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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

Trusted by ML Professionals

Data Annotation Services

Outsource data annotation to experts

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Trusted by 100+ Customers

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
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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You are working on a supervised learning / RLHF project

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In-house annotations take too long

Dataset Business

Dataset Business

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You are selling annotated datasets

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Need to process raw datasets

AI-Powered Business

AI-Powered Business

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Your business is using an ML model to recognize objects

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Your raw data stockpiles faster than you can process it

Academic Researchers

Academic Researchers

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Peer reviewers require annotated datasets

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You can’t afford to spend time on annotations

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
placeholder
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
placeholder
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

Send us your dataset sample for free labeling to experience our services risk-free.

Step 2

QA

Evaluate the pilot results to ensure we meet your quality and cost expectations.

Step 3

Proposal

Receive a detailed proposal tailored to your specific text annotation needs.

Step 4

Start Labeling

Begin the labeling process with our expert team to enhance your NLP project.

Step 5

Delivery

Receive timely delivery of your labeled data, keeping your project on schedule.

Calculate Your Cost
Estimates line

1 Pick application field
2 Select annotation method
3 Specify how many objects to label
4 Check the approximate total cost
5 Run free pilot
Pick Application Field
Select Annotation Method
Amount
0 1M
Estimated Cost
$ x objects
$
Select Annotation 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

Stars "We were impressed by the labeled data quality as well."

Label Your Data makes significant progress toward the goal of enhancing the client’s algorithm performance. The team works quickly to deliver annotations and annotators with extensive experience in the field. Their project management is straightforward, making for a smooth engagement.

Stars "They’re willing to drop everything to fulfill our needs and keep us happy."

Demonstrating a profound dedication to the project, Label Your Data consistently provides near-perfect deliverables at a cost-effective price. Thanks to their help, the client has been able to be more flexible with their work. Their impressive turnaround time further enhances the solid partnership.

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

LYD showed high quality and flexibility and convinced us from day one with their high passion for delivering a great service. We currently evaluate further possibilities to further involve them in our initiatives and projects.

Stars “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.

Stars "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.

Stars "They are flexible, dedicated, and their processes are well organized."

Label Your Data sends out weekly data annotation for the client to review. So far, the platform hasn’t had any issues and they are focusing on enhancing the platform since it was launched in November 2020. Their expertise shows productive results as the project progresses.

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 annotation hour

Tool-Agnostic

Tool-Agnostic

Working with every annotation 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

Trusted by ML Professionals

FAQs

Do I need to commit to a long-term contract?

Not at all. We offer 3 flexible pricing options:

Short term for one-off projects like academic research
On-demand for seasonal projects when you need additional workforce
Long term for a streamlined data annotation workflow

What is your pricing?

We calculate according to how much time is spent on your object annotation. That’s why the pilot project is also valuable for us – we get to calculate the expenses

When can I start the free pilot?

As soon as we clarify details on your dataset. You’ll get a reply within this or the next work day.

Do you work with our labeling tools?

Absolutely! We’re flexible with any tools you want us to work in.

How can I trust you with my confidential data?

We earned several data compliance certificates to prove our dedication to data security:

PCI DSS Level 1
ISO:27001
GDPR
CCPA

After we sign the NDA, we make sure to anonymize your data for our employees to prevent any data leaks.

What is a data annotation service?

A data annotation service, like the one offered by Label Your Data, involves labeling and tagging datasets such as text, images, and videos to make them usable for training ML models. This process converts raw data into structured information, enabling AI systems to learn and improve their performance.

What is a data annotation example?

An example of data annotation is image labeling, where objects in an image are highlighted with colored layers, such as bounding boxes or segmentation masks, to identify features like cars, people, or animals. You can check out more examples in the data services section to see how we annotate different data types for various use cases at Label Your Data.

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