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.
FiftyOne allows you to visualize model predictions against ground truth labels

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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