Data Annotation / Labelling / Tagging / Classification Services

Data labeling is used to create large volumes of annotated data like pictures or images that can be used to train machines and make them functional for AI-based models.

Data labeling is used to create large volumes of annotated data like pictures or images that can be used to train machines and make them functional for AI-based models. Systems need to understand what is shown on a photograph, said in a voice recording, or written in a text, among many other things. By labeling all this data, machines can improve their learning and AI keeps evolving. It concerns speech recognition on our smartphones, autonomous driving, parking systems and many other technologies.

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What are our data sources?

We use the data sources on the side for ranking solutions and awarding badges in data annotation / labelling / tagging / classification service category. Our data sources in data annotation / labelling / tagging / classification service category include;

4

review websites

2

social media websites

1

search engine data for branded queries

Data Annotation / Labelling / Tagging / Classification Service Leaders

According to the weighted combination of 7 data sources

SuperAnnotate

Amazon Mechanical Turk

CloudFactory

Appen

LabelBox

What are Data Annotation / Labelling / Tagging / Classification Service market leaders?

Taking into account the latest metrics outlined below, these are the current data annotation / labelling / tagging / classification service market leaders. Market leaders are not the overall leaders since market leadership doesn’t take into account growth rate.

SuperAnnotate

Amazon Mechanical Turk

CloudFactory

Appen

Clickworker Image Annotation Service

What are the most popular Data Annotation / Labelling / Tagging / Classification Services?

What are the most searched data annotation / labelling / tagging / classification service brands?

Searches with Brand Name

These are the number of queries on search engines which include the brand name of the solution. Compared to other Data categories, Data Annotation / Labelling / Tagging / Classification Service is more concentrated in terms of top 3 companies’ share of search queries. Top 3 companies receive 86%, 9% more than the average of search queries in this area.

What are the most mature Data Annotation / Labelling / Tagging / Classification Services?

Which data annotation / labelling / tagging / classification service companies have the most employees?

302 employees work for a typical company in this solution category which is 281 more than the number of employees for a typical company in the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 13 companies with >10 employees are offering data annotation / labelling / tagging / classification service. Top 3 products are developed by companies with a total of 100k employees. The largest company building data annotation / labelling / tagging / classification service is AWS with more than 100,000 employees.

AWS
Appen
CloudFactory
Clickworker
Cogito Tech LLC

What are the Data Annotation / Labelling / Tagging / Classification Services growing their number of reviews fastest?


We have analyzed reviews published in the last months. These were published in 4 review platforms as well as vendor websites where the vendor had provided a testimonial from a client whom we could connect to a real person.

These solutions have the best combination of high ratings from reviews and number of reviews when we take into account all their recent reviews.

What is the average customer size?

According to customer reviews, most common company size for data annotation / labelling / tagging / classification service customers is 1-50 Employees. Customers with 1-50 Employees make up 59% of data annotation / labelling / tagging / classification service customers. For an average Data solution, customers with 1-50 Employees make up 52% of total customers.

Overall
Customer Service
Ease of Use
Likelihood to Recommend
Value For Money

Customer Evaluation

These scores are the average scores collected from customer reviews for all Data Annotation / Labelling / Tagging / Classification Services. Data Annotation / Labelling / Tagging / Classification Services is most positively evaluated in terms of "Likelihood to Recommend" but falls behind in "Customer Service".

What is the level of interest in Data Annotation / Labelling / Tagging / Classification Services?

Interest in Data Annotation / Labelling / Tagging / Classification Services

This category was searched on average for 483 times per month on search engines in 2022. This number has increased to 572 in 2023. If we compare with other data solutions, a typical solution was searched 1k times in 2022 and this increased to 1.2k in 2023.

As mentioned before, data labeling tasks are accomplished by humans manually. Unsupervised learning or semi supervised learning are machine learning approaches that do not rely on labeled data. However, they are not the best performing solutions for most current machine learning applications. For more, feel free to read our more detailed explanation.

There are 4 common resources for data labelling. Companies can rely on a combination of these resources for their data labeling needs.

  • Full/Part-Time Employees
  • Managed Workers
  • Contractors
  • Crowdsourcing

Feel free to explore the pros and cons of each approach

Data labeling service companies provide data annotation services for machine learning. They achieve this by using pre-trained machine learning models and human-powered skills to label (i.e. annotate) an image, text, video or audio.

Data labeling is used in machine learning model training.

To enable machine learning, data labeling tasks are completed by humans who manually label and classify objects. There are different types of labeling. Below are the most common ones for videos and images:

  • Semantic segmentation is the process of labeling each pixel in an image to a class. Autonomous vehicles, robot vision and medical applications are common areas for semantic segmentation.
  • Polygon Annotation detects irregular shapes and uneven shaped objects by creating shapes and outlines with an arbitrary number of sides on image data. Annotators draw lines by placing dots around the outer edge of the object they want to classify.
  • Bounding Box: Annotators are given an image and are tasked with drawing a box around objects for in-depth recognition of objects in the image data. The most common usage of bounding box annotation type is autonomous vehicles. Entities such as vehicles, pedestrians, traffic lights are identified by bounding boxes so that vehicles can distinguish these entities. Image tagging for e-commerce, retail and damage detection for insurance companies are other application areas for the bounding box method.
  • 3D Cuboids: Cuboids are similar to bounding boxes with one difference. An annotator illustrates the length and width of the object as in the bounding box method. However, 3D Cuboid method adds one more dimension, which is the depth of the object.
  • Lines and Splines: Annotators draw lines along the boundaries such as lane separators on the road. It is also used to train warehouse robots so that robots can accurately place boxes in a row.
  • Landmark Annotation : Annotator labels key points at specified locations. It is generally used for facial recognition applications and counting applications. It helps to understand the movement trajectory of each point motion in the targeted object.

Technologies such as Internet of Things (IoT), robotics and predictive analytics all rely on Machine Learning (ML) and Artificial Intelligence (AI). Modern machine learning approaches rely on labeled/annotated data and data annotation companies create labeled data.

Raising interest on autonomous vehicles is another reason why data annotation services are growing in importance. The annotated data allow autonomous vehicle computer models to recognize objects.

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