I've been assigned with the task of creating a model to detect whether and advertisement exists in an image and optionally to draw a bounding box around it.
My first thought was that this is an object detection problem, since I also have to draw bounding boxes. However, after some consideration I believe that an image classification model could perform better in this task for these reasons.
- An ad never has a standard format some time it contains text some times only objects.
- Very often an ad contains more than a single object.
- If I was to create an object detection model I would have to train it for pairs of objects, since I would want it to be able to distinguish a parked car in a image from a car in an banner advertisement in the same image.
- I would need to gather at least a pair of collection of annotated images for every object that I would need from my model to be able to detect. In other words it would have been extremely difficult to generalize for many different ad, since for every ad I would need at least one pair if we suppose that one ad contains one or more objects.
For those reasons I think that I should create an image classification model, although this way I loose the optional feature of bounding boxes.
Any thoughts on that?