Imagine you have a big pile of puzzle pieces that you want to put together to make a picture. But instead of working on the puzzle all by yourself, you have a group of friends who are helping you out.
Now, let's relate this to computer vision and the concept of batch size. When the computer is learning from lots of pictures, it's like solving a big puzzle made up of many tiny pieces (data points). The batch size is like deciding how many puzzle pieces you want to work on at once.
If you choose a small batch size, it's like working with just a few puzzle pieces at a time. You and your friends can really focus on those pieces, making sure they fit together perfectly. But it might take a bit longer to finish the entire puzzle because you're only working on a small part of it at a time.
On the other hand, if you choose a larger batch size, it's like trying to put together more pieces in one go. This might speed things up because you're making progress on multiple parts of the puzzle simultaneously. However, with more pieces to deal with at once, it could be a bit trickier to make sure everything fits together perfectly.
So, the batch size is all about how many pictures the computer looks at and learns from in one step. Just like in our puzzle analogy, there's a balance to strike between working with a small batch for precision and a larger batch for speed.