Welcome to LightlyStudio!

LightlyStudio is an open-source tool designed to unify your data workflows from curation, annotation and management. Built with Rust for speed and efficiency, it lets you work seamlessly with datasets like COCO and ImageNet, even on a MacBook Pro with an M1 chip and 16 GB of memory.

ExploreAnnotateEmbedding PlotExport

Discover insights instantly with AI-powered search and smart filters. Learn more in Search and Filter.

Create, edit, or remove annotations directly within your dataset.

Visualize your dataset’s structure in the embedding space projected with PaCMAP.

Export selected samples and annotations in your preferred format.

Installation

Ensure you have Python 3.9 to 3.14. We strongly recommend using a virtual environment.

The library is OS-independent and works on Windows, Linux, and macOS.

Linux/macOSWindows

# 1. Create and activate a virtual environment (Recommended)
python3 -m venv venv
source venv/bin/activate

# 2. Install LightlyStudio
pip install lightly_studio

# 1. Create and activate a virtual environment (Recommended)
python -m venv venv
.\venv\Scripts\activate

# 2. Install LightlyStudio
pip install lightly_studio

Quickstart

The examples below download the required example data the first time you run them. You can also directly use your own image, video, or YOLO/COCO dataset.

Image FolderVideo FolderYOLO Object DetectionCOCO Instance Segmentation

1. Create a file named example_image.py with the following contents:

   example_image.py

   import lightly_studio as ls
   
   # Download the example dataset (will be skipped if it already exists)
   dataset_path = ls.utils.download_example_dataset(download_dir="dataset_examples")
   
   # Indexes the dataset, creates embeddings and stores everything in the database.
   dataset = ls.ImageDataset.create()
   dataset.add_images_from_path(
       path=f"{dataset_path}/coco_subset_128_images/images",
   )
   
   # Start the UI server on localhost port 8001.
   # Pass `host` and `port` parameters to customize.
   ls.start_gui()
   

2. Run python example_image.py in your terminal.

3. Click on the printed URL to open the app in your browser.

1. Create a file named example_video.py with the following contents:

   example_video.py

   import lightly_studio as ls
   
   # Download the example dataset (will be skipped if it already exists)
   dataset_path = ls.utils.download_example_dataset(download_dir="dataset_examples")
   
   # Create a dataset and populate it with videos.
   dataset = ls.VideoDataset.create()
   dataset.add_videos_from_path(path=f"{dataset_path}/youtube_vis_50_videos/train/videos")
   
   # Start the UI server.
   ls.start_gui()
   

2. Run python example_video.py in your terminal.

3. Click on the printed URL to open the app in your browser.

1. Create a file named example_yolo.py with the following contents:

   example_yolo.py

   import lightly_studio as ls
   
   # Download the example dataset (will be skipped if it already exists)
   dataset_path = ls.utils.download_example_dataset(download_dir="dataset_examples")
   
   dataset = ls.ImageDataset.create()
   dataset.add_samples_from_yolo(
       data_yaml=f"{dataset_path}/road_signs_yolo/data.yaml",
   )
   
   ls.start_gui()
   

2. Run python example_yolo.py in your terminal.

3. Click on the printed URL to open the app in your browser.

1. Create a file named example_coco.py with the following contents:

   example_coco.py

   import lightly_studio as ls
   
   # Download the example dataset (will be skipped if it already exists)
   dataset_path = ls.utils.download_example_dataset(download_dir="dataset_examples")
   
   dataset = ls.ImageDataset.create()
   dataset.add_samples_from_coco(
       annotations_json=f"{dataset_path}/coco_subset_128_images/instances_train2017.json",
       images_path=f"{dataset_path}/coco_subset_128_images/images",
       annotation_type=ls.AnnotationType.INSTANCE_SEGMENTATION,
   )
   
   ls.start_gui()
   

2. Run python example_coco.py in your terminal.

3. Click on the printed URL to open the app in your browser.

How It Works

• Your Python script creates a LightlyStudio dataset.
• The dataset.add_<samples>_from_<source> functions read your samples and annotations, calculate embeddings, and save metadata to a local lightly_studio.db file (using DuckDB).
• ls.start_gui() starts a local backend API server.
• This server reads from lightly_studio.db and serves data to the UI Application running in your browser (by default http://localhost:8001).
• Images and videos are streamed from their original local folder or remote storage for display in the UI.

For Linux Users

We recommend using Firefox for the best experience with embedding plots, as other browsers might not render them correctly.

Feature Overview

Datasets

• Image Dataset

  

• Video Dataset

  

Concepts

• Annotations

  

• Tags

  

• Captions

  

• Metadata

  

Tools

• Search and Filter

  

• Export

  

• Sampling

  

• Plugins

  

Python API

LightlyStudio has a powerful Python interface. You can not only index datasets but also query and manipulate them using code. It supports local and cloud-hosted image and video folders; see Using Cloud Storage for setup and limitations.