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TU Dublin Quotes

Label Your Data were genuinely interested in the success of my project, asked good questions, and were flexible in working in my proprietary software environment.

Quotes
TU Dublin
Kyle Hamilton

Kyle Hamilton

PhD Researcher at TU Dublin

Trusted by ML Professionals

Yale
Princeton University
KAUST
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Bizerba
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Advanced Farm
Searidge Technologies

Data Labeling Services

Outsource data labeling to professionals

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Trusted by 100+ Customers

ABB – ASEA Brown Boveri
Uipath
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George Washington University
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ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX
ABB – ASEA Brown Boveri
Uipath
Yale
Bizerba
George Washington University
Toptal
Miami University
Hypatos
NEX

Use Cases for

ML Engineers

ML Engineers

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Tackling supervised learning/RLHF projects

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Facing delays with in-house annotations

Dataset Business

Dataset Business

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Offering annotated datasets for sale

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Need rapid processing of raw data

AI-Powered Business

AI-Powered Business

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Implementing ML models for object recognition

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Struggling to manage growing data volumes

Academic Researchers

Academic Researchers

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Required to provide annotated data for peer review

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Have limited time for annotation tasks

Discuss your challenges

Data Services for You

Computer Vision Annotation

Computer Vision Annotation

NLP Annotation

NLP Annotation

QA & Model Validation

QA & Model Validation

LLM Fine Tuning

LLM Fine Tuning

Data Collection

Data Collection

Content Moderation

Content Moderation

Data Processing

Data Processing

An image of a frog, used as an example of a rectangle-based image annotation
An image of a frog, used as an example of a polygon-based image annotation
An image of a frog, used as an example of a 3D cuboid-based image annotation
An image of a frog, used as an example of a keypoint-based image annotation
An image of a frog, used as an example of an instance segmentation annotation applied to an image
An image of a frog, used as an example of a semantic segmentation annotation applied to an image
An image of a frog, used as an example of a panoptic segmentation annotation applied to an image
A LiDar data feed depicted as numbered shapes moving in a dark background
Rectangles Rectangles
Polygons Polygons
Cuboids Cuboids
Keypoints Keypoints
Instance segmentation Instance segmentation
Semantic segmentation Semantic segmentation
Panoptic segmentation Panoptic segmentation
Lidar / Radar Lidar / Radar
An example of text classification depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. Each word is annotated as a part of speech like noun or verb
An example of named entity recognition depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. Frodo is annotated as a name, the frog as an animal, and the tree as a location
An example of sentiment analysis depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. The words determined and hopeful are annotated as positive, while the word slowly crawled is annotated as neutral
An example of sentence comparison depicted as an image of 2 sentences. The first sentence is: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. The second sentence is: Sam the toad, feeling anxious and tired, climbed the tree in search of shelter
An example of audio to text transcription depicted as a sound wave like in the audio editor software. Some parts of the wave are annotated to show what’s being said
Text Classification Text Classification
Named Entity Recognition Named Entity Recognition
Sentiment Analysis Sentiment Analysis
Comparison Comparison
Audio-To-Text Transcription Audio-To-Text Transcription
An example of a data annotation edge case depicted as a bird next to a flower that looks like a bird. But the flower is wrongfully recognized as a bird with 92% accuracy
An example of an object recognition of a bird with 98% accuracy
Quality Assurance Quality Assurance
Model Validation Model Validation
An example of 2 LLMs generating different inferences for data annotator to choose the best response
An example of one LLM generating two different inferences for data annotator to choose the best one
An example of a user submitting a detailed prompt with a detailed response from the LLM
An example of a user requesting the LLM to generate an image of a green truck. The LLM responses with the correctly generated image
An example of a user requesting the LLM to depict the image. The LLM correctly guesses the description
An image example of semantic segmentation with a title: train your LLM like Segment Anything to annotate objects on images
LLM Comparison LLM Comparison
Question-Answer Pairs Question-Answer Pairs
Prompts Generation Prompts Generation
Image and Text Alignment Image and Text Alignment
Image Captioning Image Captioning
Object Detection and Classification Object Detection and Classification

Request to gather publicly available or field data.

An example of content moderation: identified NSFW content on social media. It is censored and labeled as NSFW.

Get a dedicated content moderator or annotate your NSFW social data to train your model.

Beyond annotation, process your data with services like digitizing, parsing, and cleansing.

Computer Vision Annotation

Computer Vision Annotation

svg
An image of a frog, used as an example of a rectangle-based image annotation
An image of a frog, used as an example of a polygon-based image annotation
An image of a frog, used as an example of a 3D cuboid-based image annotation
An image of a frog, used as an example of a keypoint-based image annotation
An image of a frog, used as an example of an instance segmentation annotation applied to an image
An image of a frog, used as an example of a semantic segmentation annotation applied to an image
An image of a frog, used as an example of a panoptic segmentation annotation applied to an image
A LiDar data feed depicted as numbered shapes moving in a dark background
Rectangles Rectangles
Polygons Polygons
Cuboids Cuboids
Keypoints Keypoints
Instance segmentation Instance segmentation
Semantic segmentation Semantic segmentation
Panoptic segmentation Panoptic segmentation
Lidar / Radar Lidar / Radar
NLP Annotation

NLP Annotation

svg
An example of text classification depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. Each word is annotated as a part of speech like noun or verb
An example of named entity recognition depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. Frodo is annotated as a name, the frog as an animal, and the tree as a location
An example of sentiment analysis depicted as an image of the sentence: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. The words determined and hopeful are annotated as positive, while the word slowly crawled is annotated as neutral
An example of sentence comparison depicted as an image of 2 sentences. The first sentence is: Frodo the frog, determined and hopeful, slowly crawled up the tree, seeking a safe heaven. The second sentence is: Sam the toad, feeling anxious and tired, climbed the tree in search of shelter
An example of audio to text transcription depicted as a sound wave like in the audio editor software. Some parts of the wave are annotated to show what’s being said
Text Classification Text Classification
Named Entity Recognition Named Entity Recognition
Sentiment Analysis Sentiment Analysis
Comparison Comparison
Audio-To-Text Transcription Audio-To-Text Transcription
QA & Model Validation

QA & Model Validation

svg
An example of a data annotation edge case depicted as a bird next to a flower that looks like a bird. But the flower is wrongfully recognized as a bird with 92% accuracy
An example of an object recognition of a bird with 98% accuracy
Quality Assurance Quality Assurance
Model Validation Model Validation
LLM Fine Tuning

LLM Fine Tuning

svg
An example of 2 LLMs generating different inferences for data annotator to choose the best response
An example of one LLM generating two different inferences for data annotator to choose the best one
An example of a user submitting a detailed prompt with a detailed response from the LLM
An example of a user requesting the LLM to generate an image of a green truck. The LLM responses with the correctly generated image
An example of a user requesting the LLM to depict the image. The LLM correctly guesses the description
An image example of semantic segmentation with a title: train your LLM like Segment Anything to annotate objects on images
LLM Comparison LLM Comparison
Question-Answer Pairs Question-Answer Pairs
Prompts Generation Prompts Generation
Image and Text Alignment Image and Text Alignment
Image Captioning Image Captioning
Object Detection and Classification Object Detection and Classification
Data Collection

Data Collection

svg

Request to gather publicly available or field data.

Content Moderation

Content Moderation

svg
An example of content moderation: identified NSFW content on social media. It is censored and labeled as NSFW.

Get a dedicated content moderator or annotate your NSFW social data to train your model.

Data Processing

Data Processing

svg

Beyond annotation, process your data with services like digitizing, parsing, and cleansing.

How It Works

Step 1

Free Pilot

Submit your dataset for free labeling and test our services without risk.

Step 2

QA

Assess the pilot output to ensure it meets your quality and budget needs.

Step 3

Proposal

Receive a proposal customized to your specific data labeling needs.

Step 4

Start Labeling

Begin the labeling process with our expert team to advance your ML project.

Step 5

Delivery

Get your labeled data delivered promptly, ensuring your project stays on track.

Calculate Your Cost
Estimates line

1 Pick application field
2 Select labeling method
3 Specify how many objects to label
4 Check the approximate total cost
5 Run free pilot
Pick Application Field
Select Labeling Method
Amount
0 1M
Estimated Cost
$ x objects
$
Select Labeling Method
Amount
0 1M
Estimated Cost
$ x entities
$
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For other use cases

send us your request to receive a custom calculation

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Send your sample data to get the precise cost FREE

Reviews

What Our Clients Say

"Their flexibility and ability to move fast impress us."

Label Your Data has successfully collected data from public sources, annotated missing skeletons, and validated pre-annotations for almost 15,000 images. Although their instructions are sometimes unclear, the team has received praise for their flexibility and speed. They also communicate via email.

"Although the task is unusual and difficult, their annotation quality is incredible."

Label Your Data has delivered high-quality data labels, which have received positive client feedback. Their team produces outputs on time and solves challenges without upselling. They also communicate effectively through virtual meetings.

“I'm impressed with how easy our communication with the team is and how simple and effective the processes are."

Label Your Data's support has been crucial in developing the client's computer vision perception algorithms. They lead a simple and effective collaboration by responding quickly to concerns and questions and delivering everything on time.

"The Label Your Data team was always available for questions."

Label Your Data provided the client with high-quality annotations and added to the number of annotations in the client's portfolio. The team was consistently available for questions or updates that needed to be added to the data set. The client was impressed with the team's communication skills.

"Their flexibility and ability to work with multiple languages are impressive."

Label Your Data continues to work diligently for the client. They consistently provide training data for different document types and languages. Furthermore, they are excellent communicators and collaborators, and their ability to serve high-quality services is outstanding.

"They were always ready to work whenever we had projects."

After engaging with Label Your Data, the client has seen a reduction in error rate and success with model accuracy running on production. The team delivered on time and was communicative in their approach. The client was impressed with the steps Label Your Data took to ensure accurate QA testing.

Why Projects Choose Label Your Data

No Commitment

No Commitment

Check our performance based on a free trial

Flexible Pricing

Flexible Pricing

Pay per labeled object or per labeling hour

Tool-Agnostic

Tool-Agnostic

Working with every labeling tool, even your custom tools

Data Compliance

Data Compliance

Work with a data-certified vendor: PCI DSS Level 1, ISO:2700, GDPR, CCPA

Start Free Pilot

fill up this form to send your pilot request

Email is not valid.

Email is not valid

Phone is not valid

Some error text

Referrer domain is wrong

Thank you for contacting us!

Thank you for contacting us!

We'll get back to you shortly

TU Dublin Quotes

Label Your Data were genuinely interested in the success of my project, asked good questions, and were flexible in working in my proprietary software environment.

Quotes
TU Dublin
Kyle Hamilton

Kyle Hamilton

PhD Researcher at TU Dublin

Trusted by ML Professionals

Yale
Princeton University
KAUST
ABB
Respeecher
Toptal
Bizerba
Thorvald
Advanced Farm
Searidge Technologies

FAQs

What are data labelling services?

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It’s a service where people annotate/tag/label visual or text data. The clients use this data to sell it or train an Al model.

How do I label my data?

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To label your data, you’ll first need to choose the right annotation method and labeling tool, depending on the data type you work with. This process is often time-consuming and complex to handle in-house, but Label Your Data makes it easier. They offer secure data labeling services with a global team of experts, and you can start with a free pilot to see how it works for you.

RUN FREE PILOT