There are different pictures of the same object. The pictures made from different angles, so while the object on the picture is the same, the pictures itself could be quite different.
Is there an example or ready to use deep learning model that will produce similar/close vectors for different pictures of the same object? (seems like face detection works in a kinda similar way...)
What you are looking for is a Siamese network, where in you pass 2 images through the same network and try to maximize distance between dissimilar images and minimize it between similar ones. Another variant used three images instead of two with one acting as anchor and one of the other two belonging to same class as original and other belonging to different class and you try to minimize and maximize distance from the anchor respectively. The loss function that achieves this is contrastive loss function. Look here for implementation of contrastive loss. And you can use any standard architecture in such a setting , I have personally found VGG-16 easy to tune and simple.
Here are some papers you should look at to understand the math and theory behind the same
