Metadata-Version: 2.1
Name: crfseg
Version: 0.2.0
Summary: PyTorch implementation of conditional random field for multiclass semantic segmenation.
Home-page: http://github.com/migonch/crfseg
Author: Mikhail Goncharov, Stanislav Shimovolos
Author-email: goncharov.myu@phystech.edu
License: MIT
Description: # crfseg: CRF layer for segmentation in PyTorch
        
        Conditional random field (CRF) is a classical graphical model which allows to make structured predictions 
        in such tasks as image semantic segmentation or sequence labeling.
        
        You can learn about it in papers:
        * [Efficient Inference in Fully Connected CRFs with
        Gaussian Edge Potentials](https://arxiv.org/pdf/1210.5644.pdf)
        * [Conditional Random Fields as Recurrent Neural Networks](https://arxiv.org/pdf/1502.03240.pdf)
        
        ## Installation
        `pip install crfseg`
        
        ## Usage
        Can be easily used as differentiable (and moreover learnable) postprocessing layer of your NN for segmentation.
        ```angular2html
        import torch
        import torch.nn as nn
        from crfseg import CRF
        
        model = nn.Sequential(
            nn.Identity(),  # your NN
            CRF(n_spatial_dims=2)
        )
        
        batch_size, n_channels, spatial = 10, 1, (100, 100)
        x = torch.zeros(batch_size, n_channels, *spatial)
        log_proba = model(x)
        ```
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
