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FiftyOne

FiftyOne is an open source toolkit for building high-quality datasets and computer vision models. It provides an API to create LanceDB tables and run similarity queries, both programmatically in Python and via point-and-click in the App.

example

Basic recipe

The basic workflow shown below uses LanceDB to create a similarity index on your FiftyOne datasets:

  1. Load a dataset into FiftyOne.

  2. Compute embedding vectors for samples or patches in your dataset, or select a model to use to generate embeddings.

  3. Use the compute_similarity() method to generate a LanceDB table for the samples or object patches embeddings in a dataset by setting the parameter backend="lancedb" and specifying a brain_key of your choice.

  4. Use this LanceDB table to query your data with sort_by_similarity().

  5. If desired, delete the table.

The example below demonstrates this workflow.

Note

Install the LanceDB Python client to run the code shown below. 

pip install lancedb


import fiftyone as fo
import fiftyone.brain as fob
import fiftyone.zoo as foz

# Step 1: Load your data into FiftyOne
dataset = foz.load_zoo_dataset("quickstart")

# Steps 2 and 3: Compute embeddings and create a similarity index
lancedb_index = fob.compute_similarity(
    dataset, 
    model="clip-vit-base32-torch",
    brain_key="lancedb_index",
    backend="lancedb",
)
Once the similarity index has been generated, we can query our data in FiftyOne by specifying the brain_key:

# Step 4: Query your data
query = dataset.first().id  # query by sample ID
view = dataset.sort_by_similarity(
    query, 
    brain_key="lancedb_index",
    k=10,  # limit to 10 most similar samples
)

# Step 5 (optional): Cleanup

# Delete the LanceDB table
lancedb_index.cleanup()

# Delete run record from FiftyOne
dataset.delete_brain_run("lancedb_index")

For a much more in depth walkthrough of the integration, visit the LanceDB x Voxel51 docs page.