Objective:
To calculate the Mean Average Precision (MaP) of a detectnet_v2 model using a test set.
Context:
I have a test set consisting of 800 images, along with their ground truth bounding boxes provided in the Kitti format. I want to evaluate the test accuracy of a DetectNet_v2 model.
I noticed in the provided notebooks that there is an option to run inference on test images, which outputs images with predicted bounding boxes and a corresponding label file containing these bounding boxes. Is there a way to use these predicted bounding boxes and their ground truth bounding boxes to calculate the MaP of the model?
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