TweetyNet results

This dataset accompanies the eLife publication "Automated annotation
of birdsong with a neural network that segments spectrograms". In the
article, we describe and benchmark a neural network architecture,
TweetyNet, that automates the annotation of birdsong as we describe in the
text. Here we provide checkpoint files that contain the weights of trained
TweetyNet models. The checkpoints we provide correspond to the models that
obtained the lowest error rates on the benchmark datasets used (as
reported in the Results section titled "TweetyNet annotates with low
error rates across individuals and species"). We share these
checkpoints to enable other researchers to replicate our key result, and
to allow users of our software to leverage them, for example to improve
performance on their data by adapting pre-trained models with transfer
learning methods.