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all right i was having some screen tearing issues but i think were good now so let me pull that a little bit closer all right welcome to this unofficial part two uh of this object detection series so uh what were gonna try to understand in this video is how to evaluate a bounding box prediction so you know we have some target bounding box for an object and we have some predicted bounding box and we want to have a way of quantifying or measure how good is our predicted bounding box for that object and for that were going to learn about a metric called intersection over union and then were also going to implement that in pytorch so thatll be fun so without further ado lets get started lets roll that intro and then lets get started with intersection over [Music] union so the question is how do we measure how good a bounding box is so we have an image with in this case a car in it and we are given a target bounding box for that object and then we have some prediction bounding box
