How do I get one overall prediction, where each data point has many pictures? My task is not a simple image -&gt; category. I have between 5 and 10 images of an object, and I must classify it. The problem is that the category isn't &quot;visible&quot; in each picture, so I would need many images -&gt; 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.
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.