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Developer Documentation
  • πŸ‘‹EyePop.ai Introduction
  • 🎯Getting Started
    • πŸ‘¨β€πŸ’»Pop Quick Start
    • πŸ’ͺLow Code Examples
  • πŸ—οΈAPI Key
  • 🏷️Finding People & Objects
  • SDKs
    • ☁️React/Node SDK
      • Render 2D (Visualization)
    • 🐍Python SDK
  • Self Service Training
    • Dataset SDK (Node)
    • πŸ‹οΈHow To Train a Model
      • Defining Your Computer Vision Model
      • Example Use Case: Detecting Eyeglasses
      • Preparing & Uploading Data
      • Using EyePop.ai’s AutoLabeler
      • Human Review
      • Data Augmentation Setup
      • Training in Progress
      • Deployment
        • Deploy to Windows Runtime
      • Previewing Results
      • Iterative Training
      • Deep Dives (FAQ)
  • EyePop.ai Visual Intelligence
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  1. Self Service Training
  2. How To Train a Model

Defining Your Computer Vision Model

EyePop.ai supports three model types:

  • Detection

    • Locates and identifies objects in an image. Example: Detecting eyeglasses in photos.

  • Classification

    • Categorizes images into defined labels. Example: Identifying whether an image shows a dog or a cat.

  • Segmentation

    • Outlines and isolates objects from backgrounds. Example: Determining the precise area taken up by a product.

When choosing and preparing your model, it’s important to clearly define the problem you aim to solve and ensure it’s well-scoped for the model type chosen. Are you detecting an object, condition, or feature? What are your specific attributes of interest (i.e. size, position, or appearance)? In the example use case that follows, the problem we aim to solve is identifying images of glasses in various contexts, which we accomplish using a Detection model type.

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Last updated 5 months ago

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