataset:
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Ingesting Batches - Test Dataset:
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Computing Check:
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Computing Check:
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Computing Check:
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We detected that for several classes our model performance is below the threshold.
Total running time of the script: ( 1 minutes 11.760 seconds)