crfseg 1.0.0

PyTorch implementation of conditional random field for multiclass semantic segmenation.

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
• Conditional Random Fields as Recurrent Neural Networks

Installation

pip install crfseg

Usage

Can be easily used as differentiable (and moreover learnable) postprocessing layer of your NN for segmentation.

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)