gpleiss/temperature_scaling

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1175 stars · 164 forks observed · 2026-08-28

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

A small PyTorch module implementing temperature scaling, a post-processing technique that calibrates the overconfident probability outputs of trained neural networks. It learns a scalar temperature parameter on a validation set to minimize negative log-likelihood.

Use cases

• calibrate neural network confidence scores
• fix overconfident softmax probabilities in pytorch
• implement temperature scaling for a trained classifier
• reduce expected calibration error of a model
• make model probabilities match true accuracy

When to choose

• you need a minimal, copy-paste implementation of temperature scaling for a PyTorch model
• you want to reproduce the 'On Calibration of Modern Neural Networks' paper
• you're okay maintaining the code yourself since it's a single small module

When to avoid

• you need a maintained, packaged calibration library (the repo is explicitly unmaintained and targets PyTorch 0.3)
• you need calibration methods beyond temperature scaling
• you're not using PyTorch

Facets

library · maturity abandoned

machine-learning deep-learning deep-learning machine-learning python temperature-scaling model-calibration pytorch confidence-calibration post-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="gpleiss/temperature_scaling")