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Overview & Quick Start

What is LightlyOne?
Quick Start

Getting Started

Install LightlyOne
Set Up Your First Dataset
Run Your First Selection
Analyze the Selection Results
Export Filenames

Selection

Selection
Examples and Use Cases
Selection Algorithm
- Active Learning Scorers

Providing Data to LightlyOne

Cloud Storage
Local Storage
- Mount Permissions
Work with Metadata
Work with Predictions
- Prediction Format
- Lightly Pretagging

Tutorials

Active Learning Using YOLOv7 and Comma10k
Active Learning Using YOLOv7 for Waste Sorting
Active Learning for Driveable Area Segmentation Using Cityscapes
Active Learning for Transactions of Images
Improving YOLOv8 using Active Learning on Videos
Assertion-based Active Learning with YOLOv8
Active Learning for Sports Applications using Keypoint Detections
Use Similarity Search to Find Similar Samples

Advanced

Videos as Input
Train a Self-Supervised Model
Relevant Filenames
Datapool
Corruption Check
Crop Selection
Sequence Selection
Custom Embeddings
Configuration Options
Outputs
Automation

The LightlyOne Platform

Overview
Core Concepts
Analyze and Filter
Export
Teams

Other Bits

Security
Hardware Recommendations
Known Issues & FAQ
Common Errors That Abort the LightlyOne Worker
Debugging
LightlyOne Worker Installation Guide
Migration guides
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Core Concepts

There are many core concepts of understanding the LightlyOne platform. Head to one of the subpages to read about them!

Datasets
Tags
Samples
Runs
Collaboration
Crop Datasets

Updated about 2 months ago