Labellerr’s Classification Agent introduces a fully automated, agentic (AI-driven) workflow designed to make classification tasks for images—and beyond—faster, smarter, and more accurate. By leveraging the power of AI agents and batch processing, Labellerr replaces time-consuming manual workflows, minimizing errors and dramatically boosting annotation productivity for images, videos, audio, and text.

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Key Features

  • Batch classification: Processes many images (or files) at once, not one by one.
  • Automatic answers: AI agent analyzes each image and completes multiple classification questions automatically.
  • Template saving: Save and reuse classification setups for future projects.
  • Multi-domain support: Works for images, videos, audio, and text data.
  • Easy review: Annotators simply verify or correct AI suggestions, speeding up final approval.

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How Does the Classification Agent Work?

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1. Set Up Your Project

  • Select your dataset (e.g., a set of vehicle scene images).
  • Define your classification questions:
    • Radio button: Traffic volume (High, Low, Clear Road).
    • Dropdown: Weather condition (Sunny, Cloudy, Rainy, Fog, Snowy).
    • Multi-select: Objects present (Pedestrian, Car, Truck, Bike, Bus).
  • Select your AI agent (e.g., Gemini 2 flash) as the engine for classification.

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2. Save Templates for Reuse

  • Save this classification setup as a template.
  • Instantly reuse it in any current or future project, ensuring consistency and efficiency.

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3. Batch Process with the Agent

  • Initiate the AI agent workflow: The Classification Agent automatically analyzes all images in your dataset.
  • All questions are answered in bulk, not per image.

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4. Review and Verify

  • The review screen shows each image with its AI-generated classification answers.
  • Your only job: accept or correct outputs. No more repetitive manual selections.

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5. Apply to Any Data Type

  • The agentic classification workflow can be adapted for videos, audio, text, and more—making it a universal tool for large datasets across domains.

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Real-World Example

Previously, annotators would view each image, manually judge factors such as traffic volume and weather, mark every item present, and possibly make mistakes or omissions. Now, Labellerr’s Classification Agent performs all this in bulk using AI, so human reviewers simply verify each result. Work that used to take hours or a full day can now be finished in minutes.

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Benefits

  • Faster annotation: Drastically cuts manual effort and completion time.
  • Higher accuracy: Consistent labeling, fewer human mistakes.
  • Effortless review: Focus on quality verification, not repetitive input.
  • Scalable: Suitable for datasets with thousands (or more) files.
  • Versatile: Extendable across modalities (images, videos, audio, text).

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Best Practices

  • Save templates for standard classification flows to reuse across projects.
  • Use the review step to quickly catch and correct outlier results or edge cases.
  • Regularly update your classification questions and AI agent selection as your project’s needs evolve.