Doleus/doleus
Build confidence in your AI with systematic slice-based testing
This tool helps AI practitioners ensure their image-based AI models work reliably across different real-world conditions. You input your image dataset, add contextual information like patient age or weather, and your model's predictions. The tool then identifies specific situations where your model underperforms, such as failing to detect defects on glossy surfaces or missing objects in foggy conditions, so you can make targeted improvements.
Use this if you need to systematically test your image classification or object detection models on specific subsets of your data to uncover hidden failure modes.
Not ideal if your AI model doesn't process images, or if you only need aggregate performance metrics rather than detailed slice-based insights.
Stars
11
Forks
—
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 16, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Doleus/doleus"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations...
holistic-ai/holisticai
This is an open-source tool to assess and improve the trustworthiness of AI systems.
microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment...
datamllab/awesome-fairness-in-ai
A curated list of awesome Fairness in AI resources