How To Migrate Data Labeling Project From Label Studio To Labellerr

Label Studio is an open-source data labeling and annotation tool that helps you prepare high-quality training data for machine learning models.

Features of Label Studio

Flexibility: Label Studio supports various data types like images, text, audio, and video, making it suitable for a wide range of machine learning tasks.

Annotation Options: Label Studio provides users with a wide range of annotation options, like rectangles, circles, and polygons.

Workflow Management: It provides easier workflow management by assigning tasks to specific annotators or setting rules for the automatic distribution of datasets.

In this blog, we will use the Label Studio enterprise cloud version 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. We will annotate the dataset with two labels: Septoria and Stripe rust.

Table of Contents

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

Image Annotation using Label Studio

1) Sign up on the label studio website, https://labelstud.io. with your email ID and password.

2) Select the Create Project button on the homepage, enter a name for your project, and choose the type of annotation you want to perform (e.g., image classification, object detection).

3) After creating the project, upload the images you want to annotate in the data import tab.

4) In the labeling setup tab, choose the type of annotation (bounding box) required and select the imported file.

5) Start annotating mages by clicking on the first image. Use the annotation tools provided to mark the region of interest and draw a bounding box around it.

6) Once the annotations are made and reviewed, save the annotations by clicking on the Save or Submit button.

Congratulations! You have completed the annotation using Label Studio.

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

3) It 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)It offers customization options which allows user to define annotation tasks, create custom labeling interfaces, and configure project workflows according to their specific need.

Labellerr'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 Label Studio 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: 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 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 Label Studio 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 Label Studio used for?

Label Studio is an open-source data labeling tool for labeling and annotating. It supports different types of data, like images, text, and audio. It can also be used to prepare raw data or improve existing training data to get more accurate ML models.

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.