Preprint: https://arxiv.org/abs/2403.09598
We introduce a framework that leverages mixing regularization methods Mixup, Manifold Mixup, and MultiMix to handle multi-label and class imbalance on the Anuraset dataset.
We base our code on the official implementation of AnuraSet baseline: https://github.com/soundclim/anuraset where you can find the link to download the dataset AnuraSet
Python libraries required : torch, torchmetrics, numpy, pandas, tqdm
main.py
: main code for training and evaluating on AnuraSet
dataset.py
: dataset class
models.py
: code for MobileNetV3 model
train.py
: train utility functions
val.py
: evaluation utility functions
transforms.py
: transformation classes
args.py
: argparse of the arguments
python3 main.py --rootdir dataset_path --mix mix2 --device 'cuda' --sr 16000 --workers 16 --save
@misc{2403.09598,
Author = {Ilyass Moummad and Nicolas Farrugia and Romain Serizel and Jeremy Froidevaux and Vincent Lostanlen},
Title = {Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds},
Year = {2024},
Eprint = {arXiv:2403.09598},
}