{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"Computes geometric loss functions for comparing point clouds, images, and volumes using optimal transport and kernel methods, built on PyTorch and NumPy.","skillfed_tags":["optimal-transport","geometric-learning","pytorch-integration"],"use_cases":["Training generative models (GANs, VAEs) with geometric loss instead of pixel-space or feature-space losses.","Comparing 3D point clouds in computer vision or robotics applications.","Computing optimal transport distances between probability measures in statistical learning.","Shape matching and registration tasks in medical imaging or geometry processing.","Differentiable metric learning where standard distances are too rigid."],"what_it_does":"Geomloss implements geometric loss functions for comparing structured data like point clouds, images, and volumetric data using optimal transport theory and kernel methods. It wraps PyTorch tensors to compute distances and divergences between distributions, making it useful for tasks that need differentiable, geometrically meaningful loss metrics rather than standard Euclidean or statistical measures.\n\nThe package is designed for machine learning and scientific computing workflows where you need to optimize over geometric structures. It depends on numpy, scipy, and torch, so it integrates naturally into PyTorch-based pipelines. The code is marked pre-alpha but actively maintained, indicating it is still evolving but usable for research and production work.","worth_installing":"Yes, if you are working with PyTorch and need geometric or optimal-transport-based loss functions. The pre-alpha status and recent maintenance suggest active development; the low install friction and permissive license make it a low-risk addition. Install only if you have a specific use case for optimal transport or kernel-based metrics\u2014it is not a general-purpose library."},"id":"geomloss","links":{"html":"https://skillfed.io/packages/geomloss","md":"https://skillfed.io/packages/geomloss.md","pypi":"https://pypi.org/project/geomloss/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-05","license_spdx":"MIT","license_treatment":"permissive","name":"geomloss","python_support":"supports_current","summary":"Geometric loss functions between point clouds, images and volumes."},"popularity":{"monthly_downloads":158195,"position":10731,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.1"}