Hands-On Convolutional Neural Networks with TensorFlow

Filtering output

More often than not, your model in practice will return multiple detection windows for the same object. To handle this, we use an algorithm called Non-Maximum Suppression. This algorithm filters these multiple boxes using the "IoU and presence of object" as heuristics. Here's how it works:

1. Discard all boxes with a low probability of containing an object (pc < 0.6)
2. Select the box with the biggest probability of having an object (pc on our label)
3. Discard all boxes with a high overlap with the selected box (IoU > 0.5)
4. Repeat steps 2 and 3 until all detections are either discarded or selected

We will use the Non-Maximum suppression on the prediction time on our detector:

Tensorflow already has a function that implements the ...