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.

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

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}
}