How to correctly label images for multi-label classification? I have images that contain lots of elements. Some I know, some I don't. I want to know if it's ok to only label those I do know. Let's take this image for example. I would label the green stuff and the worm but leave the rest unlabeled.
Is that ok?

Another question I would also like to ask is how concise I should be in labeling. For instance, You can see in the picture a bit of blue behind the green plant. So should I label that bit and say water or leave it unlabeled?

EDIT:

I also want to ask if it's ok to label only the things I'm interested in even if they take up to  30% of the picture? Won't the neural network be confused by all the details in the picture that it perceives and that I label as A for example, even if A is just a part of it?

Another question would be, let's say I have labels A, B and C. I have an image in which I'm a bit confused if a certain object is of label B or A or even a totally different class other than (A,B,C). What should I do in this instance?

I'm having a hard time with the dataset. It would take an expert to label this correctly. But I want to do things as cleanly as possible, so all the effort doesn't go to waste. I would really appreciate your help. Thank you guys.
You can't label things you don't know.  The goal of labeling is to label the things you want the classifier to learn so that when you run it in inference mode you can discover what is in your data (new data that you didn't use for training, validating, or testing).

It is not a good idea to label small objects like the 'blue water' unless it is important to you to discover these fine details in inference mode.