Loss Function Implementations ML Frameworks
Implementations and comparisons of custom loss functions for machine learning models across different frameworks and domains. Does NOT include general model training frameworks, evaluation metrics libraries, or domain-specific applications.
There are 32 loss function implementations frameworks tracked. 7 score above 50 (established tier). The highest-rated is adobe/antialiased-cnns at 66/100 with 1,681 stars and 17,044 monthly downloads.
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| # | Framework | Score | Tier |
|---|---|---|---|
| 1 |
adobe/antialiased-cnns
pip install antialiased-cnns to improve stability and accuracy |
|
Established |
| 2 |
HelmchenLabSoftware/Cascade
Calibrated inference of spiking from calcium ΔF/F data using deep networks |
|
Established |
| 3 |
lucidrains/RIM-pytorch
Implementation of Recurrent Independent Mechanisms in Pytorch |
|
Established |
| 4 |
orchardbirds/bokbokbok
Custom Loss Functions and Evaluation Metrics for XGBoost and LightGBM |
|
Established |
| 5 |
kimhc6028/relational-networks
Pytorch implementation of "A simple neural network module for relational... |
|
Established |
| 6 |
devsisters/pointer-network-tensorflow
TensorFlow implementation of "Pointer Networks" |
|
Established |
| 7 |
KaiyangZhou/pytorch-center-loss
Pytorch implementation of Center Loss |
|
Established |
| 8 |
kyegomez/LiqudNet
Implementation of Liquid Nets in Pytorch |
|
Emerging |
| 9 |
vandit15/Class-balanced-loss-pytorch
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective... |
|
Emerging |
| 10 |
fcakyon/balanced-loss
Easy to use class balanced cross entropy and focal loss implementation for Pytorch |
|
Emerging |
| 11 |
Hsuxu/Loss_ToolBox-PyTorch
PyTorch Implementation of Focal Loss and Lovasz-Softmax Loss |
|
Emerging |
| 12 |
richardaecn/class-balanced-loss
Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019 |
|
Emerging |
| 13 |
seorim0/DCCRN-with-various-loss-functions
DCCRN with various loss functions |
|
Emerging |
| 14 |
kahnchana/opl
Official repository for "Orthogonal Projection Loss" (ICCV'21) |
|
Emerging |
| 15 |
sniklaus/softmax-splatting
an implementation of softmax splatting for differentiable forward warping... |
|
Emerging |
| 16 |
abhuse/polyloss-pytorch
Polyloss Pytorch Implementation |
|
Emerging |
| 17 |
gan3sh500/local-relational-nets
A Pytorch implementation for the paper Local Relational Networks for Image... |
|
Emerging |
| 18 |
statmlben/ensLoss
EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in... |
|
Emerging |
| 19 |
neuronflow/blob_loss
blob loss example implementation |
|
Experimental |
| 20 |
isaaccorley/Making-Convolutional-Networks-Shift-Invariant-Again-Tensorflow
Tensorflow Implementation of BlurPool the Antialiasing Pooling operation... |
|
Experimental |
| 21 |
g0lemXIV/LambdaNetworks
Implementation of LambdaNetworks, a framework for capturing long-range... |
|
Experimental |
| 22 |
richzhang/antialiased-cnns
Repository has been moved: https://github.com/adobe/antialiased-cnns |
|
Experimental |
| 23 |
aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-PyTorch
This repository compares the performance of 8 different regression loss... |
|
Experimental |
| 24 |
stabgan/awesome-loss-functions
A comprehensive, chronologically ordered collection of loss functions across... |
|
Experimental |
| 25 |
glanceable-io/ordinal-log-loss
Repository of the COLING 2022 paper : Ordinal Log-Loss - A simple log-based... |
|
Experimental |
| 26 |
sararahman1729/Custom-Loss-Function
I did this project during my Neural network and pattern recognition course. |
|
Experimental |
| 27 |
Subkash2206/spectral-aliasing-cnns
A spectral analysis framework for studying aliasing in strided CNNs. Defines... |
|
Experimental |
| 28 |
anwai98/Loss-Functions
Different Loss Function Implementations in PyTorch and Keras |
|
Experimental |
| 29 |
mlnjsh/Loss-Functions-In-Detail
📐 Deep dive into Loss Functions — MSE, MAE, Cross-Entropy, Huber, Focal,... |
|
Experimental |
| 30 |
csinva/mouse-brain-decoding
Decoding images from calcium recordings using data from stringer et al. 2018. |
|
Experimental |
| 31 |
kyegomez/nebula
1 Loss Function For Everything |
|
Experimental |
| 32 |
nataliarodriguez-uc/auc-opt
Constructing new surrogate loss for optimizing pairwise difference metrics... |
|
Experimental |