# voxel51/fiftyone

Refine high-quality datasets and visual AI models

Repository: https://github.com/voxel51/fiftyone
Canonical: https://ross.abutalabs.com/products/fiftyone
Homepage: https://fiftyone.ai
Language: TypeScript
License: Apache-2.0
License Family: permissive
Topics: machine-learning, artificial-intelligence, deep-learning, computer-vision, developer-tools, data-science, python, active-learning, data-centric-ai, data-cleaning, data-curation, data-quality, image-classification, object-detection, unstructured-data, vector-search, visualization
Last push: 2026-08-26T22:05:40+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 2324, "days_push": 7, "days_rel": 14, "gap_med": 13, "n_releases_24m": 46}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11042, forks 817 (observed 2026-08-28T04:10:45.946678+00:00)

## What it is
FiftyOne is an open-source Python library and GUI app for building high-quality computer vision datasets and models. It enables visualizing, curating, annotating, and evaluating image, video, and 3D datasets alongside model predictions.

## Use cases
- visualize and explore image and video datasets with labels and predictions
- curate and clean datasets by finding duplicate, mislabeled, or low-quality samples
- evaluate object detection and classification models and analyze failure cases
- send data to annotation backends like CVAT or Label Studio
- run vector similarity search over unstructured visual data
- manage 3D point cloud and LiDAR datasets for autonomous driving
- perform active learning to select the most informative samples for labeling

## When to choose
- you are building or refining computer vision datasets and need visual inspection
- you need to compare model predictions against ground truth and find error patterns
- you want a Python-first workflow with an interactive GUI for data curation
- you work with multimodal data including images, videos, and 3D point clouds

## When to avoid
- you need a fully managed multiuser collaborative platform out of the box (consider FiftyOne Enterprise)
- your project is NLP-only with no visual data
- you need lightweight batch processing without any GUI or visualization

## Facets
- artifact type: library
- maturity: active
- function: data-science, machine-learning, computer-vision, data-visualization, image-processing, video-processing, search-engine, vector-database, developer-tools
- domain: computer-vision, machine-learning, data-science, artificial-intelligence, deep-learning, developer-tools
- platform: python, cross-platform, windows
- tags: data-centric-ai, dataset-curation, model-evaluation, annotation, active-learning, object-detection, image-classification, vector-search, data-quality, visual-ai, point-clouds, 3d-vision, macos, linux, docker

## Member repositories
- voxel51/fiftyone (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:45.946678+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:17:11.585993+00:00, confidence not recorded.
  - readme: https://github.com/voxel51/fiftyone (fetched 2026-08-28T04:10:45.946678+00:00, sha b5d172776ad7)
  - homepage: https://fiftyone.ai (fetched 2026-08-29T08:16:05.433183+00:00, sha 1aa94df1b23f)
  - site_page: https://docs.voxel51.com/ (fetched 2026-08-29T08:16:05.438865+00:00, sha 4633938689da)
  - site_page: https://docs.voxel51.com:443/installation/index.html (fetched 2026-08-29T08:16:05.440748+00:00, sha 66f3ee765f56)
  - site_page: https://docs.voxel51.com:443/installation/environments.html (fetched 2026-08-29T08:16:05.442861+00:00, sha 483288f0cfb6)
  - site_page: https://docs.voxel51.com:443/installation/virtualenv.html (fetched 2026-08-29T08:16:05.445602+00:00, sha cb63c497b19c)
  - site_page: https://docs.voxel51.com:443/installation/upgrading-mongodb.html (fetched 2026-08-29T08:16:05.447565+00:00, sha 3393ce7e5431)
  - site_page: https://docs.voxel51.com:443/installation/troubleshooting.html (fetched 2026-08-29T08:16:05.449295+00:00, sha 12cf27ad7a19)
  - site_page: https://docs.voxel51.com:443/getting_started/annotation/01_quickstart.html (fetched 2026-08-29T08:16:05.451444+00:00, sha 58b076dc052d)
  - site_page: https://voxel51.com/integrations (fetched 2026-08-29T08:16:05.436523+00:00, sha b7f180abfee6)
- Data as of 2026-08-30T08:39:29.467469+00:00.