Hello everyone
I would like to create a classification model with the following characteristics:
- 4 different classification classes
- Each class directory contains several thousand measurement point directories
- Each measuring point directory contains 10 png files of the same size (images that follow each other
like a movie).
I have all the data on my disk
How do I load the data into a dataset ?
Should I use a 3D CNN? LSTM?
How do I do it ?
Thanks for your help!
Hi @Ecostate, Please try to arrange the images in a way that each sub directory contains images corresponding to one class like
main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg
so that you can load the images easily through tf.keras.utils.image_dataset_from_directory( )
.
You can try with different models containing different layers like conv2d, lstm, conv3d and select the model which gives best results. Thank You.