What is Labelbox?

Labelbox is an online platform that helps people label data-like images, text, or video-so computers can learn from it. It provides tools to organize, annotate, and manage that labeled data for machine-learning projects.

Let's break it down

  • Online platform: a website you can use from any computer with internet.
  • Label data: add tags or notes to raw information (e.g., draw a box around a cat in a photo).
  • Images, text, video: the three common types of data that need labeling.
  • Computers can learn: machine-learning models need labeled examples to recognize patterns.
  • Annotation tools: the drawing boxes, highlighting words, or marking timestamps that you use to label.
  • Manage workflow: keep track of who is labeling, how much is done, and quality checks.

Why does it matter?

Without good labeled data, AI models are inaccurate or biased. Labelbox makes the labeling process faster, more organized, and higher-quality, which leads to better AI performance and saves teams time and money.

Where is it used?

  • Self-driving cars: labeling street-view images to teach cars to recognize pedestrians, signs, and lanes.
  • Retail product search: tagging product photos so shoppers can find items by visual similarity.
  • Medical imaging: marking tumors or anomalies in scans to train diagnostic AI tools.
  • Social media moderation: labeling harmful or inappropriate content to improve automated filters.

Good things about it

  • User-friendly interface that works for both technical and non-technical labelers.
  • Built-in quality-control features (review queues, consensus checks).
  • Scalable cloud infrastructure-handles small projects and massive datasets alike.
  • Integration with popular ML frameworks and data storage services.
  • Collaboration tools let teams assign tasks, track progress, and share feedback.

Not-so-good things

  • Subscription pricing can be pricey for startups or hobbyists.
  • Learning curve for advanced workflow customization (e.g., custom scripts).
  • Reliance on internet connection; offline labeling isn’t supported.
  • Some specialized annotation types (3D point clouds, complex video tracking) may need extra plugins or workarounds.