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We provide data labeling services by designing custom data annotation solutions with enterprise-class security level for our clients since 2010.
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ISO/IEC 27001:2013
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High Quality Annotations
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Flexible Business Models
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Obligatory Quality Assurance
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Custom Annotation Solutions
Computer
Vision Annotation
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Semantic Segmentation
2D Boxes
Polygons
OCR
3D Cuboids
Key Points
Video Annotation
Image Categorization
LiDAR/RADAR
Each pixel of an image is associated with a certain object class (e.g., car, person, building, road, sky). A machine learning model clusters together the pixels that belong to the same class. As a result, you get a map with clusters of different classes of objects to train your model.
This is a popular type of data annotation in machine learning also referred to as bounding boxes. Drawing 2-dimensional frames around objects of interest (cars, pedestrians, trees) allows a machine to classify these objects into predefined categories.
2D boxes can be not enough to train a machine learning algorithm because it is sometimes necessary to give an idea of the shape of the object. For such cases, it is possible to draw outlines around objects with varying numbers of sides. Polygons allow the machines to recognize objects by their shape.
OCR allows deciphering images of text into regular text. For example, if you have scanned documents or photocopies, this data annotation type tells the machine learning algorithm how to turn them into machine-encoded text.
This type of data annotation is used in machine learning to add an in-depth perspective to a 2D image by drawing 3D boxes around objects (cars, people, buildings). These boxes give you three dimensions (height, width, depth), rotation, and relative position of the object.
Also known as landmark annotation, this data annotation type allows the machine learning algorithm to plot the most important points to define the shapes of natural objects (think facial features and recognition of emotions, poses of sports players, and so on).
Annotating a video is similar to annotating an image, but first, it's necessary to break it into frames. Then object detection is used to predict the class and position of an object on each frame and object segmentation for predicting composition (e.g., subject vs background).
The classification of images is among the most widely pursued goals in data annotation and machine learning. Categorization helps to train the machines to group images into predefined classes (for example, after training, a machine learning model can tell if a photo contains a dog or a cat).
A machine learning model predicts objects based on 3D point cloud data obtained from different sensors that complement each other (hence the alternative name of this type of data annotation, sensor fusion). LiDAR and RADAR are used to improve the fidelity of machine predictions.
Semantic Segmentation
Each pixel of an image is associated with a certain object class (e.g., car, person, building, road, sky). A machine learning model clusters together the pixels that belong to the same class. As a result, you get a map with clusters of different classes of objects to train your model.
2D Boxes
This is a popular type of data annotation in machine learning also referred to as bounding boxes. Drawing 2-dimensional frames around objects of interest (cars, pedestrians, trees) allows a machine to classify these objects into predefined categories.
Polygons
2D boxes can be not enough to train a machine learning algorithm because it is sometimes necessary to give an idea of the shape of the object. For such cases, it is possible to draw outlines around objects with varying numbers of sides. Polygons allow the machines to recognize objects by their shape.
OCR
OCR allows deciphering images of text into regular text. For example, if you have scanned documents or photocopies, this data annotation type tells the machine learning algorithm how to turn them into machine-encoded text.
3D Cuboids
This type of data annotation is used in machine learning to add an in-depth perspective to a 2D image by drawing 3D boxes around objects (cars, people, buildings). These boxes give you three dimensions (height, width, depth), rotation, and relative position of the object.
Key Points
Also known as landmark annotation, this data annotation type allows the machine learning algorithm to plot the most important points to define the shapes of natural objects (think facial features and recognition of emotions, poses of sports players, and so on).
Video Annotation
Annotating a video is similar to annotating an image, but first, it's necessary to break it into frames. Then object detection is used to predict the class and position of an object on each frame and object segmentation for predicting composition (e.g., subject vs background).
Image Categorization
The classification of images is among the most widely pursued goals in data annotation and machine learning. Categorization helps to train the machines to group images into predefined classes (for example, after training, a machine learning model can tell if a photo contains a dog or a cat).
LiDAR/RADAR
A machine learning model predicts objects based on 3D point cloud data obtained from different sensors that complement each other (hence the alternative name of this type of data annotation, sensor fusion). LiDAR and RADAR are used to improve the fidelity of machine predictions.
“The team is professional and provides excellent value for money. I feel like Label Your Data truly prioritizes our project — they’re willing to drop everything to fulfill our needs and keep us happy.”
Jack
CTO, Raveler Ltd.
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NLP
Annotation
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Text Classification
Named Entity Recognition
Intent/Sentiment Analysis
Comparison
Audio-To-Text Transcription
One of the major tasks of NLP, text classification involves dividing text into groups based on their content. Topic labeling and spam detection are good examples of text classification.
This type of data annotation is based on the detection and categorization of entities, which are specific words or phrases in a text. NER is helpful for the machine learning models built to summarize and navigate through large volumes of text.
Sentiment analysis is used to analyze texts and categorize them depending on the tone of the message. It is useful in market research, brand reputation, and understanding customer experiences. Intent analysis helps to identify the intention of the text (e.g. phishing emails).
Certain machine learning tasks need to compare one text to a different text (or texts) and identify how similar they are. Comparison is used to find semantically similar texts.
As the name suggests, the transcription is used to teach a machine learning model to turn audio into text. It’s helpful for a variety of cases, such as transcribing public speeches or business meetings. It can also be used for training voice assistants.
Text Classification
One of the major tasks of NLP, text classification involves dividing text into groups based on their content. Topic labeling and spam detection are good examples of text classification.
Named Entity Recognition
This type of data annotation is based on the detection and categorization of entities, which are specific words or phrases in a text. NER is helpful for the machine learning models built to summarize and navigate through large volumes of text.
Intent/Sentiment Analysis
Sentiment analysis is used to analyze texts and categorize them depending on the tone of the message. It is useful in market research, brand reputation, and understanding customer experiences. Intent analysis helps to identify the intention of the text (e.g. phishing emails).
Comparison
Certain machine learning tasks need to compare one text to a different text (or texts) and identify how similar they are. Comparison is used to find semantically similar texts.
Audio-To-Text Transcription
As the name suggests, the transcription is used to teach a machine learning model to turn audio into text. It’s helpful for a variety of cases, such as transcribing public speeches or business meetings. It can also be used for training voice assistants.
“Label Your Data completed the work quickly and accurately. We ran some tests, checking the algorithm and its effectiveness. The data was labeled correctly, and the results were exactly what we wanted to see. We were pleased.”
Kate Robinson
Chief Communications Officer, SHEis.ai
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Additional
Services
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Data collection
Model validation
Know Your Customer
Data Anonymization
Data Entry
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Alina
If you need to collect a dataset, but you don’t have the time or the resources to complete this task, just come to us with your requirements. We will work on delivering the high-quality, relevant, and consistent data that will help you train your AI model effortlessly.
During the annotation process at Label Your Data, we try to ensure the labels are relevant and of high quality. We will make sure that the objects are correctly identified and classified, and that the structure of your annotation meets the requirements and goals of the algorithm.
Our team specializes in document/ID card, face, and biometric verification. The process includes the verification of the client's identity, comprehending the nature of their activities, determining the legitimacy of the source of funds, and evaluating the risk of money laundering associated with the client.
Suppression, character masking, pseudonymization, swapping, generalization, perturbation, and synthetic data are the data anonymization methods our Label Your Data team uses to adhere to security regulations and data protection.
Our team carries out the process on spreadsheets, handwritten or scanned documents, audio files, or videos. With data entry services, we aim to help our clients conduct analyses and develop business strategies. We have well-trained personnel to do manual data entry.
Data collection
On photo:
Alina
If you need to collect a dataset, but you don’t have the time or the resources to complete this task, just come to us with your requirements. We will work on delivering the high-quality, relevant, and consistent data that will help you train your AI model effortlessly.
Model validation
During the annotation process at Label Your Data, we try to ensure the labels are relevant and of high quality. We will make sure that the objects are correctly identified and classified, and that the structure of your annotation meets the requirements and goals of the algorithm.
Know Your Customer
Our team specializes in document/ID card, face, and biometric verification. The process includes the verification of the client's identity, comprehending the nature of their activities, determining the legitimacy of the source of funds, and evaluating the risk of money laundering associated with the client.
Data Anonymization
Suppression, character masking, pseudonymization, swapping, generalization, perturbation, and synthetic data are the data anonymization methods our Label Your Data team uses to adhere to security regulations and data protection.
Data Entry
Our team carries out the process on spreadsheets, handwritten or scanned documents, audio files, or videos. With data entry services, we aim to help our clients conduct analyses and develop business strategies. We have well-trained personnel to do manual data entry.
Case 1: OCR for Accounting
Case 2: Bounding Boxes for Insurance Solutions
Case 3: Semantic Segmentation for Robotics
Case 4: Intent & Sentiment Analysis for Customer Care
Case 5: Website Elements Annotation for Security
Client: Accounting organization
Main challenge:
Annotation in 10 languages
Solution:
Multilingual OCR annotation with double rounds of QA
Imagine that you have a ton of bills and checks photocopied, and you need to train your machine learning algorithm to read them. How do you do this?
To solve this task, we at Label Your Data used OCR annotation. Our team of annotators boxed every bit of info and manually entered the text and numbers to make them readable for the machines.
We faced the challenge of annotating the bills in several languages, 8 of which used Latin characters, and 2 were Asian languages. We also added special characters like â and é. Besides, our Label Your Data family consists of perfectionists, so we run not one but two rounds of annotation QA to be sure we didn't miss any of the tiny peculiarities of different languages. That's just how we roll. Want to make sure? Place an order and see for yourself!
Client: Insurance Company
Main challenge:
Poor quality of photographs
Solution:
Bounding boxes and full-fledged QA
The Client tasked our team of annotators to look through 100,000 photos of different cars. The idea was to gather a dataset of damages of different severity with the further goal of automating the insurance compensation calculations.
We used bounding boxes annotation to tag each damage and identify the locations of specific car parts. Later on, the Client returned to us with additional instructions of image tagging and dataset clearing.
As you can imagine, most of the images lacked in quality: they were taken in different conditions, and sometimes it was quite a challenge to discern between damages and lens flares. Our annotating team compared multiple photos and ran a 100% round of annotation QA before submitting the labeled images. As an additional bonus, the Client can now generate synthetic images with the cars and damages on them.
Client: Robotics Firm
Main challenge:
Large volume with a short deadline
Solution:
Instance segmentation (bounding boxes + semantic segmentation)
Our Client launched a huge outdoor robotics project that relied upon a fully annotated dataset for daylight and nighttime sceneries of different locations: city streets, parks, etc. Our team was tasked with labeling 20,000 images of the outdoor scenery that were collected by the robots. We received a list of 50 identified objects, some of them with additional attributes (for example, "car" should have been marked as "parked" or "moving", and "person" could be "moving", "standing", or "sitting").
For the first two weeks, we've set a team of 10 people working on this project, and after the first results, we've upscaled to 50 people. It was a massive volume of data to be processed in a short time. And it was important for us not only to deliver quality, but also to finish the project before the deadline.
Client: Customer Support Company
Main challenge:
Complicated sentiment conditioning
Solution:
Intent/sentiment analysis and 2-week training with the Client
Our Client with a big customer care team provided Label Your Data with a dataset of thousands of tickets sent by the customers. They were collected from multiple projects over the years. The goal was to automate the customer care process and optimize the work of the support team.
We used intent and sentiment analysis annotation to prepare the dataset for training the ML algorithm to detect the emotion, urgency, intent of the customer, as well as offer a fitting template to the support agent.
Our team also dedicated 2 weeks of training to understand the needs of the Client, their internal system, as well as to learn all the technical aspects. This allowed us to annotate the tickets as if we were working with the customers like support agents.
Client: A Security Company
Main challenge:
Different designs of websites
Solution:
Labeling of HTML codes for various zones and elements on webpages
The Client's goal was to train the machine learning model to spot the vulnerable places on any website, regardless of its design or complexity. For this, we had to gather and label different HTML codes for each zone and element throughout the pages. That's why we were excited about this task where we needed to annotate 1000 websites with around 20 pages for each.
The Label Your Data team ran a 2-week pilot to learn the Client's data annotation extension. During the online training sessions, our annotators learned on the go to get a full understanding of the Client's vision. We can do the same for you, build the annotation project from scratch, and customize it for your requirements. Ask us for a quote and get your own state-of-the-art pilot plan.
FAQ
What does it mean to annotate data for ML models?
It means to add meaningful labels or tags to each piece of data. Data annotation is the process of explaining to the machine learning model what the dataset contains.
What are the steps of a data annotation project?
First, you define the goal. Then you collect the data in accordance with your task. Afterward, your dataset is labeled by data annotation specialists. Finally, you use the annotated dataset to train your machine learning model.
What is the role of a data annotator in an ML project?
Data annotation is a tedious task best done by humans. A data annotation specialist uses labeling tools to create labels. This process requires accuracy and knowledge, especially with highly specialized data.
What do data annotation companies do?
These companies specialize in converting your unlabeled data into labeled datasets with tags that correspond to your specific tasks. Such remote data annotation services are used when you don't have an in-house annotation team.
Can’t find the required data service for your ML project?
There are numerous data annotation services, and it's okay if you didn't find what you need on the list. At Label Your Data, we offer custom data annotation with regard to your unique vision and task.
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