From c02e04f45f958b6333b03c7f430c7c425289fe2e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?N=C3=A9stor=20N=C3=A1poles=20L=C3=B3pez?= Date: Sat, 5 Feb 2022 20:57:05 -0500 Subject: [PATCH] Update README.md --- README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 99ca4b92..5aeb9c00 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,3 @@ -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/napulen/AugmentedNet/blob/main/notebooks/AugmentedNet.ipynb) - # AugmentedNet A Roman Numeral Analysis Network with Synthetic Training Examples and Additional Tonal Tasks @@ -31,7 +29,10 @@ N. Nápoles López, M. Gotham, and I. Fujinaga, "AugmentedNet: A Roman Numeral A ## Try out the pre-trained network -Clone, create a virtual environment, and get the `python` dependencies +[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/napulen/AugmentedNet/blob/main/notebooks/AugmentedNet.ipynb) + + +Clone, create a virtual environment, and get the `python` dependencies. ```bash git clone https://github.com/napulen/AugmentedNet.git @@ -110,7 +111,7 @@ The trained model is available in: .model_checkpoint/debug/testexperiment-220101 You can use that trained model for inference, using the same workflow shown above. -## Introduction +## About the AugmentedNet ### The neural network architecture