In the fast-paced world of artificial intelligence and machine learning, high-quality labeled data is everything. Whether you’re training models for computer vision, natural language processing, or audio recognition, you need accurate annotations to get accurate results.
This is where Labelbox comes in.
Labelbox is a popular data labeling platform that allows teams to manage, annotate, and improve their training data efficiently. If you’re new to Labelbox or looking to understand how to use it effectively, this article will walk you through the essentials — from setting up your project to exporting labeled data.
What Is Labelbox?
Labelbox is a web-based training data platform designed to simplify and speed up the data labeling process. It supports image, video, text, and geospatial data. Its core tools include:
- Label Editor – where data is annotated
- Ontology Manager – to define your label classes
- Data Import & Export Tools
- Quality & Review Tools
- API Integration
It’s used by ML teams to collaborate on building datasets for AI training — whether it’s annotating images for object detection or labeling conversations for sentiment analysis.
Step-by-Step Guide: How to Use Labelbox
1. Create an Account and Workspace
To get started:
- Go to Labelbox.com and sign up for an account.
- Choose between a free plan (suitable for individuals or small teams) or an enterprise option.
- Once registered, you’ll be placed into your workspace, which is your central dashboard.
2. Set Up a New Project
Click on “Create New Project”. You’ll need to:
- Name your project.
- Define the type of data you’ll be labeling (e.g., image, text, video).
- Choose or create a labeling ontology — more on that next.
3. Build Your Ontology (Labeling Schema)
Ontology is the structure of your labeling task — think of it as the blueprint.
- Click on Ontology > Add Classification or Tool.
- You can define:
- Classifications (e.g., “positive”, “negative”, “neutral”)
- Tools for object detection (bounding boxes, polygons, etc.)
- Attributes to add more detail to a label
- Classifications (e.g., “positive”, “negative”, “neutral”)
For example, if you’re labeling vehicles in images, your ontology might include tools like bounding boxes and classes like “car”, “truck”, “bike”.
4. Upload Your Data
Labelbox supports multiple upload options:
- Direct Upload – Drag and drop from your local computer
- Cloud Storage Integration – Connect to AWS S3, GCP, or Azure
- API Upload – For developers automating the pipeline
Accepted file types vary depending on the data type (e.g., JPG, PNG for images, JSON for text).
5. Start Labeling
Click on the “Label” tab in your project to access the Label Editor. Here’s what you’ll see:
- The data (image, text, etc.)
- Your tools from the ontology
- Labeling panel to apply annotations
You can now draw bounding boxes, classify text, or segment objects. Labelbox auto-saves your progress.
6. Review and Manage Quality
Once labeling is underway, quality control is critical.
Labelbox allows you to:
- Assign reviewers to verify labels
- Set up consensus scoring to measure agreement between labelers
- Use benchmarks (pre-labeled data) to measure annotator accuracy
You can also track productivity and reassign tasks as needed.
7. Export Your Data
After labeling is complete:
- Go to the Export tab
- Choose your format (JSON, COCO, CSV, etc.)
- Export via UI or use the API for automated workflows
You now have structured, labeled data ready for machine learning training.
Tips for Getting the Most Out of Labelbox
- Use hotkeys to speed up labeling
- Leverage workflows for larger teams to assign roles (labeler, reviewer, admin)
- Integrate your pipeline using Labelbox’s API or SDK (Python)
- Enable model-assisted labeling (only on paid plans) to auto-label simple cases
- Keep ontologies simple — start small and evolve as needed
FAQs: How to Use Labelbox
Q1: Is Labelbox free to use?
Yes, Labelbox offers a free plan with basic features, ideal for individuals or small projects. Paid plans are available for enterprise-level collaboration, automation, and support.
Q2: What types of data can I label in Labelbox?
Labelbox supports images, videos, text, audio, and geospatial data. Each data type has its own set of tools and labeling capabilities.
Q3: Can I collaborate with a team in Labelbox?
Absolutely. Labelbox is built for collaboration. You can invite team members, assign roles (labelers, reviewers), and manage large datasets efficiently.
Q4: Is there an API for automating tasks?
Yes, Labelbox has a powerful Python SDK and REST API that lets you upload data, export labels, and manage projects programmatically.
Q5: How do I ensure labeling quality?
Use features like review queues, consensus scoring, and benchmarking to maintain and monitor labeling accuracy across your team.
Final Thoughts
Labelbox is a powerful tool for machine learning teams that need high-quality labeled data. From simple image annotation to complex multi-label classification, it handles it all. By setting up your project thoughtfully and using built-in quality controls, you’ll ensure your AI models are trained on the best data possible.
Now that you know how to use Labelbox, you’re ready to start labeling smarter and faster.
