Automate quality gates
Replace subjective manual review with deterministic quality gates. Automated checks are the only way to catch systematic errors like schema violations or class imbalance that human reviewers inevitably miss at scale.
from fiftyone import ViewField as F
# Find bounding boxes that are impossibly small
tiny_boxes = dataset.filter_labels(
"ground_truth",
(F("bounding_box")[2] * F("bounding_box")[3]) < 0.01
)
# Find samples where the model disagrees with ground truth
possible_errors = dataset.match(F("mistakenness") > 0.8)
# Schema Validation: Find detections missing required attributes
incomplete_labels = dataset.filter_labels(
"ground_truth",
F("occluded") == None
)
Maintain annotation provenance
Track curation decisions and annotation metadata to support iterative improvement. This provenance enables sophisticated analysis of which curation strategies yield the best model improvements and supports continuous workflow optimization.
# Grab the "most unique" sample from a curated view of unique smaples
most_confusing_sample = unique_view.first()
# Add sample-level provenance
most_confusing_sample.tags.append("curated_for_review")
# Set metadata on the specific labels (detections)
if most_confusing_sample.detections:
for det in most_confusing_sample.detections.detections:
det["annotator"] = "expert_reviewer"
det["review_status"] = "validated"
most_confusing_sample.save()
A unified platform for curation-driven workflows
Voxel51’s flagship open source computer vision platform, FiftyOne, provides the necessary tools to curate, annotate, and evaluate AI models. It provides a unified interface for data selection, QA, and iteration.
