Reopen the app later with lightly-studio gui instead of re-running this script.

September 21, 2026 · View on GitHub

Curate, Annotate, and Manage Your Data in LightlyStudio.

LightlyStudio: an image grid with annotations next to an embedding plot, a cluster selection filtering the grid, and the annotation editor open on one image

The embedding plot shows how images relate to each other, with a preview on hover. A lasso selection filters the grid to one cluster. A search for "coffee" finds a match, and the annotation editor opens to label it.

⚡ Works smoothly with 2M+ images, embeddings included, on a single MacBook (M1, 16GB RAM).

🚀 Try it in 60 seconds

LightlyStudio runs on your computer and opens in your browser. The following command downloads an example dataset with images, annotations, and evaluation results — no account needed.

pip install lightly-studio
lightly-studio quickstart

Local only: your images and datasets never leave your machine.
Your own images: one Python call indexes them, then start the server. See the Image Dataset guide.
Your own videos: see the Video Dataset guide.
No install: open the quickstart in Colab
A guided walkthrough: read the Tutorials below. Curate a Traffic CCTV Dataset for YOLO Training goes from raw images to a trained model.
Runs on Python 3.9 to 3.14 on Windows, Linux, and macOS. Use Python 3.10 for plugin compatibility, for example SAM autolabeling.

Workflows

Image Dataset	Video Dataset	Annotation
Curation	Plugins	Model Evaluation

Example workflows

LightlyStudio is a browser app that runs on your own computer. Load your data into the local database with a Python script, then start the server and explore it in your browser.

Index a COCO dataset

Create a file named 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.load_or_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",
)

# Start the UI server on localhost:8001.
# Pass `host` and `port` parameters to customize it.
# Reopen the app later with `lightly-studio gui` instead of re-running this script.
ls.start_gui()

Run python example_coco.py and open the printed URL to inspect images with their annotations.

To import COCO segmentation masks instead of object detections, pass annotation_type=ls.AnnotationType.SEGMENTATION_MASK to add_samples_from_coco().

Index a folder of images

Create a file named 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")

# Index the images, create embeddings, and store everything in the local database.
dataset = ls.ImageDataset.load_or_create()
dataset.add_images_from_path(
    path=f"{dataset_path}/coco_subset_128_images/images",
)

# Start the UI server on localhost:8001.
# Pass `host` and `port` parameters to customize it.
ls.start_gui()

Run python example_image.py and open the printed URL in your browser.

Other starting points: YOLO datasets, video folders, custom annotations, model evaluation, notebooks, and more in the docs.

📚 Tutorials

Step-by-step guides covering complete workflows — from raw, unlabeled data to a trained and evaluated model:

Curate a Traffic CCTV Dataset for YOLO Training: Explore embeddings, remove near-duplicates, auto-label with a detection plugin, and review annotations before training.
Evaluate YOLO26 on Your Dataset with LightlyStudio: Compare predictions against ground truth, use the confusion matrix and embeddings to find failure patterns, and export issues for relabeling.