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trajo123 t1_je7dgjz wrote

100 images??? Folks, neural nets are data hungry, if you don't have reams of data, don't fiddle with architectures, definitely not at first. The first thing to do when data is limited is to use pre-trained models. Then do data augmentation and only then look at other things like architectures and losses if you really have nothing better to do with your time.

SMP offers a wide variety of segmentation models with the option to use pre-trained weights.

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viertys OP t1_je9mjxr wrote

Thank you a lot! I will try SMP

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Tight-Lettuce7980 t1_jea4ojl wrote

How about medical images, which are more difficult to obtain due to privacy issues? I don't think it's easy to get for example 1000+ images. Would 300 - 700 or so be sufficient?

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trajo123 t1_jebbxaf wrote

Sufficient to train a model from scratch? Unlikely. Sufficient to fine-tune a model pre-trained on 1million+ images (imagenet, etc)? Probably yes. As mentioned, some extra performance can be squeezed out with some smart data augmentation.

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