iMerit vs. SuperAnnotate: Features, Pricing, and Performance Compared

Deciding on the right data labeling company for your ML project can be daunting. Accuracy, reliability, and cost-effectiveness are crucial, and you’ve narrowed it down to two data annotation companies: iMerit and SuperAnnotate. But which one is the better choice?

In this article, we compare their key features, pricing, and performance to help you pick the best vendor for your ML model training needs.

iMerit vs. SuperAnnotate: 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. Headquartered in San Jose, California, with offices in New Orleans, Kolkata, and Bengaluru, iMerit provides high-quality data for AI applications in areas like agriculture, autonomous vehicles, and healthcare. Their end-to-end solutions support Fortune 500 companies in building robust AI models.

SuperAnnotate Company

SuperAnnotate is a Silicon Valley startup with an engineering team in Armenia, which started as a tool for semantic segmentation. Since joining Berkeley's Skydeck Accelerator in 2019, it has evolved into a robust ML-powered image annotation platform. Leveraging patented AI algorithms, SuperAnnotate automates image segmentation and object selection for faster, more accurate results. It provides businesses with high-quality training data and efficient workflows, empowering them to build, fine-tune, and manage AI models quickly and effectively.

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.

SuperAnnotate Services and Products

SuperAnnotate company offers a comprehensive platform to streamline machine learning model development with key services including:

• Data Annotation:

  • FineTune: Create high-quality training data for text, images, audio, video, and LLMs.

  • WForce: Access a global network of over 400 professional annotation teams across multiple continents, with specialists for complex use cases. Supports 18 languages, including English, Chinese, French, Arabic, Spanish, and more.

• Data Management:

  • Explore: Manage data with version control, debugging, and visualization tools to ensure dataset accuracy and monitor annotator performance.

• MLOps & Automation:

  • Orchestrate: Build robust CI/CD pipelines for machine learning projects with features like built-in neural networks, Python SDK, and webhooks.

• LLMs & GenAI Services:

  • Automated annotation with patented algorithms for fast, accurate image segmentation.

  • Data curation and QA tools for consistent training data.

  • Workflow automation features to optimize resources.

  • Seamless native integrations with various tools and platforms.

  • Robust data governance for security and compliance.

• SuperAnnotate Desktop:

  • A free app offering advanced features like polygon annotation, filtering, and labeling to enhance the annotation process.

Pricing Models

Dataset Types

iMerit Dataset Types

iMerit handles a diverse range of dataset types, providing 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 offers audits and quality assurance (QA) for generative AI systems.

SuperAnnotate Dataset Types

SuperAnnotate's annotation teams are adept at handling various data types, including images, audio, videos, LiDAR, text, and custom formats. The platform supports a range of data formats for different ML applications, ensuring a smooth annotation process.

Supported data types include:

• Images: JPG, JPEG, PNG, WEBP, TIFF, BMP, and TIF variations.

• Videos: MP4, AVI, MOV, FLV, MPEG, and WEBM for image projects; OGG, WEBM, and MP4 for video/audio projects.

• Text: Plain text files in TXT format with UTF encoding.

• Tiled Imagery: Manages large images by breaking them into smaller sections for annotation.

• Point Cloud Data: Handles 3D applications with data representing 3D objects through points in space.

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.

SuperAnnotate Annotation Tools

SuperAnnotate offers a robust suite of annotation tools to create high-quality training data for machine learning:

• LLM Annotation Tool: Trains large language models for tasks like RLHF, question answering, instruction following, and image captioning, with an instant feedback loop for data refinement.

• Image Annotation Tool: Supports object detection, classification, pose estimation, OCR, and segmentation, handling complex formats including tiled and multilayer images.

• Video Annotation Tool: Annotates objects, events, and movements within videos for tasks such as object tracking, classification, segmentation, action detection, and lane detection.

• Text Annotation Tool: Extracts information from text data for NLP model training, supporting sentiment analysis, summarization, classification, translation, question answering, and NER.

• Audio Annotation Tool: Provides audio transcription, segmentation, classification, and training for speech recognition, speaker identification, and sound event detection.

• Classification Tool: Classifies document formats like PDFs, HTML files, and websites, supporting topic labeling, categorization, content analysis, question answering, sentiment analysis, and summarization.

Automation Features:

• Superpixel Functionality: Groups pixels with similar characteristics to speed up and enhance segmentation tasks.

• SAM Integration: Incorporates Meta AI’s Segment Anything Model (SAM) for faster, higher-quality polygon generation and improved annotation processes.

Upcoming Feature:

• Pre-Labeling: In development to allow users to use pre-trained models from SuperAnnotate or their own models for initial data labeling.

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.

SuperAnnotate Integrations

SuperAnnotate enables seamless data import from various cloud storage services, including AWS S3, GCP Buckets, Azure Containers, and Databricks. It also supports integration with custom storage solutions. Whether you’re working with LLMs, images, videos, text, or point clouds, SuperAnnotate integrations streamline the data import process. For enhanced automation, use the Python SDK to import data programmatically.

Importantly, SuperAnnotate maintains read-only access to your data, ensuring a secure and non-destructive workflow, with your items remaining safely stored in your cloud while being accessible for annotation on the platform.

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.

SuperAnnotate Annotation Process

Platform:

SuperAnnotate offers various project types to streamline data annotation and ensure high-quality training data. Here’s an overview of the annotation process using the platform:

1. Choosing the project type

   • Images: Use AI-assisted tools for tasks like segmentation and text extraction.

   • Videos or Audio: Upload and track objects with Autotrack (video only).

   • Text: Classify and connect text snippets for context.

   • Tiled Imagery: Upload high-resolution images for detailed annotation.

   • LLMs and GenAI: Create a project, design the annotation interface, and assign items to users.

2. Setting up the project

   Navigate to the Projects section, click “+ New Project,” select your project type, name it, click “Create,” and import your data for annotation.

3. Annotating data

   Use SuperAnnotate’s specific tools (e.g., Magic Select for image segmentation) to label and define objects within your data.

4. Utilizing classes and attributes

   Define objects using Classes and refine them with Attributes to enhance annotation accuracy.

Annotation Service Teams:

SuperAnnotate’s annotation service teams follow a structured process to ensure efficient and high-quality data labeling for machine learning:

1. Project Management:

   A dedicated project manager serves as your central contact, overseeing the team of annotators and quality assurance (QA) specialists.

2. DataOps:

   The project manager handles dataset import, sets up relevant classes, provides clear instructions for annotators, and exports the completed project.

3. MLOps:

   The high-quality data is seamlessly integrated into your ML pipeline, enabling effective model training.

By dividing tasks among specialized roles, SuperAnnotate ensures a smooth workflow and efficient use of your time.

Quality Assurance

iMerit QA

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

• Setting a gold standard with mini-sets

• Using annotator consensus

• Applying scientific methods for label consistency

• Implementing subsampling

A solution architect randomly selects a 5-10% sample of the labeled dataset to check for errors. iMerit reviews labeled datasets multiple times before project submission thanks to AI-based frameworks that assist their annotators.

SuperAnnotate QA

SuperAnnotate reviews annotations through a rigorous multi-stage QA process involving different user roles, such as annotators, QAs, and project admins.

For data annotation services, SuperAnnotate employs multiple methods to guarantee the quality of your labeled data:

• Vetted Workforce: Access to a network of pre-qualified annotation teams trained to meet specific competency criteria.

• Multi-Level QA: An automated quality assurance system routes data through various user roles for comprehensive quality checks.

• Project Management: A dedicated project manager oversees data processes, configurations, and the assigned annotation team.

• Dedicated QA Teams: Separate QA teams supervise annotators and ensure data meets specific quality standards.

Security and Data Compliance

To sum up, iMerit offers comprehensive data annotation services across multiple industries with a large vetted workforce, but can be costlier for premium services and custom exports, with complex tools and time-consuming QA processes. SuperAnnotate, on the other hand, provides advanced annotation tools for diverse data types, automated workflows, and global vetted teams, with flexible cost-per-unit pricing and a 14-day free trial. However, it has limited support for less common languages, relies on external teams for complex tasks, and may require manual refinement for certain annotations, with potentially unclear upfront SuperAnnotate pricing.

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

• No commitment

• Flexible pricing

• Tool-agnostic

• Data-compliant

Run a free pilot to experience our data labeling expertise.

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!