Voxel51
Similarity seek for high quality management
As soon as you discover one problematic annotation, similarity search turns into a strong instrument to seek out all associated errors. Click on on a mislabeled pattern and immediately retrieve probably the most related pictures to verify if they’ve the identical systematic labeling downside.
FiftyOne’s similarity search transforms “discover extra like this” from guide tedium into prompt discovery. Index your knowledge set as soon as, then immediately retrieve visually related samples by way of point-and-click or programmatic queries.
import fiftyone as fo
import fiftyone.mind as fob
import fiftyone.zoo as foz
# Load dataset
dataset = foz.load_zoo_dataset("quickstart")
# Index pictures by similarity
fob.compute_similarity(
dataset,
mannequin="clip-vit-base32-torch",
brain_key="img_sim"
)
# Kind by most probably to include annotation errors
mistake_view = dataset.sort_by("mistakenness", reverse=True)
# Question the primary pattern and discover 10 most related pictures
query_id = mistake_view.take(1).first().id
similar_view = dataset.sort_by_similarity(query_id, ok=10, brain_key="img_sim")
# Launch App to view related samples and for point-and-click similarity search
session = fo.launch_app(dataset)
Key capabilities embody prompt visible search by way of the App interface, object-level similarity indexing for detection patches, and scalable again ends that swap from sklearn to Qdrant, Pinecone, or different vector databases for manufacturing.
