keras-unet-collection 0.1.9

The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.

keras-unet-collection

The tensorflow.keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.

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keras_unet_collection.models contains functions that configure keras models with hyper-parameter options.

• Pre-trained ImageNet backbones are supported for U-net, U-net++, UNET 3+, Attention U-net, and TransUNET.
• Deep supervision is supported for U-net++, UNET 3+, and U^2-Net.
• See the User guide for other options and use cases.

keras_unet_collection.models	Name	Reference
unet_2d	U-net	Ronneberger et al. (2015)
vnet_2d	V-net (modified for 2-d inputs)	Milletari et al. (2016)
unet_plus_2d	U-net++	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+	Huang et al. (2020)
transunet_2d	TransUNET	Chen et al. (2021)
swin_unet_2d	Swin-UNET	Hu et al. (2021)

Note: the Swin-UNET implementation is experimental, and is not compared to the official repository of Hu et al. (2021).

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keras_unet_collection.base contains functions that build the base architecture (i.e., without model heads) of Unet variants for model customization and debugging.

keras_unet_collection.base	Notes
unet_2d_base, vnet_2d_base, unet_plus_2d_base, unet_3plus_2d_base, att_unet_2d_base, r2_unet_2d_base, resunet_a_2d_base, u2net_2d_base, transunet_2d_base, swin_unet_2d_base	Functions that accept an input tensor and hyper-parameters of the corresponded model, and produce output tensors of the base architecture.

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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
dice	Dice loss	Sudre et al. (2017)
tversky	Tversky loss	Hashemi et al. (2018)
focal_tversky	Focal Tversky loss	Abraham et al. (2019)
ms_ssim	Multi-scale Structural Similarity Index loss	Wang et al. (2003)
iou_seg	Intersection over Union (IoU) loss for segmentation	Rahman and Wang (2016)
iou_box	(Generalized) IoU loss for object detection	Rezatofighi et al. (2019)
triplet_1d	Semi-hard triplet loss (experimental)
crps2d_tf	CRPS loss (experimental)

Installation and usage

pip install keras-unet-collection

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

• Note: Currently supported backbone models are: VGG[16,19], ResNet[50,101,152], ResNet[50,101,152]V2, DenseNet[121,169,201], and EfficientNetB[0-7]. See Keras Applications for details.

• Note: Neural networks produced by this package may contain customized layers that are not part of the Tensorflow. It is reommended to save and load model weights.

Keras models created by this package may contain customized layers that are not

• Jupyter notebooks are provided as examples:

  • Attention U-net with VGG16 backbone [link].

  • UNET 3+ with deep supervision, classification-guided module, and hybrid loss [link].

  • Vision-Transformer-based examples are in progress, and available at keras-vision-transformer

• Changelog

Dependencies

• TensorFlow 2.5.0, Keras 2.5.0, Numpy 1.19.5.

• (Optional for examples) Pillow, matplotlib, etc.

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