My task is not a simple image -> category. I have between 5 and 10 images of an object, and I must classify it. The problem is that the category isn't "visible" in each picture, so I would need many images -> category.

For instance, let's say I'm trying to classify men vs. women. I have a number of pictures taken from random angles. I need to determine, based on the aggregated score of these pictures, which category the bunch belongs to.

All the pictures contain a little bit of unique information.

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1 Answer 1


You are describing a problem of supervised learning with multiple inputs. That is not an uncommon task and you can find many tutorials about multiple inputs for neural networks out there. Using Tensorflow, I personally recommend Keras Functional API for this task, since it gives you more control on the layers while keeping the high-level simplicity.


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