Best Way to combine multiple datasets into one model I want to make a multilabel image classification model that can detect many different labels. For each label, I can get at least 5000 positive examples and 5000 negative examples. However, my question is how can I use data in this format to train a multilabel image classifier. Part of the challenge is, for example, I can download 10,000 images of a hand and know that they are positive examples but then if I want to detect a shoe as well, I don’t know how many of those hand photos might have also had a shoe in them. I’m trying to make a model this way because I will have a fairly high amount of labels and need to be continuously adding new labels. What is the best way to go about doing this?
Why don't you give Multi-label classification a try.you can actually train your model to predict multiple labels for one image by training the model on a dataset that has images with multiple labels the only problem you might face here is to where to get the data from you can actually use this dataset for practice:
https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data.

Another better option that you have is as you said in your question you want to train your model on multiple classes you can actually use Imagenet dataset it mas 1000 classes and more than a million images.
Note:Use tranfer learning to build your model and do watch lesson 1,2,3 from fast.ai part1 2019 course you will have all your answers. Jeremy Howard is a god!!!!