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LightlyStudio

LightlyStudio is an open-source Python application that runs on a user's computer and opens in a browser, providing tools to curate, annotate and manage machine-learning datasets. Its interface links image samples with embedding visualizations, search and filtering, while its documented workflows also cover video data, plugins and model evaluation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

LightlyStudio — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use LightlyStudio when dataset inspection, curation or annotation is part of a computer-vision workflow, especially when you want to explore embeddings and image samples together in a local browser interface.

Who it is for

  • Computer-vision practitioners preparing image datasets for model development.
  • Machine-learning teams reviewing labels, clusters and evaluation results alongside their data.
  • Developers who want a local browser interface for exploring indexed datasets.

Documented uses

  • Explore image relationships in an embedding plot and locate samples with search.
  • Filter a dataset by selecting a cluster and review the matching images.
  • Open individual images in an annotation editor to add or refine labels.
  • Index and manage image or video data, and review model evaluation results.
Before you choose

Requirements & limits

The README lists Python 3.9–3.14 on Windows, Linux and macOS. It describes the example quickstart as requiring no account and links to a Colab notebook as a no-install route. The project is open source under Apache-2.0.

  • Data is described as staying on the local machine; the workflow loads it into a local database before browser exploration.
  • The primary setup and custom-dataset workflow use Python, while the interface is accessed in a browser.
  • Python 3.10 is suggested for plugin compatibility, with SAM autolabeling given as an example.
  • The README links both image- and video-dataset workflows, while the visual embedding and annotation example focuses on images.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start the example workspace

Install LightlyStudio and open its example dataset in the browser interface.

Before you start

  • Python 3.9–3.14 on Windows, Linux or macOS.
  • A terminal and internet access to install the package and retrieve the example dataset.

Steps

  1. Install the package with the command below.

    Documented command
    pip install lightly-studio

  2. Run lightly-studio quickstart to download and open the example workflow.

Expected outcome

The project describes an example workspace containing images, annotations and evaluation results, opened in a browser.

Guide limitations

  • This quickstart covers the provided example dataset, not ingestion of a custom collection.
  • The README describes this quickstart as requiring no account.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
13 October 2026
Original website title
lightly-ai/lightly-studio · Official homepage title · retained in its source language
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