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.

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 two Transformer models are incompatible with Numpy 1.20; NumPy 1.19.5 is recommended.

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.

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.
Changelog

Examples

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

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 <yingkai@eoas.ubc.ca> <yingkaisha@gmail.com>

License

MIT License

Citation

Sha, Y., 2021: Keras-unet-collection. GitHub repository, accessed 4 September 2021, https://doi.org/10.5281/zenodo.5449801

@misc{keras-unet-collection,
  author = {Sha, Yingkai},
  title = {Keras-unet-collection},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/yingkaisha/keras-unet-collection}},
  doi = {10.5281/zenodo.5449801}
}

basharbme/keras-unet-collection