Metadata-Version: 2.1
Name: highresnet
Version: 0.3.5
Summary: PyTorch implementation of HighResNet
Home-page: https://github.com/fepegar/highresnet
Author: Fernando Perez-Garcia
Author-email: fernando.perezgarcia.17@ucl.ac.uk
License: UNKNOWN
Description: # HighRes3DNet
        
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        [![PyPI version](https://badge.fury.io/py/highresnet.svg)](https://badge.fury.io/py/highresnet)
        [![DOI](https://zenodo.org/badge/195385893.svg)](https://zenodo.org/badge/latestdoi/195385893)
        
        PyTorch implementation of HighRes3DNet from [Li et al. 2017,
        *On the Compactness, Efficiency, and Representation of
        3D Convolutional Networks: Brain Parcellation as a Pretext Task*][li].
        
        A 2D version (`HighRes2DNet`) is also available.
        
        [li]: https://arxiv.org/pdf/1707.01992.pdf
        
        ## Installation
        
        ### [PyTorch Hub](https://pytorch.org/hub)
        
        If you are using the nightly version of PyTorch, you can import the model
        directly from this repository using [PyTorch Hub](https://pytorch.org/hub).
        
        ```python
        >>> import torch
        >>> repo = 'fepegar/highresnet'
        >>> model_name = 'highres3dnet'
        >>> print(torch.hub.help(repo, model_name))
        
                "HighRes3DNet by Li et al. 2017 for T1-MRI brain parcellation"
                "pretrained (bool): load parameters from pretrained model"
            
        >>> model = torch.hub.load(repo, model_name, pretrained=True)
        ```
        
        ### [PyPI](https://pypi.org/)
        
        ```shell
        $ pip install highresnet
        ```
        
        ```python
        >>> from highresnet import HighRes3DNet
        >>> model = HighRes3DNet(in_channels=1, out_channels=160)
        ```
        
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Description-Content-Type: text/markdown
