The performance of an object detection model is evaluated using two separate metrics that parallel its loss components. The classification results of anchor boxes are evaluated using accuracy. Meanwhile, the predicted bounding box offsets are evaluated using the mean absolute error, aligning with the $$\ell_1$$ norm loss used during training.

Evaluation Metrics for Object Detection Predictions

The overall loss function for an object detection model is computed by summing the anchor box class loss and the anchor box offset loss. This combined objective allows the model to simultaneously learn correct classification and precise localization.

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The prediction of offsets for positive (non-background) anchor boxes in object detection is formulated as a regression problem. Instead of the squared error loss commonly used in regression, the $$\ell_1$$ norm loss—the absolute value of the difference between the predicted offset and the ground-truth—is employed to calculate the error.

Anchor Box Offset Loss in Object Detection

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During the calculation of the anchor box offset loss, a mask variable is utilized to filter out irrelevant anchor boxes. This mask ensures that negative (background) anchor boxes and illegal (padded) anchor boxes do not contribute to the final regression loss.

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