pytorch-loss

Loss functions

Provides a comprehensive set of implementation of various loss functions and operators for deep learning models

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

GitHub

2k stars
23 watching
374 forks
Language: Python
last commit: almost 2 years ago
amsoftmaxcudadice-lossemafocal-losslabel-smoothinglovasz-softmaxmishpartial-fcpytorchtriplet-loss

Related projects:

RepositoryDescriptionStars
hanxunh/active-passive-lossesA PyTorch-based framework for implementing normalized loss functions to improve deep learning model robustness against noisy labels.134
alanchou/truncated-lossAn implementation of a loss function designed to improve the training of deep neural networks with noisy labels126
mblondel/fenchel-young-lossesProvides Fenchel-Young losses for probabilistic classification in PyTorch/TensorFlow/scikit-learn.183
bes-dev/mpl.pytorchA PyTorch implementation of a loss function used in semantic image segmentation175
kefirski/pytorch_neg_lossA PyTorch implementation of negative sampling loss for text classification models125
rachtsingh/lgammaImplementations of mathematical special functions for use in machine learning and PyTorch applications24
chosj95/mimo-unetDevelops a deep learning model for single image deblurring with improved performance and computational efficiency382
kaiyangzhou/dassl.pytorchA PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision.1,236
pochih/fcn-pytorchA Python implementation of fully convolutional networks for semantic segmentation in computer vision.409
zhanghang1989/pytorch-encodingA Python framework for building deep learning models with optimized encoding layers and batch normalization.2,044
seannaren/warp-ctcPyTorch bindings for the Warp-CTC loss function used in speech recognition.757
unsky/focal-lossAn implementation of focal loss for dense object detection in mxnet.486
yunlongdong/fcn-pytorchA PyTorch implementation of FCN for semantic segmentation with an easy-to-use interface and pre-trained models.161
zudi-lin/pytorch_connectomicsA deep learning framework for automatic and semi-automatic segmentation of 3D image stacks in connectomics172
zapata-engineering/orqvizVisualizes loss landscapes of parameterized quantum algorithms86