Adding images to README file

README.md

@@ -11,7 +11,7 @@ tags:
 
 # Pivaenist
 
-Pivaenist is a two-minute, random piano music generator with a VAE architecture.
+Pivaenist is a random piano music generator with a VAE architecture.
 
 By the use of the aforementioned autoencoder, it allows the user to encode piano music pieces and to generate new ones.
 
@@ -19,9 +19,10 @@ By the use of the aforementioned autoencoder, it allows the user to encode piano
 
 ### Model Description
 
-<!-- Going to include a graph of the VAE, with a description below. -->
-
-
+<figure>
+<img src="https://huggingface.co/TomRB22/pivaenist/resolve/main/.images/architecture.png" style="width:100%">
+<figcaption align = "center"><b>Pivaenist's architecture.</b></figcaption>
+</figure>
 
 - **Developed by:** TomRB22
 - **Model type:** Variational autoencoder
@@ -101,10 +102,13 @@ The first one will clone the repository. Then, fluidsynth, a real-time MIDI synt
 
 ## Training Details
 
-[TODO: SONG MAP IMAGE]
-
 Pivaenist was trained on the [MAESTRO v2.0.0 dataset](https://magenta.tensorflow.org/datasets/maestro), which contains 1282 midi files [check it in colab]. Their preprocessing involves splitting each note in pitch, duration and step, which compose a column of a 3xN matrix (which we call song map), where N is the number of notes and a row represents sequentially the different pitches, durations and steps. The VAE's objective is to reconstruct these matrices, making it then possible to generate random maps by sampling from the distribution, and then convert them to a MIDI file.
 
+<figure>
+<img src="https://huggingface.co/TomRB22/pivaenist/resolve/main/.images/map_example.png" style="width:50%">
+<figcaption align = "center"><b>A cropped example of a song map.</b></figcaption>
+</figure>
+
 ### Training Data
 
 <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->