keras-unet-collection 0.0.7

The Tensorflow, Keras implementation of U-net, U-net++, R2U-net, Attention U-net, ResUnet-a, U^2-Net, and UNET 3+.

keras-unet-collection

The tensorflow.keras implementation of U-net, U-net++, R2U-net, Attention U-net, ResUnet-a, U^2-Net, and UNET 3+.

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keras_unet_collection.models contains functions that configure keras models with user-specific hyper-parameter options, including network depth, hidden layer activations and batch normalization for all the U-net variants, and deep supervision for U-net++, U^2-Net and UNET 3+. See the User guide for more details.

keras_unet_collection.models	Name	Reference
unet_2d	U-net/Unet	Ronneberger et al. (2015)
unet_plus_2d	U-net++/Unet++	Zhou et al. (2018)
r2_unet_2d	R2U-Net	Alom et al. (2018)
att_unet_2d	Attention U-net	Oktay et al. (2018)
resunet_a_2d	ResUnet-a	Diakogiannis et al. (2020)
u2net_2d	U^2-Net	Qin et al. (2020)
unet_3plus_2d	UNET 3+/Unet+++	Huang et al. (2020)

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keras_unet_collection.backbones contains functions that build the backone of Unet variants for model customization and debugging.

keras_unet_collection.backbones	Notes
unet_2d_backbone, unet_plus_2d_backbone, r2_unet_2d_backbone, att_unet_2d_backbone, resunet_a_2d_backbone, u2net_2d_backbone, unet_3plus_2d_backbone	Functions that accept an input tensor and hyper-parameters of the corresponded model, and produce output tensors of the backbone.

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keras_unet_collection.activations and keras_unet_collection.losses provide additional activation layers and loss functions.

keras_unet_collection.activations	Name	Reference
GELU	Gaussian Error Linear Units (GELU)	Hendrycks et al. (2016)
Snake	Snake activation	Liu et al. (2020)

keras_unet_collection.losses	Name	Reference
tversky	Tversky loss	Hashemi et al. (2018)
focal_tversky	Focal Tversky loss	Abraham et al. (2019)
crps2d_tf	CRPS loss (experimental)

Dependencies

• TensorFlow 2.3.0

• Keras 2.4.0

• Numpy 1.18.2

Installation and usage

pip install keras-unet-collection

from keras_unet_collection import models
# e.g. models.unet_2d(...)

• Note: Because of the changable hyper-parameter options, neural networks produced by this package may not be compatible with other pre-trained models of the same name. Training from scratch is recommended.

• Jupyter notebooks are provided as user guides.

• Changelog

Overview

U-net is a convolutional neural network with encoder-decoder architecture and skip-connections, loosely defined under the concept of "fully convolutional networks." U-net was originally proposed for the semantic segmentation of medical images and is modified for solving a wider range of gridded learning problems.

U-net and many of its variants take three or four-dimensional tensors as inputs and produce outputs of the same shape. One technical highlight of these models is the skip-connections from downsampling to upsampling layers, which benefit the reconstruction of high-resolution, gridded outputs.

Contact

Yingkai (Kyle) Sha <[e-mail]> <[e-mail]>

License

MIT License