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Download this comprehensive guide for measuring and improving computer vision models for accuracy, explainability, and bias.
Automatic out-of-distribution detection lets you identify where your model is likely making mistakes.
Explore the results of your CV 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.
Accuracy & Data Drift
Monitor CV model pipelines for data anomalies using built-in out-of-distribution detection and track the accuracy of bounding box models
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
Detect biases in your CV models by evaluating image classification outputs using an interactive interface and locating where your models misclassify and perpetuate bias
Our team has many models in production. With Arthur, checking their output and status at a glance is easy.
– Chris Poirier, VP of Data at Truebill
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