FiftyOne Model Evaluation
From aggregate performance metrics to sample-level diagnostics, FiftyOne allows you to diagnose failure modes and edge cases preventing your models from reaching optimal performance in production.
Model Evaluation
Instantly correlate metrics with data samples
Quickly move between aggregate performance metrics and specific data samples to identify exactly what’s driving model failures or successes.
Built-in evaluation methods
Run analyses on regression, classification, detection, polygon, instance, and semantic segmentation tasks. Access standard aggregate metrics like classification reports, confusion matrices, and precision-recall curves directly within FiftyOne.
Fine-grained, sample-level insights
Go beyond aggregate metrics by capturing detailed statistics like accuracy and false-positive counts at the sample level to reveal annotation errors, training gaps, or data biases.
Model vs. reality
Compare model predictions against ground truth labels directly on your images. Quickly identify where your model excels — and exactly where it needs improvement.
Compare models with scenario analysis
Benchmark models across key metrics and granular data slices to pinpoint performance gaps, identify edge-case failures, and guide targeted model improvements.
Enough data wrangling.
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