kkirchheim/pytorch-ood
👽 Out-of-Distribution Detection with PyTorch
This tool helps machine learning engineers and researchers validate the robustness of their deep neural networks. It takes your trained PyTorch deep learning model and new, potentially unfamiliar data, and then identifies inputs that are 'out-of-distribution' or unexpected. The output is a score for each input, indicating how likely it is to be an outlier, helping you build more reliable AI systems.
335 stars. Available on PyPI.
Use this if you need to ensure your deep learning models are reliable and can effectively flag data that falls outside their trained expertise, rather than making confident but incorrect predictions on novel inputs.
Not ideal if you are looking for a general-purpose anomaly detection tool for non-image or non-deep learning data types, or if you don't work with PyTorch.
Stars
335
Forks
32
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 19, 2026
Commits (30d)
0
Dependencies
5
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/kkirchheim/pytorch-ood"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related frameworks
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
elki-project/elki
ELKI Data Mining Toolkit
raphaelvallat/antropy
AntroPy: entropy and complexity of (EEG) time-series in Python
Minqi824/ADBench
Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.