Data Annotation / Labelling / Tagging / Classification Service

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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Compare Data Annotation / Labelling / Tagging / Classification Services
Results: 37

AIMultiple is data driven. Evaluate 37 services based on comprehensive, transparent and objective AIMultiple scores. For any of our scores, click the icon to learn how it is calculated based on objective data.

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73.00138815491056
97.00277624309392
100
100
0.0925414364640884
49.0000000667272
top5 , top10
top5 , top10
5star
Appen
4.60
100%
100%
100%
= 2 reviews
= 20 employees
= 100,000 visitors

Appen combines the best of human and machine intelligence to provide high-quality annotated training data

34.85560500616627
45.88887485692567
2.121213676624284
48.74934559092204
0.028453038674033152
23.82233515540688
top5 , top10
top5 , top10
5star
Hive
4.70
30%
100%
2%
= 2 reviews
= 20 employees
= 100,000 visitors

Transform the way your business operates using Hive's powerful, deep-learning visual recognition models and training data platform.

23.6567753481954
30.90365448897425
4.8484865235441
32.72065170260665
0.026243093922651933
16.40989620741654
top5 , top10
top5 , top10
5star
Playment
5.00
20%
92%
4%
= 2 reviews
= 20 employees
= 100,000 visitors

Playment offers a fully-managed data labeling solution to build highly accurate training datasets for computer vision models

2.5
3
0
0
100
2
top5 , top10
Amazon Mechanical Turk
0%
100%
0%
= 2 reviews
= 20 employees
= 100,000 visitors

1.4939890490397827
0.012124309392265192
0
0
0.40414364640883976
2.9758537886873
top5 , top10
CloudFactory
0%
100%
0%
= 2 reviews
= 20 employees
= 100,000 visitors

CloudFactory is a global leader in combining people and technology to provide workforce solutions for machine learning and business process optimization. Our growing team of data analysts prepare the data that powers products and trains artificial intelligence. We work with innovators across diverse industries and process millions of tasks a day for some of the world’s most innovative companies. We exist to create meaningful work for one million talented people in developing nations, so we can earn, learn, and serve our way to become leaders worth following.

1.0128308843906435
0.0003149171270718232
0
0
0.010497237569060774
2.0253468516542155
top5 , top10
SuperAnnotate
0%
36%
0%
= 2 reviews
= 20 employees
= 100,000 visitors

1
0
0
0
0
2
top5 , top10
Mindy Support
0%
0%
0%
= 2 reviews
= 20 employees
= 100,000 visitors

Mindy Support is an international brand with 6 offices across Ukraine. We empower companies all over the world by providing cost-efficient business process outsourcing with no compromise on quality. Mindy Support builds effective teams in data annotation, customer care, back office support, sales and marketing for businesses of all sizes. Why Have A Remote Team? - High-quality results - Cost-efficiency - Skilled employees - Headache-free process

1
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0
0
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2
top5 , top10
aiTouch
0%
0%
0%
= 2 reviews
= 20 employees
= 100,000 visitors

1
0
0
0
0
2
top5 , top10
DataPure
0%
0%
0%
= 2 reviews
= 20 employees
= 100,000 visitors

1
0
0
0
0
2
top5 , top10
Mighty AI
0%
0%
0%
= 2 reviews
= 20 employees
= 100,000 visitors

Market Presence Metrics

Popularity

Searches with brand name

These are the number of queries on search engines which include the brand name of the service. Compared to other service based solutions, Data annotation / labelling / tagging / classification Service is more concentrated in terms of top 3 companies' share of search queries. Top 3 companies receive 100%, 26% more than the average of search queries in this area.

Web Traffic

Data annotation / labelling / tagging / classification Service is a highly concentrated solution category in terms of web traffic. Top 3 companies receive 100% (27% more than average solution category) of the online visitors on data annotation / labelling / tagging / classification service company websites.

Maturity

Amazon Web Services (AWS)
CloudFactory
Appen
thehive
Playment

Number of Employees

103 employees work for a typical company in this category which is 49 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. 7 companies (42 less than average solution category) with >10 employees are offering data annotation / labelling / tagging / classification service. Top 3 products are developed by companies with a total of 501-1,000 employees. However, all of these top 3 companies have multiple products so only a portion of this workforce is actually working on these top 3 products.

Learn More About Data Annotation / Labelling / Tagging / Classification Service

What is a data labelling/annotation service?

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.

What are the application areas for data annotation?

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.

Why is it important now?

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.

Feel free to read more here

What are its alternatives?

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.

What are the types of data labeling service providers?

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