/asc_triplet

triplet loss on Acoustic Scene Classification-PyTorch

Primary LanguageJupyter NotebookMIT LicenseMIT

Triplet loss on Acoustic Scene Classification(ASC)-PyTorch

PyTorch implementation of triplet networks for learning embeddings.

Triplet networks are useful to learn mapping from input to a compact Euclidean space where distances correspond to a measure of similarity.

Installation

Requires pytorch 0.4 with torchvision 0.2.1

Code structure

  • data_manager folder

    • Please see the data_manager/README.md for details.

  • networks.py

    • some network classes. e.g. vggish_bn. it's VGG-like network architecture.

  • losses.py

    • OnlineTripletLoss class -triplet loss for triplets of embeddings.

  • metrics.py

    • Sample metrics that can be used with fit function from trainer.py

  • trainer.py

    • fit - unified function for training a network with different number of inputs and different types of loss functions.

  • utils.py

    • FunctionNegativeTripletSelector class -generating triplets based on embeddings and ground truth class labels.
    • plot_embeddings, extract_embeddings are function of learned embeddings visualization.

  • experiment folder

    • classification_baseline.py - A baseline of classification code.
    • hard_triplet_baseline.py - A random hard selection of triplets baseline code.

  • jupyter_script

    • some experiment results.

TODO

  1. change network architecture.
  2. novel tripets selection strategy
  3. pickup classifier.
  4. verification on embedding metric.