Image Classification
Detect failure modes, fix mislabels, and benchmark model reliability
Prism’s automated image classification tests help you uncover hidden blind spots, validate data quality, and continuously improve your model’s accuracy across every version.
Key Capabilities
Find Hidden Failure Clusters
Identify hidden failure clusters where the model underperforms.
Detect Labeling Errors
Flag mislabelled or inconsistent samples using automated quality checks.
Monitor Performance Drift
Track performance drift and compare versions with regression insights.
Tests
An overview of all tests
Failure Mode Analysis
Identify specific image scenarios where the Image Classification model underperforms, despite overall acceptable performance metrics
This helps in collecting more data for the under-represented cluster to improve model training or fine-tuning the model with specific emphasis on the failing cluster.
Labelling Quality Test
Identify mislabelled images within the dataset to ensure high-quality training data for our image classification model.
This would help reduce the risk of model bias based on inconsistent or inaccurate labelling and save time and resources by identifying and correcting labelling errors before deploying the model.
