📜
Developer Documentation
  • 👋EyePop.ai Introduction
  • 🎯Getting Started
    • 👨‍💻Pop Quick Start
    • 💪Low Code Examples
  • 🗝️API Key
  • 🏷️Finding People & Objects
  • SDKs
    • ☁️React/Node SDK
      • Render 2D (Visualization)
      • Composable Pops
    • 🐍Python SDK
      • Composable Pops
  • Self Service Training
    • Dataset SDK (Node)
    • 🏋️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
Powered by GitBook
On this page
  1. Self Service Training
  2. How To Train a Model

Preparing & Uploading Data

A good place to start when creating and training a new model is with a dataset containing at least 500 images. Include diverse lighting, angles, and contexts for real-world applicability. The dataset should also include negative images. Negative images do not include the target object, and are helpful to reduce false positives in your trained model. With a beginning dataset of 500 images, aim for 400 images (or 80%) showing your object, and 100 (20%) negative images.

PreviousExample Use Case: Detecting EyeglassesNextUsing EyePop.ai’s AutoLabeler

Last updated 6 months ago

🏋️