Predictions run on CPU hardware. Predictions typically complete within 88 seconds. The predict time for this model varies significantly based on the inputs.
This demo runs transfer learning models to estimate music approachability and engagement using effnet-discogs embeddings.
We include three model types, providing different outcome formats: three-class and binary classification and regression with continuous values.
These classifiers were trained on in-house MTG datasets.
effnet-discogs is an EfficientNet architecture trained to predict music styles for 400 of the most popular Discogs music styles.
Our models consist of single-hidden-layer MLPs trained on the considered embeddings.