Still feeling a bit new to the world of neural networks. I am working with a CNN model right now (working with Keras), and would like to train it to identify certain types of objects from a dataset. I have read through a number of walk-throughs and tutorials on the subject, but I am having conceptual issues in understanding the key component of what it means to set up your training and test data.
In many of the walk-throughs (cat or dog/mnst/etc.), there is always a portion in the keras tutorials where training data is downloaded from the
datasets package, and part of that are the
from keras.datasets import cifar10 (train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
I understand in these examples, the labels are already applied. But at the risk of highlighting how much more I need to learn, how would this be done with real-world data? I have datasets of numerous images, but how does one provide
labels to my training data? I thought this was unsupervised and as such, this wouldn't need to be done.