AI Studio
  • AI Studio Guide
  • AI Studio Basics
    • About AI Studio
      • AI Studio Feature Guide
      • Problem Statements
      • Platform Use Cases
    • Key Terminology to Know
  • Building Your First Project
    • Image Tagging
  • Detailed Guide: Image Tagging
    • Your Data
      • Supported Formats & Image Specifications
      • Exploring Datasets
        • Creating New Datasets
        • Adding Data
        • Removing Data
      • Pre-Processing Results
    • Creating and Training Models
      • Training Basics
    • Evaluating Models
      • Default Evaluation Dataset
      • Interpreting Evaluation
        • Interpreting results
      • Improving Your Model
    • Inference
    • Deployment
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  1. Detailed Guide: Image Tagging

Evaluating Models

Evaluate fashion AI models to understand performance, accuracy and errors before deployment.

How it works?

Taking a data-driven, iterative approach to machine learning, AI Studio enables users to build fashion AI models that are high-performing, accurate and robust. To this end, the platform offers a range of evaluation metrics. Users, taking advantage of the analytics, insights and suggestions, can strengthen the model before deploying it in their production environment.

Further, users can evaluate the performance of multiple models within a project and deploy the most robust model.

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Last updated 3 years ago

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