#3636th of 75 in ML frameworks and libraries
fiftyone
Dataset visualization, curation and model evaluation tool for computer vision
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- Stars
- 11.2k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- TypeScript
10,446 on 31 Mar 202611,209 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
FiftyOne is a Python library with a browser-based App for exploring image, video and 3D datasets together with their labels. It supports 2D and 3D annotation, an embeddings panel, model evaluation to find failure modes, and a Brain module for finding data and annotation issues. It integrates with PyTorch, Hugging Face and Ultralytics, and can be extended with plugins.
Who it is for: Computer vision engineers curating datasets and evaluating models
Strengths
- Installs with pip install fiftyone; supports Python 3.10 to 3.13
- Built-in dataset zoo and model zoo for quick starts
- Embeddings panel and evaluation tools to locate failure modes and label errors
- Plugin system to extend the App and workflows
Weaknesses
- Team collaboration and cloud-native workloads are in the separate commercial Voxel51 product
- Focused on computer vision; not aimed at text or LLM workflows
- Video datasets need FFmpeg installed separately
- RAM and GPU requirements are not stated in the README
What it needs
- GPU optional
- Docker
- Needs Python 3.10-3.13, MongoDB (fiftyone-db package; self-managed instance optional), FFmpeg (for video), Node.js and Yarn (source install only)
- Models: PyTorch, Hugging Face, Ultralytics
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| 2 | pytorchPython tensor library with GPU support and tape-based autograd for deep learning | 94 out of 100 |
| 3 | ultralyticsPython library and CLI for training and running YOLO vision models | 92 out of 100 |
| #4 | pytorch-image-modelsPyTorch library of image models, pretrained weights and reference training scripts | 91 out of 100 |
| #5 | supervisionPython toolkit for loading, annotating and counting computer vision detections | 90 out of 100 |