Migrate from Labelbox to Labellerr

Labelbox is a cloud-based platform for labeling data for various AI applications, including computer vision, natural language processing, and more. It has features like active learning and collaboration tools.

Features of LabelBox:

Diverse annotation: Labelbox supports a wide range of annotation types, including bounding boxes, polygons, points, text, and audio tagging. This allows us to tackle various AI tasks, from object detection to sentiment analysis.

User-Friendly Interface: Labelbox provides a user-friendly interface that allows users to efficiently label various types of data, including images, videos, and text.

Collaboration and Team Management: Labelbox allows multiple users to collaborate on a single project. It has features like team management, including user roles and permissions, task assignment, and activity tracking, which improves overall productivity.

Data Management: Labelbox provides capabilities for organizing and managing large datasets, including data versioning and data visualization. These features make it easy to track and manage annotated data.

In this blog, we will use LabelBox to make simple object detection annotations on images. We are going to use a wheat leaf disease dataset from the Kaggle website. The dataset contains two diseases: septoria and stripe rust.

Table of Contents

1. Image Annotation Using LabelBox
2. Migration
3. Challenges of Migration
4. How Does Labellerr Overcome These Challenges?
5. Why Migrate from LabelBox to Labellerr?
6. Frequently Asked Questions

Image Annotation using LabelBox

1) Sign up at https://app.labelbox.com. using your email ID and password.

2) Select the annotate option from the left menu and click on New Project at the top.

3) For this tutorial, we will be using the image datatype. After selecting the project type, name your project, optionally enter a description, and then select the type of data you will be labeling. Select benchmark for quality setting.

4) In the overview tab, select Add Data and add your dataset for annotation.

5) After adding the data, select all the images and put them in the queue to start processing them.

6) Next, we need to create an ontology for our project. We will be using the standard image editor for this tutorial and creating two objects, Stripe-Rust and Septoria.

7) Click on Start Labeling to start annotating your dataset. Use the annotation tools provided by Labelbox to annotate objects in the images.

8) After annotating images, review all the images and save them. Once annotations are completed, go to the Exports tab and export them to the desired format.

Congratulations! You have completed the annotation using LabelBox.

Migration

Migration from one platform to another means exporting data from the current platform, converting it to a compatible format, and importing it onto the new platform while preserving data and functionalities without any loss of data.

It consists of a series of steps, including evaluating the features and benefits of the new platform, planning the transition process, and ensuring data integrity.  It may also require users to reconfigure some tasks, set up user accounts and permissions, and adjust workflows.

The goal of migration is to leverage improved performance enhancements, scalability options, and an overall better user experience offered by the new platform.

Challenges of Migration

Migration from one annotation platform to another can present several challenges:

1. Data Format: Many platforms often use their formats to store annotated data. Migrating data from one platform to another may require converting annotations from the original format to a format compatible with the new platform.
2. Annotation Consistency: Different annotation platforms have different annotation tools like rectangles, polygons, etc. Maintaining consistency in annotations during migration is important to maintain the quality and accuracy of the dataset.
3. User Training and Familiarization: Migrating to a new annotation platform requires users to learn tools, interfaces, and workflows.
4. Customization and configuration: Annotation platforms provide users with different customization options, like defining annotation tasks, labeling interfaces, and project workflows. Migrating these custom configurations to new platforms requires a lot of effort.

How does Labellerr overcome these challenges?

1) Labellerr supports importing data from various formats commonly used in annotation tasks, like JSON and CSV. It also supports various data types like image, text, audio, etc.

2) Labellerr provides a wide range of annotation tools, including polygons, keypoints and more ensuring consistency in annotations regardless of the original format.

3) Labellerr prioritizes user-friendly interfaces and has intuitive workflows to minimize the learning curve for new users. It also provides customer support to facilitate user training and familiarization with the platform.

4)Labellerr offers customization options that allow the user to define annotation tasks, create custom labeling interfaces, and configure project workflows according to their specific need.

Labeller's comprehensive feature set, data format compatibility, annotation consistency, user-friendly interface, and customization capabilities allow it to overcome the challenges related to migrating annotation data and workflows from other platforms.

Why migrate from LabelBox to Labellerr?

There could be several reasons why someone might consider migrating their existing annotating projects from Label Studio to Labellerr, including feature enhancements, ease of use, performance improvements, and the overall user experience.

The old platform may no longer meet the team's growing needs, lacking essential features. There may also be frequent bugs, crashes, or security issues that could disrupt your workflow and compromise data integrity. Inadequate customer service or a declining user community could leave you struggling with issues.

Labellerr has some of the most advanced features that many other tools lack, making it a better choice for annotating datasets. Following are some of the features of Labellerr:

1) Smart Feedback Loop: Labellerr’s smart feedback loop can help identify and improve inaccurate annotations, leading to higher data quality.

2) Customizable Workflows: Labellerr offers more flexibility in creating custom workflows for specific labeling tasks. This is required for projects with unique requirements or non-standard formats.

3) Easy Expansion: One critical advantage of Labellerr is its ability to add new classes to existing annotations. This flexibility comes in handy when your project’s scope expands, requiring additional labeling of objects or categories within your data.

Here’s how you can easily migrate from Labelbox to Labellerr.

1. Go to https://login.labellerr.com/ and create an account and a workspace of your own.

2. Enter a project name and select the type of data you want to annotate. In this tutorial, we will be using image data, and then click save and next.

3. Choose how you want to upload your previous annotations. Here, we will be using AWS S3 to upload our previous annotations. Enter your previously configured AWS details and click connect.

4. Add labels to your existing annotations or make any changes you want to your previous annotations, and click on Create Project.

5. Now you can successfully annotate your dataset with the required changes.

Frequently Asked Questions

Q1) What is migration, and why is it necessary?

Migration refers to the process of moving data from one platform to another. This can involve transferring data, applications, and configurations from the existing system to the new one. It is necessary for many reasons, like scalability and performance, or to adapt to changing business requirements.

Q2) What is Labelbox used for?

Labelbox is used for generating high-quality data for computer vision and LLMs, evaluating model performance, and automating tasks by combining AI and human-centric workflows.

Q3) What are the potential benefits of migrating to a new annotation platform like Labellerr?

Labellerr provides many advanced features, like improved scalability and performance, better integration with existing workflows and tools, and an enhanced user experience.