Comments (1)
Hi,
Thanks for opening the issue. Let me answer in order.
- I've already provide some guidelines on how to support custom datasets. You can refer to issues #3 and #5 for that. If you have more specific questions, I'm happy to help, but please elaborate your ask.
- There is no cap on the maximum number of classes the model can work with. Though, when you get to the order of thousands it is generally better to work with metric-learning-based losses (such as the ArcFace loss I mention in the README), instead of the standard classification-based losses (such as cross-entropy).
Alessio.
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