Hello, I’m trying to aggregate different Image Datasets together based on some mapping rules between the different datasets.
Example:
train1 = (ImageList.from_csv(path, csv_name=‘train_labels.csv’, folder=‘dataset1/train’)
.split_from_df(col=6)
.label_from_df(cols=1)
.transform(augmentations, size=size, resize_method=ResizeMethod.SQUISH))
train2 = (ImageList.from_csv(path, csv_name=‘train_labels.csv’, folder=‘datset2/train’)
.split_from_df(col=6)
.label_from_df(cols=1)
.transform(augmentations, size=size, resize_method=ResizeMethod.SQUISH))
I have no problem getting the Datasets independently but how do I merge them together. For example, train1 has 100 classes and train 2 has 150 classes but they are all unique classes. So I want labels to train one to be 1-100. and labels of train2 to be 101-250. Something like this.
What is the best way to go about this with the new fastaiv1.0 library?
I would also like to know if both datasets share the same class how will I merged them without having to change the labels values.