Identify Anomalies & Data Drift
Automatic out-of-distribution detection lets you identify where your model is likely making mistakes.
Improve Your Models and Explore Your Datasets
Explore the results of your vision models (classification and object detection) with an interactive interface that makes it easy to identify issues.
Understand Your Models Better
Visualize important image regions that are impactful for model predictions.
Explainability and Monitoring for Your CV Models
Monitor CV model pipelines for data anomalies using built-in out-of-distribution detection and track the accuracy of bounding box models.
Detect biases in your CV models by evaluating image classification outputs using an interactive interface and locating where your models misclassify and perpetuate biases.
Visualize which regions of an image are impactful for an image classification model’s decision or how your object detection models are performing on pipeline images.
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.