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) |
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], andEfficientNetB[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.
-
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
-
-
TensorFlow 2.5.0, Keras 2.5.0, Numpy 1.19.5.
-
(Optional for examples) Pillow, matplotlib, etc.
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.
Yingkai (Kyle) Sha <[email protected]> <[email protected]>
- 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}
}
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