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: 31

AIMultiple is data driven. Evaluate 31 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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94.1236653888967
97.00277624309392
100
100
0.0925414364640884
68.20229086806043
top5 , top10
top5 , top10
5star
Figure Eight
4.60
100%
22%
100%
= 2 reviews
= 300 employees
= 100,000 visitors

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

44.47627649596878
45.88887485692567
2.121213676624284
48.74934559092204
0.028453038674033152
31.76000822107228
top5 , top10
top5 , top10
5star
Hive
4.70
30%
7%
2%
= 2 reviews
= 300 employees
= 100,000 visitors

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

30.37177534168905
30.903654488974254
4.8484865235441
32.72065170260665
0.026243093922651933
25.58220391420929
top5 , top10
top5 , top10
5star
Playment
5.00
20%
6%
4%
= 2 reviews
= 300 employees
= 100,000 visitors

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

4.880000000000001
3
0
0
100
21.8
top5 , top10
Amazon Mechanical Turk
0%
100%
0%
= 2 reviews
= 300 employees
= 100,000 visitors

2.991861143779448
0.012124309392265192
0
0
0.40414364640883976
29.768542483805597
top5 , top10
CloudFactory
0%
100%
0%
= 2 reviews
= 300 employees
= 100,000 visitors

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% (21% more than 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% (23% more than average solution category) of the online visitors on Data annotation / labelling / tagging / classification Service company websites.

Maturity

Number of Employees

Median number of employees that provide Data annotation / labelling / tagging / classification Service is 219 which is 156 more than the median number of employees for the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 6 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.

Amazon Web Services (AWS)
CloudFactory
figure-eight
thehive

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