Mean Average Precision Report#

This notebooks provides an overview for using and understanding the mean average precision report check.

Structure:

What is the purpose of the check?
Generate Dataset
Run the check
Define a condition

What Is the Purpose of the Check?#

The Mean Average Precision Report evaluates the mAP metric on the given model and data, plots the AP on graph, and returns the mAP values per bounding box size category (small, medium, large). This check only works on the Object Detection task.

Generate Dataset#

We generate a sample dataset of 128 images from the COCO dataset, and using the YOLOv5 model.

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 MeanAveragePrecisionReport
from deepchecks.vision.datasets.detection import coco_torch as coco

test_ds = coco.load_dataset(train=False, object_type='VisionData')

Run the check#

check = MeanAveragePrecisionReport()
result = check.run(test_ds)
result

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Mean Average Precision Report

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.

Observe the check’s output#

The result value is a dataframe that has the Mean Average Precision score for different bounding box area sizes. We report the mAP for different IoU thresholds: 0.5, 0.75 and an average of mAP values for IoU thresholds between 0.5 and 0.9 (with a jump size of 0.05).

result.value

	mAP@[.50::.95] (avg.%)	mAP@.50 (%)	mAP@.75 (%)
Area size
All	0.409436	0.566673	0.425339
Small (area < 32^2)	0.212816	0.342429	0.212868
Medium (32^2 < area < 96^2)	0.383089	0.600228	0.349863
Large (area < 96^2)	0.541146	0.674493	0.585378

Define a condition#

We can define a condition that checks whether our model’s mean average precision score is not less than a given threshold for all bounding box sizes.

check = MeanAveragePrecisionReport().add_condition_average_mean_average_precision_greater_than(0.4)
result = check.run(test_ds)
result.show(show_additional_outputs=False)

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