Factual package intelligence from PyPI

imbalanced-losses 0.5.0 package intelligence

imbalanced-losses is a PyTorch library of training losses for class-imbalanced classification and ranking-metric optimization — Focal Loss, plus differentiable surrogates for Average Precision (Smooth-AP), Recall-at-Quantile, and Partial-AUC-at-Budget — with built-in DDP all-gath

pip install imbalanced-losses

Current version
0.5.0
Python requirement
>=3.10
License
MIT
Distribution type
Pure Python wheel
Release files
2 (1 wheels, 1 source)
Download size
732.1 KiB
Release history
11 releases with files
Median release cadence
12 days

imbalanced-losses dependencies

PyPI declares 3 unique dependency rules for this release. Environment markers are shown when supplied by the project.

  1. torch >=2.8.0

Compatibility and release files

imbalanced-losses publishes 1 wheel and 1 source archive for version 0.5.0. Wheel platform tags: any.

No version-specific Python classifiers are declared.

Security snapshot for imbalanced-losses 0.5.0

An OSV query completed on and found 0 known vulnerabilities affecting this version. 0 advisories are classified as critical. 0 advisories appear in the CISA Known Exploited Vulnerabilities catalog.

Metadata completeness

PyPI does not currently declare: project URL. PyDeps marks these fields as unknown instead of guessing values.

Release activity and sources

PyPI lists 11 releases with files. The first dated release is ; 11 releases fall within the 365 days preceding the latest dated release. The current release files were uploaded on . The preceding dated release was .

The project does not publish external project URLs in its PyPI metadata.