Automatic out-of-distribution detection lets you identify where your model is likely making mistakes.
Explore the results of your vision models (classification and object detection) with an interactive interface that makes it easy to identify issues.
Visualize important image regions that are impactful for model predictions.
Want a demo of how Arthur can enable you to build better AI models and detect fraud more effectively? Understand how to build an explainable fraud detection system to save time, reduce costs, and increase trust with explainable AI tools and proper AI model governance.
As computer vision technology has grown more sophisticated and computational power has become more available, companies have increasingly adopted computer vision models to augment and automate critical processes. The adoption of computer vision into industry applications promises enormous potential upside; however, computer vision models, like any ML model, must be carefully monitored. A promising model that has gone off the rails can quickly become a dangerous liability.
In this whitepaper, we lay out several aspects of computer vision models that are important for users to understand and demonstrate how Arthur’s product offers simple solutions to these pressing problems.