But there is a lot of transfer learning between classes. For example, the network may learn to detect "broad leaves" or "red stems" for a common plant. It will

But there is a lot of transfer learning between classes. For example, the network may learn to detect "broad leaves" or "red stems" for a common plant. It will then be able to use those feature on less common plants, and require much less training data for them. Transfer learning has been shown to work with just a single image of a desired class. It's also nice that life naturally fits into nested hierarchies because of evolution. So if you can recognize what family it belongs to, then that narrows down the possible sub families it can belong to. That in turn narrows down the possible clades it can belong to, etc, which narrows down the exact species. You couldn't find a more perfect use case for hierarchical softmax!