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
2k stars
23 watching
374 forks
Language: Python
last commit: 4 months ago amsoftmaxcudadice-lossemafocal-losslabel-smoothinglovasz-softmaxmishpartial-fcpytorchtriplet-loss
Related projects:
Repository | Description | Stars |
---|---|---|
| A PyTorch-based framework for implementing normalized loss functions to improve deep learning model robustness against noisy labels. | 134 |
| An implementation of a loss function designed to improve the training of deep neural networks with noisy labels | 126 |
| Provides Fenchel-Young losses for probabilistic classification in PyTorch/TensorFlow/scikit-learn. | 183 |
| A PyTorch implementation of a loss function used in semantic image segmentation | 175 |
| A PyTorch implementation of negative sampling loss for text classification models | 125 |
| Implementations of mathematical special functions for use in machine learning and PyTorch applications | 24 |
| Develops a deep learning model for single image deblurring with improved performance and computational efficiency | 382 |
| A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. | 1,236 |
| A Python implementation of fully convolutional networks for semantic segmentation in computer vision. | 409 |
| A Python framework for building deep learning models with optimized encoding layers and batch normalization. | 2,044 |
| PyTorch bindings for the Warp-CTC loss function used in speech recognition. | 757 |
| An implementation of focal loss for dense object detection in mxnet. | 486 |
| A PyTorch implementation of FCN for semantic segmentation with an easy-to-use interface and pre-trained models. | 161 |
| A deep learning framework for automatic and semi-automatic segmentation of 3D image stacks in connectomics | 172 |
| Visualizes loss landscapes of parameterized quantum algorithms | 86 |