= Implementation

== Experiment Setup
% todo
todo setup of experiments, which classes used, nr of samples
kinds of experiments which lead to graphs

== Jupyter

To get accurate performance measures the active-learning process was implemented in a Jupyter notebook first.
This helps to choose which of the methods performs the best and which one to use in the final Dagster pipeline.
A straight forward machine-learning pipeline was implemented with the help of Pytorch and RESNet-18.

Moreover, the Dataset was manually imported with the help of a custom torch dataloader and preprocessed with random augmentations.
After each loop iteration the Area Under the Curve (AUC) was calculated over the validation set to get a performance measure.
All those AUC were visualized in a line plot, see section~\ref{sec:experimental-results} for the results.