lucasb-eyer/pydensecrf

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2022 stars · 428 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyDenseCRF is a Cython-based Python wrapper around Philipp Krähenbühl's fully-connected (dense) conditional random field implementation with Gaussian edge potentials. It lets Python users add CRF-based post-processing to per-pixel classification tasks such as semantic segmentation.

Use cases

• refine semantic segmentation masks with a dense CRF
• apply fully connected CRF post-processing to image labeling outputs
• smooth noisy per-pixel probability maps in computer vision pipelines
• run efficient CRF inference with Gaussian edge potentials from Python
• combine CNN segmentation outputs with CRF refinement

When to choose

• you need dense/fully-connected CRF inference in a Python computer-vision project
• you want to post-process segmentation or labeling probabilities for sharper boundaries
• you need the classic NIPS 2011 densecrf algorithm with a pip-installable wrapper

When to avoid

• you need a pure-Python solution without C++ compilation
• you need actively developed features or modern deep-learning integration
• you are on Windows and want a frictionless install without conda or build tooling

Facets

library · maturity maintenance

machine-learning computer-vision image-processing computer-vision machine-learning image-processing python cross-platform crf conditional-random-fields cython semantic-segmentation densecrf image-labeling

Member repositories

For agents

markdown · JSON · MCP: product_card(name="lucasb-eyer/pydensecrf")