Add tutorial for multilabel training#254
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Jul 25, 2024
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Hey @PariaValizadeh,
Thanks for your work. To get the most out it for our readers, I would suggest the following changes:
- Load the data with our
BirdSetDataModulewithout any preprocessing so that it outputs batched waveforms - Pick the first sample of the batch from the train and test set (this will show the difference of focal / soundscapes)
- Make the sounds audible
- Now, add preprocessing to train a EfficientNet (EfficientNet because we can upload a pretrained model for that, and we included the EfficientNet in BirdSet), make sure to use the same preprocessing config as done in
configs/experiment/birdset_neurips24/HSN/DT/efficientnet.yaml - Visualize the first sample (now a spectrogram)
- Train the model (with same configs) and add the option to download the pretrained model
- Run test with
trainer.test() - Run model on previously selected test sample
- Print the predicted classes and the target class.
| "source": [ | ||
| "## load the test dataset\n", | ||
| "from datasets import load_dataset\n", | ||
| "hsn_test = load_dataset(\"DBD-research-group/BirdSet\",\"HSN\", split=\"test\")" |
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@PariaValizadeh This will not use the cached version and therefore requires a complete redownload.
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Step 6: Visualization" |
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@PariaValizadeh Could you move the visualization before the model training? So that the reader sees how the data gets preprocessed by the birdset pipeline?
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This PR adds a tutorial notebook to guide practictioners how to train a ResNet model for multilabel classification on the HSN dataset.