What's the right way to setup an image classifier by multiple params? I'm very new to the data science and machine learning, so apologies for my ignorance. 
What I'm trying to understand is how to setup an image classifier system (maybe based on CNN) which will classify my image by multiple params. Most of the examples I found are about classifying by single class, i.e. "cat", "dog", "horse", etc, but what I'd like to have is, for example, {"red", "dog", "tongue"}. Is there a simple way to do it? The best option would be to have a ready setup, so I can just change their test dataset with mine and see the right formatting. Thanks!

UPDATE:

Also please help me understand if it's a complicated task for an experienced machine learning engineer? What'd be the timing and cost given a dataset?
You normally one hot encode your labels so that every possible attribute gets it's own binary representation. So if you have 10 attributes they would be represented as [attr1, attr2, attr3, ..., attr10], with values either 1 if the attribute is present, or 0 if it is not. 

Then you train a network with the same number of output neurons as possible categories and use a sigmoid activation function.