iMerit vs. Appen: An In-Depth Comparison for 2024

Selecting the right data labeling service can make or break your AI/ML projects. If you're deciding between iMerit and Appen, this article will help you make an informed choice.

We’ll provide an in-depth comparison of these two data labeling companies, covering their strengths, weaknesses, pricing, service quality, and more. Read on to get the insights you need to select the best data labeling company for your ML needs.

iMerit vs. Appen: Company Profiles

iMerit Company

Founded by Dipak Basu in 2012, iMerit employs over 5,000 people and offers comprehensive data annotation services across multiple industries. With its headquarters in San Jose, California, and additional offices in New Orleans, Kolkata, and Bengaluru, iMerit delivers high-quality data for AI applications in agriculture, autonomous vehicles, healthcare, and more. Their end-to-end solutions support Fortune 500 companies in developing robust AI models.

Appen Company

Founded in 1996 by linguist Dr. Julie Vonwiller, Appen Ltd is a leading Australian company specializing in human-annotated data for AI and machine learning. Appen has a global network of over 1 million contributors, providing services in 130+ countries and 180 languages. With 25+ years of experience, Appen company offers tailored data solutions for various industries, attracting clients from tech giants to automotive manufacturers.

Services and Products

iMerit Services and Products

iMerit offers a wide range of data labeling services, including:

• Reinforcement Learning from Human Feedback (RLHF): Domain expertise and expert feedback for LLMS and LVMS.

• Image Annotation: Bounding boxes, keypoint annotation, polygon annotation, image classification, semantic segmentation, and LiDAR.

• Video Annotation: Bounding-box, polygon, keypoint, and semantic segmentation annotation.

• Text Annotation: Sentiment analysis, intent analysis, named entity recognition (NER), and entity classification.

• Audio Transcription: Converts audio data into text and labels it for machine learning.

• LiDAR Annotation: Semantic segmentation, landmark annotation, 3D cuboids/box annotation, polygon, and polyline annotation.

• Content Moderation: Monitors, assesses, and filters user-generated content.

• Product Categorization: Categorizes images, videos, and text for product suggestions and personalized recommendations.

• Image Segmentation: Methods include bounding boxes, grayscale, segmentation masks, and Gaussian blur.

iMerit’s multilingual annotators are based in India, the US, Bhutan, Germany, and Latin America.

Appen Services and Products

Appen provides a comprehensive suite of annotation services. The company also offers an end-to-end platform to support the entire AI development lifecycle.

SERVICES

• Data Collection. Appen sources various data types, including text, audio, video, and geospatial data.

• Data Annotation. Human-in-the-loop annotation for NLP, speech processing, and computer vision.

• Search Relevance. Enhancing search engine algorithms through model evaluation, content moderation, and related search refinement.

• Reinforcement Learning. RLHF services specifically for developing LLMs.

• Document Intelligence. Improves document processing through data curation and annotation.

• Location-Based Services. Geospatial data annotation for accurate mapping and location intelligence.

• Pre-Labeled Datasets. A library of 270+ pre-labeled audio, image, video, and text datasets in over 80 languages.

• Data for LLMs: Custom data solutions for LLM builders.

PLATFORM

A global crowdsourcing network with over 1 million contributors across 170+ countries. It offers features like project management, performance monitoring, data quality control, and security management. It also provides access to subject-matter experts (SMEs).

The platform handles various tasks:

• Data Collection

• Data Annotation

• Transcription

• Translation

• Speech Modeling

• Model Evaluation

Pricing Models

Dataset Types

iMerit Dataset Types

iMerit handles a diverse range of dataset types, offering data curation, generation, annotation, and evaluation for various AI applications. They specialize in preparing datasets for computer vision, sentiment analysis, natural language processing, categorization, and LiDAR annotation. Their annotation capabilities include polygons, bounding boxes, keypoints, polylines, classification, semantic segmentation, and text extraction. Additionally, iMerit provides audits and quality assurance (QA) for generative AI systems.

Appen Dataset Types

Appen offers a variety of datasets to meet diverse ML needs. They process and annotate text data, including emails, documents, and chat conversations in over 80 languages. Audio data, such as phone calls and voice commands, are transcribed and labeled for features like speaker identification. Visual data expertise includes image annotation for objects and scenes, facial recognition, and video data analysis for object tracking and activity recognition.

Supported Data Formats:

• CSV (Comma-separated values)

• TSV (Tab-separated values)

• XLSX (Microsoft Excel spreadsheets)

• ODS (OpenDocument Spreadsheets)

Encoding:

• All files must use UTF-8 encoding.

Formatting:

• Each column in your data file must have a clear and descriptive header.

Data Annotation Tools

iMerit Annotation Tools

iMerit company uses its proprietary tool, Ango Hub, for most annotations, preferring it over client-specific tools. Available by subscription, Ango Hub handles image, video, and text annotation, facilitating tasks such as:

• Image and Video Annotation: Features include autodetect, OCR, and magnetic lasso.

• Radiology Annotation: iMerit Radiology Editor supports medical imaging, offering data compliance and partial automation.

• In-Cabin Monitoring: Annotates driver behaviors for driver monitoring systems.

• Defect Detection: Automated surface inspection for manufacturing defects.

• Crop and Weed Detection: Pre-labeling and auto-labeling for agriculture using built-in ML models.

• Ground Control: Provides analytics, metrics, and seamless annotated data transfer.

• Edge Cases: Manages complex scenarios like reflections, hidden signs, and ambiguous objects.

Appen Annotation Tools

Appen doesn’t offer standalone data annotation tools for public use, but they provide a comprehensive data annotation platform as part of their services. This platform combines human annotators with machine learning to deliver high-quality training data for AI models.

The vendor can also integrate with client-provided tools, although these may not always be scalable. If project demands increase and client tools can’t keep up, Appen may switch to their platform to handle the workload. Additionally, Appen has demonstrated the ability to automate tasks using Large Language Models (LLM), though it’s unclear if this is a standard feature or a custom solution.

Integrations

iMerit Integrations

iMerit provides seamless no-code integrations, primarily through APIs and plugins. They can integrate with a variety of applications and MLOps platforms, ensuring efficient data pipelines that can accommodate custom requests.

Appen Integrations

Within the Appen projects, you get two main integration options: API and Live Large Language Model (LLM) APIs.

API Integration:

• Automate tasks related to Appen’s data annotation services.

• Programmatically create, edit, and launch annotation jobs.

• Download results from completed jobs.

• Integrate seamlessly with your existing workflow.

Appen uses a RESTful API with JSON data format and key-based authentication. They recommend trying out the process manually before setting up the API.

Live LLM API Integration:

• Connect your Large Language Models (LLMs) with Appen’s platform.

• Test your LLMs against real-world data.

• Gather insights to improve your models.

• Create a feedback loop with human experts to fine-tune your LLM.

This integration ensures your LLMs are accurate, relevant, and aligned with your specific needs.

Annotation Process

iMerit Annotation Process

While iMerit offers machine-assisted labeling for specific cases, they believe human annotation is often faster and more efficient. Here are the main steps in their data annotation process:

1. Consultation with an Expert: Customers register on iMerit’s platform and prepare their technical task with expert guidance.

2. Trial and Annotator Training: Annotators undertake a pilot project or proof of concept, with specific training provided, especially for tasks requiring industry-specific knowledge.

3. Workflow Customization: Annotation is carried out on a project segment defined during the pilot stage.

4. Feedback Cycle: Clients provide feedback, which informs the final offer.

5. Evaluation: Each project undergoes evaluation before final submission.

Clients can be involved in the annotation process at any stage they choose.

Appen Annotation Process

Appen’s data annotation platform offers customizable workflows for efficient management from start to finish:

1. Onboarding and Training: A Customer Success Manager helps your team get acquainted with the platform, enabling them to quickly create and launch annotation jobs.

2. Setting Up Annotation Jobs: Use customizable templates to define the type of data (text, image, audio, etc.) and specific annotations needed. Jobs can be set up via Appen’s user interface or API for programmatic control.

3. Data Annotation by Global Contributors: Jobs are assigned to a global network of qualified contributors. The platform includes tools and guidelines to ensure consistency in annotations.

4. Monitoring and Adjustment: Track job progress and review data to make necessary adjustments, such as refining instructions or tweaking annotation requirements.

5. Data Download and Reporting: Once complete, download the labeled data for your AI or ML projects. Appen provides dashboards and reports to help optimize jobs for cost, quality, and efficiency.

Appen’s workflow aims to streamline data annotation with a user-friendly platform, a global workforce, and comprehensive management tools.

Quality Assurance

iMerit QA

At every stage of data annotation, iMerit utilizes reports, dashboards, and tracking systems to ensure efficient project management, troubleshoot issues, and monitor KPI metrics. Their quality assurance process includes the following steps:

• Setting a Gold Standard with Mini-Sets: Establishing benchmark datasets for quality control.

• Using Annotator Consensus: Ensuring accuracy through multiple annotators agreeing on labels.

• Applying Scientific Methods for Label Consistency: Utilizing proven methods to maintain uniformity in labeling.

• Implementing Subsampling: Randomly selecting 5-10% of the labeled dataset to check for errors.

A solution architect reviews these subsamples to identify any errors. Additionally, iMerit uses AI-based frameworks to assist annotators, enabling multiple reviews of labeled datasets before project submission.

Appen QA

Appen guarantees high-quality training data for AI projects through a multi-layered QA process, customized to your needs with a global network of contributors.

• Customizable Solutions: Tailored QA plans with human expertise and advanced options like real-time learning for continuous improvement.

• Seamless Workflow: Integrates your team, labeling tools, and workers. Offers automated or managed solutions for performance tracking and quality analysis.

• Rigorous Quality Control: Appen reviews your data through multiple checks from pre-production to post-production to ensure top-notch data quality.

• AI-Powered Crowd Management: Utilizes a network of over 1 million contributors and AI to match tasks to worker skills and streamline labeling.

• Detailed Quality Reports: Comprehensive dashboards track project progress, including labeled units and overall accuracy. Data is segmented by annotator to identify training needs.

Security and Data Compliance

TL;DR

Summarizing about the two data annotation companies, iMerit offers a wide range of services and uses its proprietary tool, Ango Hub. Appen, with over 25 years of experience, provides comprehensive data solutions through a global network and end-to-end platform. Both prioritize data security and compliance. iMerit excels in quality assurance, while Appen offers flexible pricing and a broader service range. Your choice will depend on your project's requirements, budget, and preferred features.

And if you’re looking for a vendor with these qualities:

• No commitment

• Flexible pricing

• Tool-agnostic

• Data-compliant

Run a free pilot to put our team’s labeling expertise to the test.

FAQ

Written by

Yuliia Kniazieva Editor-at-Large

One of the technical writers at Label Your Data, Yuliia has been gradually delving into the intricate aspects of AI. With her strong passion for the written word and technical expertise, Yuliia has developed a keen interest in the evolving field of data annotation and the power of machine learning in today's tech-savvy world. Check out her articles to learn more about the complex world of technology and find the solutions that work best for your AI project!