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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
    • Reports
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  1. Self Service Training
  2. How To Train a Model

Training in Progress

As the model begins training, EyePop.ai’s training system first augments the dataset. This automatically applies transformations such as noise, rotation, blur, color, and position transformation to simulate a larger dataset. After augmentation, the model begins training in earnest, providing real-time progress feedback by graphing a Curve of Confidence.

Model Metrics

The Curve of Confidence is created by graphing Precision on the y-axis versus Recall on the x-axis.

Precision answers the question: did the model find only what I wanted it to find and nothing else? We want to know the model isn’t predicting false negatives.

Recall answers the question: did the model find the object in each scene regardless of whether extra objects were identified? We want to know the model is picking up all instances of the object, even if there are extra boxes.

You can expect a training duration of approximately 10-20 minutes for 500 images, depending on the complexity. Exit the training any time by clicking back to my models in the upper right corner, and come back once it has finished running.

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Last updated 5 months ago

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