Why is Object Detection so Messy? The downside is its high memory cost and lower detection accuracy. Each box consumes memory proportional to the number of classes, and the number of boxes grows quadratically with the image resolution. This hunger can be quite costly when there are many classes and a high input resolution
Those working with Neural Networks know how complicated Object Detection techniques can be. It is no wonder there is no straight forward resource for training them. You are always required to convert your data to a COCO-like JSON or some other unwanted format. It is never a plug and play experience. Moreover, no diagram thoroughly explains Faster R-CNN or YOLO as there is for U-Net or ResNet. There are just too many details.
While these models are quite messy, the explanation for their lack of simplicity is quite straight forward. It fits in a single sentence:
Neural Networks have fixed-sized outputs
In object detection, you can’t know _a priori _how many objects there are in a scene. There might be one, two, twelve, or none. The following images all have the same resolution but feature different numbers of objects.
The one million dollar question is: _How can we build variable-sized outputs out of fixed-sized networks? _Plus, how are we supposed to train a variable number of answers and loss terms? How can we penalize wrong predictions?
To create outputs that vary in size, two approaches dominate the literature: the “one size fits all” approach, an output so broad that it suffices for all applications, and the “look-ahead” idea, we search for regions-of-interest, and then we classify them.
I just made up those terms 😄. In practice, they are known as “one-stage” and “two-stage” approaches, which is a tad less self-explanatory.
Overfeat, YOLO, SSD, RetinaNet, etc.
If we can’t have variable-sized outputs, we shall return an output so large that it will always be larger than what we need, then we can prune the excess
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