Loss Functions Python Packages

auraloss

Collection of audio-focused loss functions in PyTorch

dists-pytorch

IQA: Deep Image Structure and Texture Similarity Metric

focal-loss

TensorFlow implementation of focal loss

torch-log-wmse

logWMSE, an audio quality metric & loss function with support for digital silence target. Useful for training and evaluating audio source separation systems.

torch-l1-snr

Variations of L1 SNR Loss function for training audio source separation machine learning models

neurosketch

A mini supervised learning framework

sirojflow

A mini supervised learning framework

bokbokbok

Custom Loss Functions and Evaluation Metrics for XGBoost and LightGBM

torch-log-wmse-audio-quality

logWMSE, an audio quality metric & loss function with support for digital silence target. Useful for training and evaluating audio source separation systems.

balanced-loss

Easy to use class balanced cross entropy and focal loss implementation for Pytorch

pietoolbelt

Toolbelt for PiePline training pipeline

pytorch-focalloss

PyTorch implementation of binary and multi-class focal loss functions

pylandscape

This project propose the loss landscape analysis as effective methodology to understand the robustness against natural perturbation of QNN.

gedml

Generalized Deep Metric Learning.

thorcino

A minimal, deep-learning framework built from scratch on top of NumPy

iqa-pytorch

Comparison of IQA models in Perceptual Optimization

beta-divergence-metrics

PyTorch implementations of the beta divergence loss.

losshub

LossHub: Loss Functions Library for Image Classification and Detection

comport-ai

An HR predictive analytics tool for forecasting the likely range of a worker’s future job performance using multiple ANNs with custom loss functions.

object-condensation

Efficient implementation of object condensation losses for use in various projects

treeboost-autograd

Easy Custom Losses for Tree Boosters using Pytorch

distortion-residual

Differentiable Distortion Residual Level (DRL) metric for audio processing optimization

mtl-uncertainty-loss

Uncertainty-weighted multi-task loss for PyTorch (Kendall et al. 2018)

idsim

This repository can help researchers who want to use face recognition in their research.