Skip to main content

PyTorch implementation of conditional random field for multiclass semantic segmenation.

Project 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:

Installation

pip install crfseg

Usage

Can be easily used as differentiable (and moreover learnable) postprocessing layer of your NN for segmentation. It is adaptive to a number of input's spatial dimensions.

import torch
from crfseg import MeanFieldCRF

model = nn.Sequential(
    nn.Identity(),  # your NN
    MeanFieldCRF()
)

batch_size, n_channels, spatial = 10, 1, (100, 100)
x = torch.zeros((batch_size, n_channels, *spatial))
log_proba = model(x)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for crfseg, version 0.1.3
Filename, size File type Python version Upload date Hashes
Filename, size crfseg-0.1.3-py3-none-any.whl (7.1 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size crfseg-0.1.3.tar.gz (5.5 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page