lightly-studio

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🚀 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 3.10 to 3.14 on Windows, Linux, and macOS. Use Python 3.10 for plugin compatibility, for example SAM autolabeling.

Workflows

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.

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 ingest COCO samples with segmentation masks instead of object detections, pass annotation_type=ls.AnnotationType.SEGMENTATION_MASK to add_samples_from_coco().

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.

A selection of features

Set up a dataset

• Image datasets — index folders, COCO, and YOLO data
• Video datasets — index videos and work with frames
• Cloud storage — read from S3, GCS, or Azure
• Reuse datasets — append to an existing database, or point at a custom path
• Notebooks — run the GUI inside Jupyter or Colab

Work with your data

• Query, filter, and sort — build reusable subsets in Python
• Lightly Query Language — the query syntax for the GUI query editor
• Annotations, Tags, Metadata, Captions, Embeddings
• Sampling — pick the most typical and diverse samples to cut labeling cost
• Model evaluation — compare predictions against ground truth
• Plugins — auto-label with SAM and other models
• Export — write a query back out as COCO or YOLO

🐍 Python Interface

Everything in LightlyStudio is scriptable: index datasets, query and edit samples, sample subsets, and export the result. See the API reference.

🤝 Contribute

We welcome contributions! Please check our issues page for current tasks and improvements, or propose new issues yourself. See CONTRIBUTING.md for development setup and validation instructions.

💬 Contact