Metadata-Version: 2.1
Name: keras-unet-collection
Version: 0.0.15
Summary: The Tensorflow, Keras implementation of U-net, V-net, U-net++, R2U-net, Attention U-net, ResUnet-a, U^2-Net, and UNET 3+ with optional ImageNet-trained backbones.
Home-page: https://github.com/yingkaisha/keras-unet-collection
Author: Yingkai (Kyle) Sha
Author-email: yingkaisha@gmail.com
License: UNKNOWN
Description: # keras-unet-collection
        
        [![PyPI version](https://badge.fury.io/py/keras-unet-collection.svg)](https://badge.fury.io/py/keras-unet-collection)
        [![PyPI license](https://img.shields.io/pypi/l/keras-unet-collection.svg)](https://pypi.org/project/keras-unet-collection/)
        [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/yingkaisha/keras-unet-collection/graphs/commit-activity)
        
        The `tensorflow.keras` implementation of U-net, V-net, U-net++, R2U-net, Attention U-net, ResUnet-a, U^2-Net, and UNET 3+ with optional ImageNet 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++, Attention U-net, and UNET 3+.
        * Deep supervision is supported for U-net++, UNET 3+, and U^2-Net.
        * See the [User guide](https://github.com/yingkaisha/keras-unet-collection/blob/main/examples/user_guid_models.ipynb) for other options and use cases.
        
        | `keras_unet_collection.models` | Name | Reference |
        |:---------------|:----------------|:----------------|
        | `unet_2d`      | U-net           | [Ronneberger et al. (2015)](https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28) |
        | `vnet_2d`      | V-net (modified for 2-d inputs) | [Milletari et al. (2016)](https://arxiv.org/abs/1606.04797) |
        | `unet_plus_2d` | U-net++         | [Zhou et al. (2018)](https://link.springer.com/chapter/10.1007/978-3-030-00889-5_1) |
        | `r2_unet_2d`   | R2U-Net         | [Alom et al. (2018)](https://arxiv.org/abs/1802.06955) |
        | `att_unet_2d`  | Attention U-net | [Oktay et al. (2018)](https://arxiv.org/abs/1804.03999) |
        | `resunet_a_2d` | ResUnet-a       | [Diakogiannis et al. (2020)](https://doi.org/10.1016/j.isprsjprs.2020.01.013) |
        | `u2net_2d`     | U^2-Net         | [Qin et al. (2020)](https://arxiv.org/abs/2005.09007) |
        | `unet_3plus_2d` | UNET 3+        | [Huang et al. (2020)](https://arxiv.org/abs/2004.08790) |
        
        ----------
        
        ` 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`, `r2_unet_2d_base`, `att_unet_2d_base`, `resunet_a_2d_base`, `u2net_2d_base`, `unet_3plus_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)](https://arxiv.org/abs/1606.08415) |
        | `Snake` | Snake activation                     | [Liu et al. (2020)](https://arxiv.org/abs/2006.08195) |
        
        | `keras_unet_collection.losses` | Name | Reference |
        |:----------------|:----------------|:----------------|
        | `tversky`       | Tversky loss                   | [Hashemi et al. (2018)](https://ieeexplore.ieee.org/abstract/document/8573779) |
        | `focal_tversky` | Focal Tversky loss             | [Abraham et al. (2019)](https://ieeexplore.ieee.org/abstract/document/8759329) |
        | `crps2d_tf`     | CRPS loss (experimental)       | |
        
        # Installation and usage
        
        ```pip install keras-unet-collection```
        
        ```python
        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](https://keras.io/api/applications/) for details. 
        
        * **Note**: This package is planned for major updates. for versions prior to 0.1, backward compatibility is not ensured.
        
        * **Note**: Because of the changable hyper-parameter options, neural networks produced by this package may not be compatible with other pre-trained models of the same name. Training from scratch is recommended.
        
        * Jupyter notebooks are provided as [examples](https://github.com/yingkaisha/keras-unet-collection/tree/main/examples):
        
          * Attention U-net with VGG16 backbone [[link]](https://github.com/yingkaisha/keras-unet-collection/blob/main/examples/human-seg_atten-unet-backbone_coco.ipynb).
          
          * UNET 3+ with deep supervision and classification guided module [[link]](https://github.com/yingkaisha/keras-unet-collection/blob/main/examples/segmentation_unet-three-plus_oxford-iiit.ipynb).
        
        * [Changelog](https://github.com/yingkaisha/keras-unet-collection/blob/main/CHANGELOG.md)
        
        # Dependencies
        
        * TensorFlow 2.3.0, Keras 2.4.0, Numpy 1.18.2.
        
        * (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](https://github.com/yingkaisha/keras-unet/blob/main/LICENSE)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
