Hello folks, I am trying to convert images generated in to a dataset.(All I ahve is just png images in n folders and there is no label or meta data)
This is what I aspire to do:
1.I am using torch audio to convert audio formats to Mel spectrogram and save the images as Png format. Status:done
2.Now I have ānā number of folders(classes) with images so I am curious if I could convert the newly generated images into data and target as in normal datasets, so that I can use sklearn to do the test train splits sklearn.model_selection.train_test_split.
Status:not done
eg: fetch mnist dataset
> ds_mnist = sklearn.datasets.fetch_openml(
> data_id=554,
> as_frame=False
> )
Split data and target in to X and y
dataset_X = ds_mnist .data.astype('float32') dataset_y = ds_mnist .target.astype('int64')
Blockquote