Data Annotation Tool Analysis – How to Use LabelMe

“Data Annotation Tool Analysis – How to Use LabelMe”

Table of Contents

  • LabelMe General Introduction
  • Data Annotation Tool Comparison
  • LabelMe Analysis 
    • User Interface
    • Workflow
    • Output Format
  • How to Use LabelMe

LabelMe – General Introduction

      1. Description: A web-based open graphical image annotation tool (Github Location: https://github.com/wkentaro/labelme)

      2. Price: Free

      3. Functionalities:

  • upports image annotation for polygon, rectangle, circle, line and point, and also image flag annotation for classification and cleaning.
  • The format is JSON

      4. Project management:

  • It has virtually no project management properties but it does allow an easy way to import and visualize annotations and correct them if necessary.
  • The simple offline interface makes the annotation process pretty fast, even though it does not support many hotkey shortcuts.

       5. Advantages:

  • Stable and easy to use, you can access the tool from anywhere and people can help you to annotate your images without them having to install or copy a large dataset onto their computers
  • Users could create custom functions with html and JavaScript
  • You could extract segmentation masks

      6. Disadvantages:

  • Doesn’t support team coordination
  • Doesn’t support real-time annotation performance monitoring and quality check
  • Need to distribute and collect statistics manually, and it increases operational cost

Comparison with Other Annotation Tools

LabelMe – User Interface

LabelMe Workflow

LabelMe – Output Format

Step 1: Dataset Preparation

Split your data

Split your dataset into 3 Folders, namely “Training”, “Validation” and “Test”

Step 2: Class Name Preparation

Type all the Class Names (Labels) to be annotated in the “Labels.txt” file

  • The “Labels.txt” file comes with the installation of LabelMe
  • Keep “__ignore__” and “background” classes unchanged as the first and second
  • When naming the Classes, avoid using “-” as the “-” mark will be later used to distinct instances.

Fire up with User Interface using the following command

  • LabelMe [–labels labels.txt] [directory | file]

Step 3: Do Annotation

Press “Create Polygons” button then start drawing

Step 4: Name the Polygon

Pick Class Name from your predefined Class Name list

To create instance segmentation, you could manually add an instance ID after the Class Name

Step 5: Edit Polygon

To edit the shapes you created, you could click “Edit” Button.

Step 6: Save

When you have finished annotating all objects listed in “Label List” in the image, click “Save” to save .json file.

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We offer industry leading image annotation service at low cost, high efficiency, and short feedback loop so you get the images you need on time for your world changing applications.

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