PerceptiLabs
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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. Advanced
  2. UI Overview

Overview Screen

PreviousData WizardNextModel Training Settings

Last updated 3 years ago

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Key Elements

The Overview screen is where you create and manage your models, and is particularly useful for watching the training statuses when you are working with multiple models.

The listing on the screen shows all datasets – public and local – that have been loaded into PerceptiLabs. You can create as many models as you like for each dataset, and they will be listed under each dataset.

The key elements of the Overview screen are shown here:

  1. Create Project: opens the Data Wizard for you to create a new model. See Load and Pre-process Data.

  2. Selection: selects all models in the list so that you can perform actions on groups of models (e.g., delete multiple models).

  3. Dataset Name: lists the names of each dataset from which models have been created.

  4. Model Name(s): lists the names of each model that uses the parent dataset.

  5. Training Status: lists the current training status of each model:

    • Waiting: training has not yet started, PerceptiLabs is waiting for the model to be run.

    • Training: the model is training; a progress bar will be shown indicating the current progress.

    • Validation: the model is in the validation phase of the training.

    • Paused: the training is paused.

    • Stop: the training was stopped early.

    • Training Complete: the training completely finished.

  6. Duration: the amount of time that the training took to complete.

  7. Test Available: provides the option to test the model if it has been fully trained; it will be empty if the model hasn't been trained yet.

  8. Last Modified: the date when the model was last saved.

  9. Delete: deletes the model(s).

  10. New Model: creates a new model for the specific dataset.

Dataset Popup Menu

You can right click on a dataset which provides the following options:

  1. Delete: Removes the dataset from the overview screen. If the dataset is local, the dataset will remain on your local machine. If the dataset is public, it will be removed from your local storage (you can always reload it from the Data Wizard). If there are existing models that have been created from that dataset, you will be prompted to delete/unregister each of them (see Model Popup Menu below for more information) before you can delete the dataset.

Model Popup Menu

You can right click on a existing model under a dataset in the list to display the following options:

  1. Open: Opens the model for editing in the Modeling Tool.

  2. Rename: Renames the model.

  3. Delete: Removes the model from the Overview and permanently deletes it from your local storage. You may want to ensure that you have a backup of the model before using this option, in case you want to recover it at a later date.

  4. Unregister: Removes the model from the Overview but retains the model on your local storage.

🛠️
  • Key Elements
  • Dataset Popup Menu
  • Model Popup Menu