The number of images is a difficult question. The smallest dataset I trained a network on was around 1000 images but that depends on how easy to detect the object and if the network was pretrained on similar images. You don't need separate trained networks for the detection of an object if you have sufficient number of different side examples in training set. My recommendation for minibatch is the biggest what you can fit into your hardware memory (GPU memory or RAM) before getting out of memory errors. I've heard recommendations on choosing power of 2 if you are using a GPU but did not test that myself.
For ships highlighted by a bounding-box I would suggest looking on ImageNet:
Alexnet is not first recommendation because of its low accuracy, you can see comparison in link below:
If you don't want to write everything from scratch, these examples may be useful:
https://www.mathworks.com/help/vision/examples/object-detection-using-deep-learning.html
