PerceptiLabs
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v0.13
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  • Welcome
  • ⚡Getting Started
    • Quickstart Guide
      • Requirements
      • Installation
      • Video Tutorials
    • Load and Pre-process Data
    • Build Models
    • Train Models
    • Evaluate Models
    • Export and Deploy Models
    • Manage, Version, and Track Models
  • 🙏How to Contribute
    • Datasets
    • Models
    • Components
  • 👨‍🍳Tutorials
    • Basic Image Recognition
    • Basic Image Segmentation
  • 🛠️Advanced
    • Common Testing Issues
    • Common Training Issues
    • Components
      • Input and Target
      • Processing
      • Deep Learning
      • Operations
      • Custom
    • CSV File Format
    • Debugging and Diagnostic Features
    • How PerceptiLabs Works With TensorFlow
    • Included Packages
    • Types of Tests
    • UI Overview
      • Data Wizard
      • Overview Screen
      • Model Training Settings
      • Modeling Tool
      • Training View
      • Evaluate View
      • Deploy View
    • Using the Exported/Deployed Model
  • 💡Use Cases
    • General
      • A Guide to Using U-Nets for Image Segmentation
      • A Voice Recognition model using Image Recognition
    • Environmental
      • Automated Weather Analysis Using Image Recognition
      • Wildfire Detection
    • Healthcare & Medical
      • Brain Tumor Detection
      • Breast Cancer Detection
      • Classifying Chest X-Rays to Detect Pneumonia
      • Classifying Ways to Wear a Face Mask
      • Detecting Defective Pills
      • Highlighting Blood Cells in Dark-field Microscopy Using Image Segmentation
      • Ocular Disease Recognition
      • Retinal OCT
      • Skin Cancer Classification
    • Industrial IoT & Manufacturing
      • Air Conditioner Piston Check
      • Classifying Fruit
      • Classifying Wood Veneers Into Dry and Wet
      • Defect Detection in Metal Surfaces
      • Fabric Stain Classification
  • 📖Support
    • FAQs
    • Changelog
  • Code of Conduct
  • Marketing Site
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  1. Getting Started

Train Models

PreviousBuild ModelsNextEvaluate Models

Last updated 3 years ago

If you have a model ready, you can easily train it inside PerceptiLabs.

Training means to update the weights and biases of your model so that it learns to make predictions. A second phase known as validation, then checks the accuracy of the trained model on new data. PerceptiLabs trains and validates your model using the data you allocated in your Dataset Configuration Settings for training and validation.

Read on to learn how to train a model in PerceptiLabs.

Training a Model

Follow the steps below to train your model:

1) Click Run in the Modeling Tool:

2) (Optional) Adjust the training settings to customize how your model trains:

3) Click Run Model. If you've run the model before, PerceptiLabs will ask if you want to continue your training or start from scratch:

PerceptiLabs will then navigate to the Training View where you can view real-time statistics about your models training and validation performance:

Training Multiple Models in Parallel

You can optionally train other models in parallel as per the steps below:

1) Navigate to the Modeling Tool during training and select a different model (or select a different model from the Overview screen if no other model is open).

2) Click Run in the Modeling Tool for the new model you selected in Step 1. The Training View will display a new tab for the model and being training that model. You can navigate between the models during training by clicking on their tabs:

If you want to learn more about what to watch out for during training, see Common Training Issues.

⚡
  • Training a Model
  • Training Multiple Models in Parallel