– Github
README describing proper methods for constructing a dataset.
According to the README Regularization images allow for prior preservation of a models existing dataset.
- Regularization images should be generated before training from the base model.
- Image classification should be a generic term ( i.e. person, cat, dog, man, woman ).
- Most experiments suggest 200 to 300 regularization images per class improve training accuracy.
- In order to include classification in a data set directory naming convention is as follows: <number of repeats>_<data keyword> <class keyword>.
- Different data keywords can be used for a single class allowing for varied training data within a class. For example training two sets of images of different characters.
- Important Note: Previous experiments have shown that without regularization images, or using images not generated by a model, training data will overwrite a model’s data damaging the models ability to generate images other than the training data.
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