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.

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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.

Explore model evaluation

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.

Claude responded: FiftyOne confusion matrices and a radar chart comparing YOLOv8 and R-CNN across mAP, precision, recall, F1 score, and IoU.FiftyOne confusion matrices and a radar chart comparing YOLOv8 and R-CNN across mAP, precision, recall, F1 score, and IoU.

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.

Claude responded: FiftyOne prediction inspection on an aerial running track scene, with runner detections and a panel showing label, confidence, and false positive evaluation.FiftyOne prediction inspection on an aerial running track scene, with runner detections and a panel showing label, confidence, and false positive evaluation.

Product Demo

See model evaluation in action

Quickly move between aggregate performance metrics and specific data samples to identify exactly what’s driving model failures or successes.

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.

Explore model predictions

$FiftyOne detecting a clavicle fracture on a shoulder X-ray, with predicted and ground truth bounding boxes shown for model evaluation.$

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.

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FiftyOne evaluation summary table comparing two models on IoU, precision, recall, and F1-score over a fruit detection dataset.

Enough data wrangling. 
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