iMerit Company Review

1. How to Choose a Dataset Labeling Vendor for Your ML Project?
2. iMerit Overview
3. iMerit Solutions and Services
4. iMerit Dataset Types
5. iMerit Data Annotation Tools in Use
6. iMerit Integrations
7. iMerit Annotation Process
8. iMerit Quality Assurance
9. iMerit Pricing
10. iMerit Security and Data Compliance
11. Top iMerit Alternatives
    1. Label Your Data
    2. SuperAnnotate
    3. CloudFactory
12. FAQ

Every machine learning project comes to a point where there is a need to collect and label high-quality datasets. There are two options to choose from: building your in-house team or outsourcing the task to a vendor.

If you opt for an outsourcing solution and consider iMerit as your potential option, this research is for you. Save your time and read our 7-minute detailed iMerit review about one of the key players in the data annotation market.

How to Choose a Dataset Labeling Vendor for Your ML Project?

If your AI engineers manually label all the datasets themselves, it may take all their time, as a result delaying the deployment of ML models. This happens even to AI global leaders.

Thus, in search of an innovative data enrichment and annotation company, trusting this task to an outsourcing vendor seems the most suitable alternative, which will save you time and money. Hopefully, this iMerit review will serve this purpose for you.

To choose the right data labeling vendor, consider these decision criteria:

• information on services and products

• dataset types

• annotation tools

• annotation process

• integrations

• quality assurance

• data compliance

iMerit Overview

Founded in 2012 by Dipak Basu, iMerit counts over 5000 employees. It provides full end-to-end services for data annotation in a wide array of industries. The company is headquartered in San Jose, California, but also has offices in New Orleans, LA, Kolkata, and Bengaluru, India.

iMerit Solutions and Services

Our iMerit company overview shows that the data labeling team provides annotation for generative AI, computer vision (CV), natural language processing (NLP), as well as content services. Their main areas of focus include:

• Reinforcement learning from human feedback (RLHF) for LLMS and LVMS. iMerit provides domain expertise, expert feedback, and scalable experts-in-the-loop.

• Image annotation. As per iMerit reviews, the company offers various services for image annotation, including bounding boxes, keypoint annotation, polygon annotation, image classification, semantic segmentation, and LiDar.

• Video annotation. Responding to all annotation projects, the team works with bounding-box annotation, polygon, keypoint, and semantic segmentation annotation.

• Text annotation. The company particularly works with sentiment analysis, intent analysis, named entity recognition (NER), natural language processing (NLP), and entity classification.

• Audio transcription. One of iMerit’s tech-enabled data services, it converts audio data into text, and then labels it for further machine learning processing.

• LiDAR annotation. As other types, it includes semantic segmentation, landmark annotation, 3D cuboids/box annotation, polygon and polyline annotation.

• Sentiment analysis. By splitting the text into smaller segments, the labeling consists of identifying such components as phrases, sentences, or parts of speech according to the sentiment expressed in each.

• Content moderation. It helps to monitor, assess, and filter various user-generated content, detecting offensive content.

• Product categorization. Helps to put images, videos, and text into defined categories. Labeled datasets further help with new product suggestions, query-understanding algorithms, and personalized recommendations.

• Image segmentation. iMerit reviews and applies a number of approaches for image segmentation, including bounding boxes, grayscale, segmentation masks, Gaussian blur, among others.

From the iMerit overview we see that the team of annotators works with different languages. The majority of annotators are English-speaking and are based in India. But, there are also teams in the US, Bhutan, Germany, and Latin America, who can work on your multilingual ML projects.

iMerit Dataset Types

From the iMerit review we see that the company works with a wide variety of dataset types. In preparation of various cases for further AI processing, they work on data curation and generation, data annotation and evaluation. They are able to prepare datasets for computer vision, sentiment analysis, natural language processing, categorization, and LiDAR annotation.

The majority of their annotation types include polygons, bounding boxes, keypoints, polylines, classification, semantic segmentation, text extraction, and others. Besides, they can perform audits and quality assurance (QA) of generative AI systems.

iMerit Data Annotation Tools in Use

Going forward with iMerit company review, here is some information on the company's tools and tasks performed. For the majority of annotations, iMerit experts use their own tool, Ango Hub, which makes labeling quicker. Even though they can use client’s individual tools, they prefer their own, and use it for all cases. It is also available by subscription for individual annotation.

Ango Hub is used for image, video, and text annotation, and helps with accomplishing the following tasks:

• Image and video annotation. The tool has such features as autodetect, optical character recognition, and magnetic lasso.

• Radiology annotation product suite. Called iMerit Radiology Editor, it supports medical imaging annotation. It works with various formats, has data compliance, and helps to partially automate manual annotation tasks.

• In-cabin monitoring annotation. The tool helps to create data annotation for a driver monitoring system (DMS). With it, some specific driver behaviors can be auto annotated.

• Defect detection. The tool includes the application for automated surface inspection. It detects defects in manufacturing thanks to specific built-in algorithms.

• Crop and weed detection. With built-in ML models, the tool has another application for agriculture and tech industry. It’s beneficial for pre-labeling or auto-labeling for ML-assisted annotations.

• Ground control. With Ground Control, Ango Hub provides detailed analytics and metrics, and allows keeping the case materials in one place. It makes the annotated data transfer smooth and in a needed format.

• Edge cases. With the tool, the team of annotators manages such edge cases as reflections of people and objects, hidden signs, and ambiguous objects, among others.

Finally, they have a People platform that contributes to project management, workflow optimization, skill, and talent allocation.

iMerit Integrations

The company offers no-code integrations. The majority of deployment happens through APIs and plugins. They can integrate various applications and MLOps platforms. The data pipelines can be efficiently integrated even with custom requests.

iMerit Annotation Process

Despite offering machine-assistant labeling for specific cases, the company believes human annotation is faster and more efficient. Moving on with iMerit company review, here are their main steps for data annotation:

• Consultation with an expert. At this stage, the customer registers with iMerit’s platform and prepares their tech task.

• Trial and annotators’ training. Depending on the task, annotators proceed with a pilot or proof of concept. During the pilot stage, the group of annotators is chosen and trained, especially if the task requires some specific industry knowledge.

• Workflow customization. Data annotation happens for the piece of the project, which is defined in the pilot stage.

• Feedback cycle. The client provides feedback and proceeds to the offer.

• Evaluation. At the end of every project, the evaluation is done before submission of the final project.

You may be involved in the annotation process at any stage you decide.

iMerit Quality Assurance

During every step of data annotation, iMerit uses various reports, dashboards, and tracking systems to allow efficient project management, identify troubleshooting needs, and track KPI metrics. To ensure the quality of provided annotation, they use a combination of techniques:

• Setting a gold standard during mini-sets;

• Using an annotator consensus;

• Using scientific methods for label consistency;

• Subsampling.

The solution architect randomly chooses a sample of dataset, around 5-10% of the whole labeled data, and checks for possible errors. AI-based frameworks help annotators to review labeled datasets, which is done a couple of times before the project submission.

iMerit Pricing

The usage of the iMerit Ango Hub platform requires a monthly subscription. The price range for annotation is universal for all annotation tasks, but differs depending on the dataset volume. It gets flexible based on the annotator's language and location. If there are more than 100k objects to label per month, the company offers discounts.

If you need to export your data in a custom output, you’ll need to pay a one-time charge. To calculate the full price for the project, it’s important to know the scope of annotation and understand how many people will be involved.

iMerit Security and Data Compliance

The standard procedure supposes that for every new client, the company nominates a Security Manager, who plays a role of a mediator between the client and the team of annotators. This person examines the requirements and identifies the security and control measures that should be in place. The manager tracks the processes and the progress, and trains iMerit employees.

iMerit company overview shows that they possess a number of certifications. They are holders of SOC2 certification for data security, ISO 27001 for information security, ISO 9001:2015 for compliance with statutory and regulatory requirements, and GDPR for data protection requirements across the EU. Besides, they are compliant with IT security standards defined by Trusted Information Security Assessment Exchange (TISAX).

Top iMerit Alternatives

As an alternative to the iMerit overview, here are the top alternatives to consider for your ML project:

Label Your Data

At Label Your Data, we deliver high-quality training data for AI engineers and researchers working on computer vision and NLP projects. Having completed over 100 projects, we understand the specific needs of this field. We offer flexible solutions to streamline the data labeling process, including a free pilot for performance evaluation, adaptable pricing for various project durations, and compatibility with existing data labeling tools. Additionally, our team prioritizes data security, adhering to industry-standard certifications like PCI DSS Level 1, ISO:2700, GDPR, CCPA.

SuperAnnotate

SuperAnnotate offers annotation services provided by a team of human annotators. They also have a labeling tool that you can use on your own. They work with both image and video, text, audio, and custom annotation. While dealing with various types of annotation, they have provided solutions for agriculture and insurance, healthcare and robotics.

CloudFactory

The company helps with data labeling and human-in-the-loop automation. Their areas of focus are image and video annotation. Although with limited language possibilities, they can work on such annotation types as object detection, semantic and instance segmentation, image classification, and keypoint annotation. They also have compliance certifications.

FAQ

by Maria Shepeta
on March 21, 2024. Share on LinkedIn Post on Twitter