The tensorflow.keras implementations of U-net, U-net++, Residual U-net, Attention U-net.
Project description
keras-unet-collection
This repository contains tensorflow.keras
implementations of U-net, U-net++, R2U-net, Attention U-net, ResUnet-a:
keras_unet_collection.models |
Reference |
---|---|
U-net/Unet | Ronneberger et al. (2015) |
U-net++/Unet++ | Zhou et al. (2018) |
R2U-Net | Alom et al. (2018) |
Attention U-net | Oktay et al. (2018) |
ResUnet-a | Diakogiannis et al. (2020) |
These models are implemented with user friendly key words, including optional network depth, hidden layer activations and batch normalization. Examples refers to the user guide.
Additional activation layers and loss functions are also provided:
keras_unet_collection.activations |
Reference |
---|---|
Gaussian Error Linear Units (GELU) | Hendrycks et al. (2016) |
Snake activation | Liu et al. (2020) |
keras_unet_collection.losses |
Reference |
---|---|
Tversky loss | Hashemi et al. (2018) |
Focal Tversky loss | Abraham et al. (2019) |
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(...)
-
Jupyter notebooks are provided as user guides.
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 that benefit the reconstruction of high-resolution, gridded outputs.
Contact
Yingkai (Kyle) Sha yingkai@eoas.ubc.ca
License
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