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A Beginner’s Guide to Machine Learning Projects: Where to Start?

Machine learning projects have become increasingly popular in recent years, as businesses and individuals alike recognize the potential of this powerful technology. However, getting started with machine learning can be overwhelming for beginners. With a wide range of algorithms, tools, and techniques available, it’s important to have a clear roadmap to guide your journey. In this article, we will explore the essential steps you need to take when embarking on a machine learning project.

Understanding the Basics of Machine Learning

Before diving into machine learning projects, it is crucial to have a solid understanding of the basics. Machine learning is a branch of artificial intelligence that enables computers to learn from data without being explicitly programmed. It involves developing algorithms that can analyze and interpret large datasets, identify patterns, and make predictions or decisions based on the patterns discovered.

To begin your machine learning journey, familiarize yourself with key concepts such as supervised learning (where models are trained using labeled data), unsupervised learning (where models discover patterns in unlabeled data), and reinforcement learning (where models learn through trial and error). Additionally, grasp the difference between regression (predicting continuous values) and classification (predicting categorical values) tasks.

Choosing the Right Machine Learning Algorithm

Once you have grasped the basics of machine learning, it’s time to choose an algorithm that suits your project goals. There are various types of algorithms available for different types of problems. For example, if you aim to predict housing prices based on historical data, regression algorithms like linear regression or decision trees may be suitable. On the other hand, if you want to classify emails as spam or non-spam based on their content, classification algorithms such as logistic regression or support vector machines might be more appropriate.

Consider factors like dataset size, complexity of features, interpretability requirements, and computational resources when selecting an algorithm. It is also beneficial to experiment with multiple algorithms to find the one that yields the best results for your specific project.

Preparing and Exploring Data

Data preparation and exploration are crucial steps in any machine learning project. Before feeding data into a machine learning algorithm, you need to ensure it is clean, relevant, and properly formatted. Start by examining the dataset for missing values, outliers, or inconsistencies. Depending on the nature of the problem, you may need to handle missing data by imputing or removing them.

Next, explore the dataset to gain insights into its characteristics. Visualize the data using plots and graphs to identify patterns or correlations between variables. This process can help you make informed decisions about feature selection or engineering techniques that may improve model performance.

Building and Evaluating Models

With a well-prepared dataset at hand, it’s time to build and train your machine learning models. This involves splitting your data into training and testing sets to assess how well your model generalizes to unseen data. The training set is used to teach the model by adjusting its internal parameters based on known outputs (labels). The testing set is then used to evaluate how well the trained model performs on new data.

During model training, tune hyperparameters (settings that influence model behavior) using techniques like grid search or random search. This process helps optimize model performance without overfitting (when a model memorizes training examples instead of generalizing).

Once you have trained your models, evaluate their performance using appropriate metrics such as accuracy, precision, recall, or mean squared error depending on the type of problem you are solving. Compare different models and fine-tune them if necessary until you achieve satisfactory results.

Embarking on a machine learning project can be an exciting but challenging endeavor for beginners. By understanding the basics of machine learning concepts, choosing suitable algorithms for your project goals, preparing and exploring data meticulously, and building and evaluating models effectively; you can lay a strong foundation for successful machine learning projects. Remember that machine learning is an iterative process, and continuous learning and experimentation are key to improving your skills and achieving better results in the field.

This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.

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Machine Learning Essentials: What is Data Annotation?

Data annotation helps machines make sense of text, video, image or audio data..

One of the stand-out characteristics of Artificial Intelligence (AI) is its ability to learn, for better or for worse . It’s this ongoing effort that distinguishes AI from static, code-dependent software.

It’s also precisely this ability that makes high-quality annotated data a crucial element in training representative, successful, and bias-free AI models.

Data annotation is the process of labeling individual elements of training data (whether text, images, audio, or video) to help machines understand what exactly is in it and what is important. This annotated data is then used for model training. Data annotation also plays a part in the larger quality control process of data collection, as well—annotated datasets become ground truth datasets: data that is held up as a gold standard and used to measure model performance and the quality of other datasets.

Teaching Through Data

The purpose of annotating data is to tell machine learning models exactly what we want them to know. Teaching a machine to learn through annotation can be likened to teaching a toddler shapes and colors using flashcards, where the annotations are the flashcards and annotators are the teacher.

Of course, this is a simplified example of how AI learns. In practice, machine learning models need large volumes of correctly annotated data to learn how to perform a task – which can prove to be a challenge in practice. Companies must have the resources to collect and label data for their specific use case—sometimes in a less-resourced language or dialect.

The following is a closer look at the different types of data annotation, how annotated data is used, and why humans will continue to be an indispensable part of the data annotation process in the future.

The Importance of Data Annotation

The caliber of your input data will determine how well your machine learning models perform. And for this to happen, data annotation plays a key role in helping your models understand the requirements in the right way.

Before we dive into data annotation any further, let us look at the types of data that define the role of annotating data. Primarily, data around us is classified into two categories: structured and unstructured data. Structured data comes with a pattern that is clearly identifiable and searchable by computers, while unstructured data, despite having an internal structure humans can understand, lacks those patterns. Examples of unstructured data include social media posts, emails, text files, phone recordings and chat communications, and more. Both human and automated processes can produce unstructured data. This unstructured data is expanding exponentially, and organizations continue to struggle to process and extract value from it. Defined.ai strives to address this lack of structured training data for machine learning.

Data annotation is especially important when considering the amount of unstructured data that exists in the form of text, images, video, and audio. By most estimates, unstructured data accounts for 80% of all data generated .

Currently, most models are trained via supervised learning, which relies on well-annotated data from humans to create training examples.

Types of Data Annotation

Because data comes in many different forms, there are several different types of data annotation, for either text, image or video-based datasets. Here is a breakdown each of these three types of data annotation.

The Written Word: Text Annotation

There is an incredible amount of information within any given text dataset. Text annotation is used to segment the data in a way that helps machines recognize individual elements within it. Types of text annotation include:

Named Entity Tagging: Single and Multiple Entities :

Named Entity Tagging (NET) and Named Entity Recognition (NER) help identify individual entities within blocks of text, such as “person,” “sport,” or “country.”

This type of data annotation creates entity definitions, so that machine learning algorithms will eventually be able to identify that “Saint Louis” is a city, “Saint Patrick” is a person, and “Saint Lucia” is an island.

Sentiment Tagging:

Humans use language in unique and varying ways to express thoughts through phrases that can’t always be taken at face value. Therefore, it’s necessary to read between the lines or consider the context to understand the sentiment behind a phrase. This is why sentiment tagging is crucial in helping machines decide if a selected text is positive, negative, or neutral.

In many cases, the sentiment of a sentence is clear: for example, “Super helpful experience with the customer support team!” is clearly positive. However, when the intent is less straightforward or when sarcasm or other ambiguous speech is used, it becomes more difficult to discern the true meaning. For example, “Great reviews for this place, but I can’t say I agree!” This is where human annotation adds real value.

Semantic Annotation:

The intent or meaning of words can vary greatly depending on the context and within specific domains. For example, domain-specific jargon used in a technical conversation in the finance industry is very different from the one used in the telecommunications industry, or the slang used between two friends. Semantic annotation gives that extra context that machines need to truly understand the intent behind the text.

More than Meets the Eye: Image Annotation

Image annotation helps machines understand what elements are present within an image. This can be done by using Image Bounding Boxes (IBB), in which elements of an image are labeled with basic bounding boxes, or through more advanced object tagging.

Annotations in images can range from simple classifications (labeling the gender of people in an image, for example) to more complex details (for example, labeling whether the scene is rainy or sunny). Image classification is another approach where images are annotated based on single or multi-level categories. In this case, an example would be images of mountains classified into “Mountain” category.

Movement Detected Video annotation

Video annotation works in similar ways to image annotation – using Bounding Boxes and other annotation methods, single elements within frames of a video are identified, classified, or even tracked across multiple frames. Video annotation works in similar ways to image annotation – using bounding boxes and other annotation methods, single elements within frames of a video are identified, classified, or even tracked across multiple frames. For example, tagging all the humans in a Closed-Circuit Television (CCTV) video as “Customer” or helping autonomous vehicles recognize objects along the road.

Important Notes on Data Annotation

Human vs. Machine

Humans play an integral role in ensuring that data is annotated properly. Humans can provide context and a deeper understanding of intent in creating ground truth datasets, enhancing annotations’ overall value.

In-house versus outsourcing

Data annotation is essential but also resource-heavy and time-consuming. One report showed that data preparation and engineering tasks represent over 80% of the time spent on most machine learning projects. Organizations may often be faced with the decision of whether to perform data annotation in-house or to outsource it.

There are some advantages to performing data annotation in-house. For one, you retain control and visibility over the data collection process. Secondly, with very niche or technical models, subject matter experts with relevant knowledge may already be in-house.

However, outsourcing data annotation to a third party is an excellent solution to some of the biggest challenges to doing data annotation in-house, namely time, resources, and quality. Third-party data annotation can help reach the scale, speed, and quality needed to create effective training datasets while complying with increasingly complex data privacy rules and requirements.

Making Your Machine Smarter

Data annotation is key to the data collection process and essential in helping machines reach their full potential. Consistent, high-quality output becomes possible by feeding these models with accurately annotated datasets, insights, and predictions.

To learn more about our data annotation services, visit us here .

Data Annotation: Types and Use Cases for Machine Learning

It is amazing the number of things machines can be trained to do – from voice recognition to navigation to even playing chess! But for them to achieve these incredible feats, a significant amount of time is put into training them to recognize patterns and relationships between variables. This is the essence of machine learning. Large volumes of data are fed to computers for training, validation, and testing. However, for machine learning to take place, these data sets must be curated and labeled to make the information easier for them to understand; a process known as data annotation.

What is Data Annotation?

Data annotation is the process of making text, audio, or images of interest understandable to machines through labels. It is an essential part of supervised learning in artificial intelligence. For supervised learning, the data must be trained to enhance the machine’s understanding of the desired task at hand.

Take for instance that you want to develop a program to single out dogs in images. You must go through the rigorous process of feeding it with multiple labeled pictures of dogs and “non-dogs” to help the model learn what dogs look like. The program will then be able to compare new images with its existing repository to find out whether an image contains a dog in it.

Though the process is repetitive at the beginning, if enough annotated data is fed to the model, it will be able to learn how to identify or classify items in new data automatically without the need of labels. For the process to be successful, high quality annotated data is required. This is why most developers choose to use human resources for the annotation process. The process might be automatized by using a machine to prepopulate the data, but a human touch and a human eye is preferred for review when the data is nuanced or sensitive. The higher the quality of annotated data fed to the training model, the higher the quality of the output. It is also important to note that most AI algorithms require regular updates to keep up with changes. Some may be updated as often as every day.

Types of Annotation in Machine Learning

1. Text annotation

Text annotation is the process of attaching additional information, labels, and definitions to texts. Since written language can convey a lot of underlying information to a reader such as emotions, sentiment, stance, and opinion, in order for a machine to identify that information, we need humans to annotate what exactly it is in the text data that conveys that information.

Natural language processing (NLP) solutions such as chatbots, automatic speech recognition, and sentiment analysis programs would not be possible without text annotation. To train NLP algorithms, massive datasets of annotated text are required.

How is text annotated?

Most companies seek out human annotators to label text data. With language being very subjective, it is often best to utilize the help of highly-skilled human annotators who provide significant value especially in emotional and subjective texts. They are familiar with modern trends, slang, humor and different uses of conversation.

First, a human annotator is given a group of texts, along with pre-defined labels and client guidelines on how to use them. Next, they match those texts with the correct labels. Once this is done on large datasets of text, the annotations are fed into machine learning algorithms so that the machine can learn when and why each label was given to each text and learn to make correct predictions independently in the future. When built correctly with accurate training data, a strong text annotation model can help you automate repetitive tasks in a matter of seconds.

Below, we’ve laid out different types of text annotation and how each one is used in the business world.

a) Sentiment Annotation

Sentiment annotation is the evaluation and labeling of emotion, opinion, or sentiment within a given text. Since emotional intelligence is subjective – even for humans – it is one of the most difficult fields of machine learning. It can be challenging for machines to understand sarcasm, humor, and casual forms of conversation. For example, reading a sentence such as: “You are killing it!”, a human would understand the context behind it and that it means “You are doing an amazing job”. However, without any human input, a machine would only understand the literal meaning of the statement.

When built correctly with accurate training data, a strong sentiment analysis model can help businesses by automatically detecting the sentiment of:

– Customer reviews

– Product reviews

– Social media posts

– Public opinion

– Emails

b) Text Classification

Text classification is the analysis and categorization of a certain body of text based on a predetermined list of categories. Also known as text categorization or text tagging, text classification is used to organize texts into organized groups.

– Document classification – the classification of documents with pre-defined tags to help with organizing, sorting, and recalling of those documents. For example, an HR department may want to classify their documents into groups such as CVs, applications, job offers, contracts, etc.

– Product categorization – the sorting of products or services into categories to help improve search relevance and user experience. This is crucial in e-commerce, for example, where annotators are shown product titles, descriptions, and images and are asked to tag them from a list of departments the e-commerce store has provided.

c) Entity Annotation

Entity annotation is the process of locating, extracting and tagging certain entities within text. It is one of the most important methods to extract relevant information from text documents. It helps recognize entities by giving them labels such as name, location, time and organization. This is crucial in enabling machines to understand the key text in NLP entity extraction for deep learning.

– Named Entity Recognition – the annotation of entities with named tags (e.g. organization, person, place, etc.) This can be used to build a system (a Named Entity Recognizer) that can automatically find mentions of specific words in documents.

– Part-of-speech Tagging – the annotation of elements of speech (e.g. adjective, noun, pronoun, etc.)

– Language Filters – For example, a company may want to label abusive language or hate speech as profanity. That way, companies can locate when and where profane language was used and by whom, and act accordingly.

2. Image annotation

This aim of image annotation is to make objects recognizable through AI and ML models. It is the process of adding pre-determined labels to images to guide machines in identifying or blocking images. It gives the computer, vision model information to be able to decipher what is shown on the screen. Depending on the functionality of the machine, the number of labels fed to it can vary. Nonetheless, the annotations must be accurate to serve as a reliable basis for learning.

Here are the different types of image annotation:

a. Bounding boxes

This is the most commonly used type of annotation in computer vision. The image is enclosed in a rectangular box, defined by x and y axes. The x and y coordinates that define the image are located on the top right and bottom left of the object. Bounding boxes are versatile and simple and help the computer locate the item of interest without too much effort. They can be used in many scenarios because of their unmatched ability in enhancing the quality of the images.

b. Line annotation

is method, lines are used to delineate boundaries between objects within the image under analysis. Lines and splines are commonly used where the item is a boundary and is too narrow to be annotated using boxes or other annotation techniques.

c. 3D Cuboids

Cuboids are similar to the bounding boxes but with an additional z-axis. This added dimension increases the detail of the object, to allow the factoring in of parameters such as volume. This type of annotation is used in self-driving cars, to tell the distance between objects.

d. Landmark annotation

This involves the creation of dots around images such as faces. It is used when the object has many different features, but the dots are usually connected to form a sort of outline for accurate detection.

3. Image transcription

This is the process of identifying and digitizing text from images or handwritten work. It can also be referred to as image captioning, which is adding words that describe an image. Image transcription relies heavily on image annotation as the prerequisite step. It is useful in creating computer vision that can be used in the medical and engineering fields. With proper training, machines can be able to identify and caption images with ease using technology such as Optical Character Recognition (OCR).

Use Cases of Data Annotation

Improved results from search engines

When building a big search engine such as Google or Bing, adding websites to the platform can be tedious, since millions of web pages exist. Building such resources requires large pools of data that can be impossible to manage manually. Google uses annotated files to speed up the regular updating of its servers.

Large scale data sets can also be fed to search engines to improve the quality of results. Annotations help to customize the results of a query based on the history of the user, their age, sex, geographical location, etc.

Creation of facial recognition software

Using landmark annotation, machines can be able to recognize and identify specific facial markers. Faces are annotated with dots that detect facial attributes such as the shape of the eyes

and nose, face length, etc. These pointers are then stored in the computer database, to be used if the faces ever come into sight again.

The use of this technology has enabled tech companies such as Samsung and Apple to improve the security of their smartphones and computers using face unlock software.

Creation of data for self-driving cars

Although fully autonomous cars are still a futuristic concept, companies like Tesla have made use of data annotation to create semi-autonomous ones. For vehicles to be self-driving, they must be able to identify markers on the road, stay within lane limits, and interact well with other drivers. This can be made possible through image annotation. By making use of computer vision, models can be able to learn and store data for future use. Techniques such as bounding boxes, 3D cuboids and semantic segmentation are used for lane detection, collection, and identification of objects.

Advances in the medical field

New technology in the medical field is largely based on AI. Data annotation is used in pathology and neurology to identify patterns that can be used in making quick and accurate diagnoses. It is also helping doctors pinpoint tiny cancerous cells and tumors that can be difficult to identify visually.

What is the importance of using data annotation in ML?

– Improved end-user experience

When accurately done, data annotation can significantly improve the quality of automated processes and apps, therefore enhancing the overall experience with your products. If your websites make use of chatbots, you can be able to give timely and automatic help to your customers 24/7 without them having to speak to a customer support employee that may be unavailable outside working hours.

In addition, virtual assistants such as Siri and Alexa have greatly improved the utility of smart devices through voice recognition software.

– Improves the accuracy of the output

Human annotated data is usually error-free due to the extensive number of man-hours that are put into the process. Through data annotation, search engines can provide more relevant results based on the users’ preferences. Social media platforms can customize the feeds of their users when annotation is applied to their algorithm.

Generally, annotation improves the quality, speed, and security of computer systems.

Final Thoughts

Data annotation is one of the major drivers of the development of artificial intelligence and machine learning. As technology advances rapidly, almost all sectors will need to make use of annotations to improve on the quality of their systems and to keep up with the trends.

If you’re looking for reliable annotated data for your upcoming project, get in touch to see our data annotation services geared to save you time, money, and effort. We also help businesses make their AI projects multilingual with our translation services in 55+ languages.

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Data Annotation Tools for Machine Learning (Evolving Guide)

Choosing the Best Data Annotation Tool for Your Project

The data annotation tools you use to enrich your data for training and deploying machine learning models can determine success or failure for your AI project. Your tools play an important role in whether you can create a high-performing model that powers a disruptive solution or solves a painful, expensive problem - or end up investing time and resources on a failed experiment.

Choosing your tool may not be a fast or easy decision. The data annotation tool ecosystem is changing quickly as more providers offer options for an increasingly diverse array of use cases. Tooling advancements happen by the month, sometimes by the week. These changes bring improvements to existing tools and new tools for emerging use cases.

The challenge is thinking strategically about your tooling needs now and into the future. New tools, more advanced features, and changes in options, such as storage and security, make your tooling choices more complex. And, an increasingly competitive marketplace makes it challenging to discern hype from real value.

We’ve called this an evolving guide because we will update it regularly to reflect changes in the data annotation tool ecosystem. So be sure to check back regularly for new information, and you can bookmark this page.

Read the full guide below, or download a PDF version of the guide you can reference later.

In this guide, we’ll cover data annotation tools for computer vision and NLP (natural language processing) for supervised learning .

First, we’ll explain the idea of data annotation tools in more detail, introducing you to key terms and concepts. Next, we will explore the pros and cons of building your own tool versus purchasing a commercially available tool or leveraging open source options.

We’ll give you considerations for choosing your tool and share our short list of the best data annotation tools available. You’ll also get a short list of critical questions to ask your tool provider.

Introduction: will this guide be helpful to me, the basics: data annotation tools and machine learning, a critical choice: build vs. buy, how to choose a data annotation tool, the best data annotation tools: commercial and open source, iteration & evolution: changing data annotation needs, new tools, questions to ask your data annotation tool provider, tool agnostic: the cloudfactory advantage.

Introduction
Build vs. Buy
How to Choose
Best Data Annotation Tools
Iteration & Evolution
Questions to Ask
CloudFactory Advantage

This guide will be helpful if :

You are beginning a machine learning project and have data you want to clean and annotate to train, test, and validate your model.
You are working with a new data type and need to understand the best tools available for annotating that data.
Your data annotation needs have evolved (e.g., you need to add features to your annotation) and want to learn about tools that can handle what you’re doing today and what you’re adding to your process.
You are in the production stage and must verify models using human-in-the-loop .

What’s data annotation?

In machine learning, data annotation is the process of labeling data to show the outcome you want your machine learning model to predict. You are marking - labeling, tagging, transcribing, or processing - a dataset with the features you want your machine learning system to learn to recognize. Once your model is deployed, you want it to recognize those features on its own and make a decision or take some action as a result.

Annotated data reveals features that will train your algorithms to identify the same features in data that has not been annotated. Data annotation is used in supervised learning and hybrid, or semi-supervised, machine learning models that involve supervised learning.

What’s a data annotation tool?

A data annotation tool is a cloud-based, on-premise, or containerized software solution that can be used to annotate production-grade training data for machine learning. While some organizations take a do-it-yourself approach and build their own tools, there are many data annotation tools available via open source or freeware.

They are also offered commercially, for lease and purchase. Data annotation tools are generally designed to be used with specific types of data, such as image, video, text, audio, spreadsheet, or sensor data. They also offer different deployment models, including on-premise, container, SaaS (cloud), and Kubernetes.

Data annotation tools have these key elements: They can be used to annotate many data types, including text, image, video, audio, time-series, and sensor data. They support annotation for 2-D, 3-D, video, audio, transcription, and text. You can buy a commercially-available data annotation tool, you can take a do-it-yourself approach and build your own, or you can use open source or freeware to create and tailor a data annotation tool for your use case.

6 Important Data Annotation Tool Features

1) dataset management.

Annotation begins and ends with a comprehensive way of managing the dataset you plan to annotate. As a critical part of your workflow, you need to ensure that the tool you are considering will actually import and support the high volume of data and file types you need to label. This includes searching, filtering, sorting, cloning, and merging of datasets.

Different tools can save the output of annotations in different ways, so you’ll need to make sure the tool will meet your team’s output requirements. Finally, your annotated data must be stored somewhere. Most tools will support local and network storage, but cloud storage - especially your preferred cloud vendor - can be hit or miss, so confirm support-file storage targets.

2) Annotation methods

This is obviously the core feature of data annotation tools - the methods and capabilities to apply labels to your data. But not all tools are created equal in this regard. Many tools are narrowly optimized to focus on specific types of labeling, while others offer a broad mix of tools to enable various types of use cases.

Nearly all offer some type of data or document classification to guide how you identify and sort your data. Depending on your current and anticipated future needs, you may wish to focus on specialists or go with a more general platform. The common types of annotation capabilities provided by data annotation tools include building and managing ontologies or guidelines, such as label maps, classes, attributes, and specific annotation types.

Here are just a few examples:

Image or video: Bounding boxes, polygons, polylines, classification, 2-D and 3-D points, or segmentation (semantic or instance), tracking, transcription, interpolation, or transcription.
Text: Transcription, sentiment analysis, net entity relationships (NER), parts of speech (POS), dependency resolution, or coreference resolution.
Audio: Audio labeling, audio to text, tagging, time labeling

An emerging feature in many data annotation tools is automation, or auto-labeling . Using AI, many tools will assist your human labelers to improve their annotations (e.g. automatically convert a four-point bounding box to a polygon), or even automatically annotate your data without a human touch. Additionally, some tools can learn from the actions taken by your human annotators, to improve auto-labeling accuracy.

Some annotation tasks are ripe for automation. For example, if you use pre-annotation to tag images, a team of data labelers can determine whether to resize or delete a bounding box. This can shave time off the process for a team that needs images annotated at pixel-level segmentation. Still, there will always be exceptions, edge cases, and errors with automated annotations, so it is critical to include a human-in-the-loop approach for both quality control and exception handling.

Automation also can refer to the availability of developer interfaces to run the automations. That is, an application programming interface (API) and software development kit (SDK) that allow access to and interaction with the data.

3) Data quality control

The performance of your machine learning and AI models will only be as good as your data. Data annotation tools can help manage the quality control (QC) and verification process. Ideally, the tool will have embedded QC within the annotation process itself.

For example, real-time feedback and initiating issue tracking during annotation is important. Additionally, workflow processes such as labeling consensus, may be supported. Many tools will provide a quality dashboard to help managers view and track quality issues, and assign QC tasks back out to the core annotation team or to a specialized QC team.

4) Workforce management

Every data annotation tool is meant to be used by a human workforce - even those tools that may lead with an AI-based automation feature. You still need humans to handle exceptions and quality assurance as noted before. As such, leading tools will offer workforce management capabilities, such as task assignment and productivity analytics measuring time spent on each task or sub-task.

Your data labeling workforce provider may bring their own technology to analyze data that is associated with quality work. They may use technology, such as webcams, screenshots, inactivity timers, and clickstream data to identify how they can support workers in delivering quality data annotation.

Most importantly, your workforce must be able to work with and learn the tool you plan to use. Further, your workforce provider should be able to monitor worker performance and work quality and accuracy . It’s even better when they offer you direct visibility, such as a dashboard view, into the productivity of your outsourced workforce and the quality of the work performed.

5) Security

Whether annotating sensitive protected personal information (PPI) or your own valuable intellectual property (IP), you want to make sure that your data remains secure. Tools should limit an annotator’s viewing rights to data not assigned to her, and prevent data downloads. Depending on how the tool is deployed, via cloud or on-premise, a data annotation tool may offer secure file access (e.g., VPN).

For use cases that fall under regulatory compliance requirements, many tools will also log a record of annotation details, such as date, time, and the annotation author. However, if you are subject to HIPAA, SOC 1, SOC 2, PCI DSS, or SSAE 16 regulations, it is important to carefully evaluate whether your data annotation tool partner can help you maintain compliance.

6) Integrated labeling services

As mentioned earlier, every tool requires a human workforce to annotate data, and the people and technology elements of data annotation are equally important. As such, many data annotation tool providers offer a workforce network to provide annotation as a service. The tool provider either recruits the workers or provides access to them via partnerships with workforce providers.

While this feature makes for convenience, any workforce skill and capability should be evaluated separately from the tool capability itself. The key here is that any data annotation tool should offer the flexibility to use the tool vendor’s workforce or the workforce of your choice, such as a group of employees or a skilled, professionally managed data annotation team.

6 key features for data annotation tools

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Just a few years ago, there weren’t many data annotation tools available to buy. Most early movers had to use what was available via open source or build their own tools if they wanted to apply AI to solve a painful business problem or create a disruptive product.

Starting in about 2018, a wave of commercial data annotation tools became available, offering full-featured, complete-workflow commercial tools for data labeling. The emergence of these third-party, professionally developed tools began to force a discussion within data science and AI project teams around whether to continue to take a DIY approach and build their own tools or purchase one. And if the answer was to purchase a data annotation tool, they still needed to decide how to select the right tool for their project.

When to build your own data annotation tool

Even though there are third-party tools available to purchase, it may still make business sense to build a data annotation tool. Building your own tool provides you with the ultimate level of control - from the end-to-end workflow of the annotation process, to the type of data you can label and the resulting outputs.

And, as you continue to iterate your business processes and your machine learning models, you can make changes quickly, using your own developers and setting your own priorities. You also can apply technical controls to meet your company’s unique security requirements. And finally, an organization may want to include all of their AI tooling in their intellectual property, and building a data annotation tool internally allows them to do that.

However, when you’re building a tool, you often face many unknowns at the beginning, and the scope of tool requirements can quickly shift and evolve, causing teams to lose time. There is also the additional overhead of standing up the infrastructure needed to develop and run the tooling, as well as development resources required to maintain the data annotation tool.

When to buy a data annotation tool

Generally, buying a tool that is commercially available can be less expensive because you avoid the upfront development and ongoing direct support expenses. This allows you to focus your time and resources on your core project:

Without the distraction of supporting and expanding features and capabilities for an in-house tool that is custom-built; and
Without bearing the ongoing burden of funding the tool to ensure its continued success.

Buying an existing data annotation tool can accelerate your project timeline, enabling you to get started more quickly with an enterprise-ready, tested data labeling tool. Additionally, tooling vendors work with many different customers and can incorporate industry best practices into their data annotation tools. Finally, when it comes to features, you can usually configure a commercial tool to meet your needs, and there are more than one of these kinds of tools available for any data annotation workload.

Of course, a third-party data annotation tool is not typically built with your specific use case or workflow in mind, so you may sacrifice some level of control and customization. And as your project or product evolves, you may find that your data annotation tool requirements change over time. If the tool you originally bought doesn’t support your new requirements, you will need to build or buy integrations or separate tools to meet your new needs.

The open source option for data annotation tools

There are open source data annotation tools available. You can use an open source tool and support it yourself, or use it to jump-start your own build effort. There are many open source projects for tooling related to image, video, natural language processing, and transcription, and such a tool can be a great option for a one-time project.

But often an open source tool will present challenges when you try to scale your project into production, as these tools are typically designed around a single user and offer poor or insufficient workflow options for a team of data labelers. Additionally, you need to have the technical expertise on hand to deploy and maintain the tool. Many people are lured by open source being “free” and forget to factor in the total cost of ownership - the time and expense required to develop the workflows, workforce management, and quality assurance management that are necessary and inherently present in commercial data annotation tools.

Growth stage as an indicator for buy vs. build

Another helpful way to look at the build versus buy question is to consider your stage of organizational growth.

Start: In the early stages of growth, freeware or open source data annotation tools can make sense if you have development resources and you want to build your own tool. You also could choose a workforce that provides a data annotation tool. But be careful not to unnecessarily tie your data annotation tool to your workforce; you’ll want the flexibility to make changes later.
Scale: If you’re at the growth stage, you might want the ability to customize commercial data annotation tools, and you can do that with little to no development resources. If you build, you’re going to need to allocate resources to maintain and improve your tool. Keep in mind to consider existing storage and, if you use a cloud vendor, make sure they can work with your requirements.
Sustain: When you’re operating at scale, it’s likely to be important for you to have control, enhanced data security, or the agility to make changes, such as feature enhancements. In that case, open source tools that are self-built and managed might be your best bet.

When you are looking for a data annotation tool, an important consideration is the growth stage of your organization. In the early stages of growth, open source or crowdsourcing make sense. At the growth stage, consider commercial data annotation tools or building your own. At scale, you might want the control, enhanced data security, or agility you get from building your own data annotation tool.

There is a lot to consider in the build vs buy equation. If, after considering all of the factors, you conclude that the time and expense is not worth a DIY approach and the potential gain of customization and retaining IP, then the next decision you will need to make is about which commercial tool you choose to purchase. In this section we will explore some of those considerations.

1) What is your use case?

First and foremost, the type of data you want to annotate and your business processes for doing the work will influence your tool choice. There are tools for labeling text, image, and video. Some image labeling tools also have video labeling capabilities.

Of note, more and more data annotation tool providers are realizing they want to do more than provide a singular tool - they want to provide a holistic technology platform for data annotation for machine learning. A simple data annotation tool provides features that make it easy to enrich the data. A platform provides an environment that supports the data annotation and AI development process.

A platform may include features such as multiple annotation options (e.g., 2-D, 3-D, audio, text), more than one storage option (e.g., local, network, cloud), or quality control workflow. It also may be able to accept pre-annotated data or may include embedded neural networks that learn from manual annotations made using the platform. Considering a platform may be helpful if you anticipate your project or product needs evolving significantly over time, as a platform may provide greater flexibility in the future.

2) How will you manage quality control requirements?

How you want to measure and control quality is also an important consideration for your data annotation tool. Many commercially-available tools have quality control (QC) features built-in that can review, provide feedback, and correct tasks. For example, QC options might include:

Consensus - Annotator agreement determines quality. For example, when annotators disagree on an edge case, the task is passed to a third annotator or more until a percentage of certainty is reached. Feedback can be provided to the workforce to learn how to correctly annotate those edge cases.
Gold standard - The correct answer is known. The tool measures quality based on correct and incorrect tasks.
Sample review - The tools reviews a random sample of completed tasks for accuracy.
Intersection over union (IoU) - This is a consensus model used in object detection within images. It compares your hand-annotated, ground-truth images with the annotations your model predicts.

Some tools can even automate a portion of your QC. However, whenever you are using automation for a portion of your data labeling process, you will need people to perform QC on that work. For example, optical character recognition (OCR) software has an error rate of 1% to 3% per character. On a page with 1,800 characters, that’s 18-54 errors. For a 300-page book, that’s 5,400-16,200 errors. You will want a process that includes a QC layer performed by skilled labelers with context and domain expertise.

3) Who will be using the tool?

An often overlooked aspect of tool selection is workforce. Whether your data is annotated by employees or contractors, crowdsourcing, or an outsourcing provider, your workforce will need access to and training to use your data annotation tool, with specific task instructions unique to your use case. Make sure you take into account the answers to these questions:

Do you have access to a workforce that has pre-existing knowledge of viable commercial tools for your project?
Does that team have prior experience using the tool(s) you are considering?
If not, do you have detailed documentation and a proven training approach to bring the workforce up to speed?
Do you have a process by which you can ensure the required level of quality for your project?

4) Do you need a vendor or a partner?

The company you buy a data annotation tool from can be just as important as the tool itself. Here, you’ll want to consider how easy it is to do business with the company that’s providing the tool and their openness for collaboration. AI development is an iterative process, and you will need to make changes along the way. Are they willing to consider feedback or ideas for new features for their tool that would make your tasks easier or make your AI models run cleaner and with better results? Aim to find a partner who is willing to work with you on such things, not simply a vendor to provide a tool.

As you research your workforce options, you may discover some data labeling services that provide their own tool. However, be careful not to tie your tool to your workforce unnecessarily. You’ll want the flexibility to change either your workforce or your tool, based on your business needs and the solutions available to you, especially as new tools and workforce options emerge. A data labeling service should be able to provide best practices and share recommendations for choosing your tool based on their workforce strategy.

Also, keep in mind that your annotation tasks are likely to change over time. Every machine learning modeling task is different. The set of instructions you are using to collect, clean, and annotate your data today may change in the coming weeks - even days. Anticipating those changes is helpful, and you’ll want to consider that when you’re making the decision about the data annotation tool you select and the workforce that will use it to label your data.

Here’s a closer look at some of the data annotation tools we consider to be among the best available on the market today.

Commercial Data Annotation Tools

Commercially-viable data annotation tools are likely your best choice, particularly if your company is at the growth or enterprise stage. If you are operating at scale and want to sustain that growth over time, you can get commercially-available tools and customize them with few development resources of your own.

Open Source Data Annotation Tools

Open source data annotation tools allow you to use or modify the source code. You can change or customize features to fit your needs. Developers who use open source tools are part of a collaborative community of users who can share use cases, best practices, and feature improvements made by altering the original source code.

Open source tools can give you more control over features and can provide great flexibility as your tasks and data operations evolve. However, using open source tools comes with the same commitment as building your own tool. You will have to make investments to maintain the platform over time, which can be costly.

While open source tools can be good for learning or testing early versions of a commercial application, they often present barriers to scale. This is because most open source tools are not comprehensive labeling solutions and lack robust dataset management, label automation, or other features that drive efficiency (like data clustering). In addition, few open source tools provide quality assurance workflows or accuracy analytics which can hinder data quality.

It’s important to know that open source communities provide support mostly via on-line documentation, FAQs, and tutorials. There are no support numbers to call and some open source tools don’t provide data privacy and security measures needed to comply with GDPR and HIPAA.

There are several open source data annotation tools available, many of which have been available for years and have improved over time.

You will uncover buy vs. build implications throughout your product development lifecycle. From sourcing the data to labeling, modeling, deployment, and improvements - your data annotation tool plays a key role in your project’s success. That’s why your tool choice is so important - because it affects your workflow from the beginning stages of model development through model testing and into production.

With a market size of USD $805.6 million in 2022 , data annotation tools will expand as adoption of data annotation tools increases in the automotive, retail, and healthcare industries. As new options emerge, you may want to consider what is available to you.

Why change data annotation tools?

As you train, test, and validate your model - and even as you tune it in production, your data annotation needs may change. A tool that was built for your first purpose might not serve you as well in the future as your use case, tasks, and business rules evolve. That’s why it’s important to avoid getting into a long-term contract with a single tool or workforce provider - or tying your tool to your workforce.

Here are a few examples of reasons you might want to change your tool during a project:

You began building a tool but are now considering buying because commercial tools have added new features that meet your needs.
The tool doesn’t have the automation or the automation features you want.
Your cost increases for access to the commercial tool.

How do I change data annotation tools?

When you change your data annotation tool in the middle of training or production, you’ll likely ask the same questions you’d ask if you were buying the tool for a new project. However, there will be considerations regarding the ease of transferring your data into a new tool and resuming data annotation in the new tool.

For example, you will have to anticipate and manage details related to:

Introducing a different data ingestion pipeline
How data is stored
Output format
Use of a new tool - and training your data workers to use it
Your workforce provider’s technology to track the quality and productivity of its workers, and how they capture the data required to do it.

While we know it’s important to be flexible when it comes to your data annotation tool, we have yet to learn how long one tool can meet your needs and how long you should wait before evaluating your options again. The data annotation tool ecosystem is just gathering steam, and those who were among the first teams to monetize their data annotation tools are just starting to renew contracts with their earliest adopters.

This is one aspect of the market we’re watching so we can provide exceptional consultative service to our clients and ensure they are using the best-fit tool for their needs.

Here are questions to keep in mind when you’re speaking with a data annotation tool provider:

Strategic Approach

Of all of the features available with your tool, what does your team consider to be your tool’s specialty - and why?
How long have you been building, maintaining, and supporting this data annotation tool?
How is your tool different from other commercially-available tools?
Do you consider your product to be a tool or a platform? What other aspects of the machine learning data labeling process does your tool support?
Is your team open to receiving feedback about your data annotation tool, its features, and ways it could be improved to better serve the needs of our use case?
What are your pricing methods? (e.g., monthly, annual, by annotation, by worker)

Key Features

Do you offer dataset management?
Where can files be stored? What capacity does the tool support, in terms of how much data can be moved into the tool? Can I upload pre-annotated images into the tool?
Do you offer an API and/or SDK? If so, how robust are they?
Do you offer data management?
Can I bulk upload classes and attributes into the tool?
Does your tool allow us to deploy a large and growing workforce to use it?
What security compliance or certifications does your tool have?
Is quality control (QC) built into your tooling platform? What does that workflow look like?
What kind of quality assurance (QA) do you provide?

Machine Learning

Have you built any AI into your tool?
Can I bring my own algorithm and plug it into your tool?

Though the specific tools suggested above are a great place to start, it’s best to avoid dependence on any single platform for your data annotation needs. After all, no two datasets present exactly the same challenges, and no particular tool will be the best option in all circumstances. Because training data challenges are unique and dynamic in nature, tying your workforce to one tool can be a strategic liability.

For a more flexible approach to labeling text, images, and video, you’ll need to develop a versatile team that can adapt to new tools. At CloudFactory, this emphasis on versatility guides how we select and train our cloud workers. We hire team members with the skills to work on any platform our clients prefer. No matter the tool you use or the type of training data you need, we have workers ready and able to get started.

The People + Process Component

The maturity of your data annotation tool and its features impact how you and your data workforce will design workflow, quality control, and many other aspects of your data work. A tool that doesn’t take your workforce and your processes into consideration will cost you time and efficiency in building workarounds for things that you’ll wish were native within the tool.

CloudFactory delivers the people and the process, and we know data annotation because we’ve been doing it for the better part of a decade, working remotely for our clients. Our data annotation teams are vetted, trained, and actively managed to deliver higher engagement, accountability, and quality.

Work from anywhere - We work how you work, as an extension of your team. We can use any tool and follow the rules you set. Using our proprietary platform, you have direct communication with a team leader to provide feedback. Workers can share their observations to drive improved processes, higher productivity, and better quality.
Scale the work - We can flex up or down, based on your business requirements.
Select and train top-notch workers - Our workforce strategy values people, and we make sure workers understand the importance of the tasks they are doing for your business. We monitor worker performance for productivity and quality, and our team leaders come alongside workers to train and encourage them.
Flexible pricing model - You can scale work up or down without renegotiating your contract. We do not lock you into a long-term contract or tie our workforce to your tool.

Are you ready to select the right data annotation tool? Find out how we can help you save time and money.

Reviewers Anthony Scalabrino , sales engineer at CloudFactory , a provider of professionally managed teams for data annotation for machine learning.

Nir Buschi , Co-founder & Chief Business Officer at Dataloop AI , an enterprise-grade data platform for AI systems in development and in production, providing an end-to-end data workflow including data annotation, quality control, data management, automation pipelines and autoML.

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Frequently asked questions, what is annotated data.

In supervised or semi-supervised machine learning, annotated data is labeled, tagged, or processed for the features you want your machine learning system to learn to recognize. An example of annotated data is sensor data from an autonomous vehicle, where the data has been enriched to show exactly where there are pedestrians and other vehicles.

What is a data annotator?

A data annotator is: 1) someone who works with data and enriches it for use with machine learning; or 2) an auto labeling feature, or automation, that is built into a data annotation tool to enrich data. That automation is powered by machine learning that makes predictions about your annotations based on the training data it has consumed and the tuning of the model during testing and validation.

What is data annotation?

In supervised or semi-supervised machine learning, data annotation is the process of labeling data to show the outcome you want your machine learning model to predict. You are enriching - also known as labeling, tagging, transcribing, or processing - a dataset with the features you want your machine learning system to learn to recognize. Ideally, once you deploy your model, the machine will be able to recognize those features on its own and make a decision or take some action as a result.

What are data annotation tools?

Data annotation tools are cloud-based, on-premise, or containerized software solutions that can be used to label or annotate production-grade training data for machine learning. They can be available via open source or freeware, or they may be offered commercially, for lease. Data annotation tools are designed to be used with specific types of data, such as image, text, audio, spreadsheet, sensor, photogrammetry, or point-cloud data.

What is an image annotation tool?

An image annotation tool is a cloud-based, on-premise or containerized software solution that can be used to label, tag, or annotate images or frame-by-frame video for production-grade training data for machine learning. Features may include bounding boxes, polygons, 2-D and 3-D points, or segmentation (semantic or instance), or transcription. Some image annotation tools include quality control features such as intersection over union (IoU), a consensus model used in object detection within images. It compares your hand-annotated, ground-truth images with the annotations your model predicts.

What’s the best image annotation tool?

The best image annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements. Dataloop , Encord , Hasty , Labelbox , Pix4D , Pointly , and Segments.ai offer commercial annotation tools to label images that are used to train, test, and validate machine learning algorithms. CVAT and QGIS are open source tools you can use and customize for your own image annotation needs.

What is a video annotation tool?

A video annotation tool is a cloud-based, on-premise or containerized software solution that can be used to label or annotate video or frame-by-frame images from video for production-grade training data for machine learning. It can be available via open source or freeware, or it may be offered commercially, for lease. Features may include bounding boxes, polygons, 2-D and 3-D points, or segmentation (semantic or instance).

What’s an online annotation tool?

An online annotation tool is a cloud-based, on-premise, or containerized software solution that can be used to label or annotate production-grade training data for machine learning. It can be available via open source or freeware, or it may be offered commercially. Online annotation tools are designed to be used with specific types of data, such as image, text, video, audio, spreadsheet, or sensor data.

What are text annotation tools?

Text annotation tools are cloud-based, on-premise, or containerized software solutions that can be used to annotate production-grade training data for machine learning. This process also can be called labeling, tagging, transcribing, or processing. Text annotation tools can be available via open source or freeware, or they may be offered commercially.

Is there a list of video annotation tools?

Dataloop , Encord , Hasty , Labelbox , and Segments.ai offer commercial annotation tools that can be used to label video to train, test, and validate machine learning algorithms. CVAT is an open source video annotation tool you can use or customize for your own video annotation needs. The best video annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements.

What’s the best text annotation tool?

The best text annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements. DatasaurAI and Labelbox offer commercial annotation tools that can be used to analyze language and sentiment to train, test, and validate machine learning algorithms.

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What is Data Annotation in Machine Learning?

In today’s increasingly world, the need for accurate and reliable data annotation has never been greater. From self-driving cars to virtual assistants, machine learning models rely on annotated data to function effectively. Without proper annotation, even the most advanced algorithms would struggle to make sense of the vast amounts of unstructured data available. Data annotation plays a crucial role in machine learning, enabling computers to understand and process vast amounts of information.

What is Data Annotation?

In simple terms, data annotation involves labeling data to make it intelligible for machines. By annotating data, we provide context and meaning to raw information, allowing machine learning algorithms to recognize patterns, make predictions, and perform complex tasks. Computers lack the inherent ability to process and comprehend visual information as humans do.

Therefore, data annotation serves as the bridge between the raw data and the AI algorithms, enabling machines to make informed predictions and decisions. By assigning labels, tags, or metadata to specific elements within the dataset, it provides the necessary context for machines to learn and analyze patterns.

Importance of data annotation in machine learning

The process of data annotation is vital for training machine learning models. By labeling data with relevant tags, categories, or attributes, we create a ground truth dataset that serves as the basis for teaching algorithms how to interpret new, unseen data. This labeled data allows machine learning models to learn from examples and generalize their knowledge to make accurate predictions or classifications.

Accurate data annotation is crucial for ensuring the performance and reliability of machine learning models. The quality of the annotated data directly affects the model’s ability to learn, adapt, and make informed decisions. Without accurate annotations, models may produce inaccurate or biased results, leading to serious consequences in real-world applications.

Types of data annotation techniques

There are various techniques used for data annotation, each suited for different types of machine-learning tasks. Some common types of data annotation techniques include:

Image Annotation: In image annotation, objects or regions of interest within an image are identified and labeled. This technique is commonly used in computer vision tasks such as object detection, image segmentation, and facial recognition .
Text Annotation: Text annotation involves labeling textual data, such as documents, sentences, or words, with relevant tags or categories. This technique is widely used in natural language processing tasks, including sentiment analysis, named entity recognition, and text classification.
Audio Annotation: Audio annotation involves transcribing and labeling audio data, such as speech or sound events. This technique is essential for speech recognition, audio classification, and sound event detection applications.
Video Annotation: Video annotation involves labeling objects, actions, or events within video sequences. This technique is crucial for video analysis tasks, such as action recognition, object tracking, and surveillance systems.

Choosing the appropriate data annotation technique depends on the specific requirements of the machine learning task at hand.

Common challenges in data annotation

While data annotation is a crucial step in machine learning, it is not without its challenges. Some common challenges in data annotation include:

Subjectivity: Data annotation can be subjective, as different annotators may interpret and label data differently. This subjectivity can introduce inconsistencies and affect the overall quality of the annotated data.
Scale: Annotating large datasets can be time-consuming and resource-intensive. As the volume of data increases, the annotation process becomes more complex and requires efficient tools and methodologies to ensure accuracy and efficiency.
Labeling Ambiguity: Some data may be inherently ambiguous or require domain-specific knowledge to label accurately. Annotators must possess the necessary expertise and context to interpret and label such data correctly.

Addressing these challenges requires a combination of expertise, efficient annotation tools, and well-defined annotation guidelines to ensure consistent and accurate annotations.

How to become a data annotator ?

Becoming a data annotator requires a combination of domain knowledge, attention to detail, and proficiency in annotation tools. Here are some steps to become a data annotator:

Develop domain expertise: Gain knowledge and understanding in the domain you wish to annotate data for. This could be in fields such as computer vision, natural language processing, or audio processing.
Familiarize yourself with annotation tools: Learn to use popular annotation tools and software, such as Labelbox or Supervisory. Practice using these tools to annotate sample datasets and familiarize yourself with their features and functionalities.
Stay updated: Keep up with the latest trends and developments in the field of data annotation and machine learning. Attend conferences, read research papers, and participate in online communities to stay informed about new techniques and best practices.
Build a portfolio: Create a portfolio of annotated datasets that showcase your skills and expertise. This will help you demonstrate your capabilities to potential clients or employers.
Seek opportunities: Look for freelance or job opportunities in data annotation. Online platforms and marketplaces like Upwork or Kaggle often have projects available for data annotators.

By following these steps, you can establish yourself as a skilled data annotator and contribute to the development of machine learning models.

Best practices for data annotation

To ensure accurate and reliable annotations, it is essential to follow best practices in data annotation. Here are some key guidelines to consider:

Annotation guidelines: Develop clear and concise annotation guidelines that define the criteria for labeling data. These guidelines should be comprehensive, and unambiguous, and provide examples to ensure consistency among annotators.
Quality control: Implement quality control measures to evaluate the accuracy and consistency of annotations. This can involve regular reviews, inter-annotator agreement checks, or using gold-standard datasets for benchmarking.
Iterative process: It is an iterative process, and annotations may need refinement over time. Encourage feedback and collaboration among annotators to improve the quality of annotations.
Data augmentation: Consider using data augmentation techniques to increase the diversity and variability of annotated data. This can improve the model’s ability to generalize and perform well on unseen data.

By following these best practices, data annotation can be performed efficiently and consistently, leading to high-quality annotated datasets .

Case studies showcasing the impact of accurate data annotation

Accurate data annotation has a significant impact on the performance and reliability of machine learning models. Here are two case studies that highlight its importance:

1. Autonomous Driving: In the field of autonomous driving, accurate data annotation is crucial for training models to recognize and respond to various objects and scenarios on the road. Through accurate annotation of millions of images and video frames, machine learning models can learn to identify pedestrians, vehicles, traffic signs, and other critical elements, enabling safe and efficient autonomous driving.

2. Medical Diagnosis: In medical diagnosis, accuracy is essential for training models to detect and classify diseases from medical images or patient records. By annotating large datasets of medical images with precise labels, machine learning models can assist doctors in diagnosing conditions such as cancer, cardiovascular diseases, or neurological disorders, leading to early detection and better patient outcomes.

These case studies demonstrate the transformative impact of accurate data annotation in real-world applications, making it an integral part of the machine-learning pipeline.

Outsourcing data annotation services

For organizations looking to leverage the benefits of data annotation without the resources or expertise to perform annotation in-house, outsourcing data annotation services can be a viable option. Outsourcing allows businesses to access a global pool of skilled annotators, scale annotation efforts, and benefit from specialized tools and expertise.

When considering outsourcing data annotation services, it is essential to ensure data privacy and confidentiality, establish clear communication channels, and define quality control measures to maintain the accuracy and consistency of annotations.

As machine learning continues to advance and find applications in various industries, the importance of data annotation will only grow. Accurate and reliable annotated data is the foundation on which machine learning models are built, enabling them to understand and interpret complex information.

The future of this lies in the development of more sophisticated annotation techniques and tools that can handle diverse data types and improve efficiency. Additionally, advancements in artificial intelligence and automation may further streamline the annotation process, reducing the time and effort required.

By understanding its significance, we can unlock the full potential of machine learning and drive innovation in various domains. With accurate annotations, we can create intelligent systems that revolutionize industries, improve decision-making, and enhance our daily lives.

If you’re interested in learning more about data annotation or need custom data annotation services, get in touch with our team today.

Harness Twine’s established global community of over 400,000 freelancers from 190+ countries to scale your dataset collection quickly. We have systems to record, annotate and verify custom video datasets at an order of magnitude lower cost than existing methods.

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Data Annotation Tutorial: Definition, Tools, Datasets

Data is an integral part of all machine learning and deep learning algorithms .

It is what drives these complex and sophisticated algorithms to deliver state-of-the-art performances.

If you want to build truly reliable AI models , you must provide the algorithms with data that is properly structured and labeled.

And that's where the process of data annotation comes into play.

You need to annotate data so that the machine learning systems can use it to learn how to perform given tasks.

Data annotation is simple, but it might not be easy 😉 Luckily, we are about to walk you through this process and share our best practices that will save you plenty of time (and trouble!).

Here’s what we’ll cover:

What is data annotation?

Types of data annotations.

Automated data annotation vs. human annotation

V7 data annotation tutorial

Solve any video or image labeling task 10x faster and with 10x less manual work.

Don't start empty-handed. Explore our repository of 500+ open datasets and test-drive V7's tools.

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Essentially, this comes down to labeling the area or region of interest—this type of annotation is found specifically in images and videos. On the other hand, annotating text data largely encompasses adding relevant information, such as metadata, and assigning them to a certain class.

In machine learning , the task of data annotation usually falls into the category of supervised learning, where the learning algorithm associates input with the corresponding output, and optimizes itself to reduce errors.

Here are various types of data annotation and their characteristics.

Image annotation

Image annotation is the task of annotating an image with labels. It ensures that a machine learning algorithm recognizes an annotated area as a distinct object or class in a given image.

It involves creating bounding boxes (for object detection ) and segmentation masks (for semantic and instance segmentation) to differentiate the objects of different classes. In V7, you can also annotate the image using tools such as keypoint, 3D cuboids, polyline, keypoint skeleton, and a brush.

💡 Pro tip: Check out 13 Best Image Annotation Tools to find the annotation tool that suits your needs.

Image annotation is often used to create training datasets for the learning algorithms.

Those datasets are then used to build AI-enabled systems like self-driving cars, skin cancer detection tools, or drones that assess the damage and inspect industrial equipment.

💡 Pro tip: Check out AI in Healthcare and AI in Insurance to learn more about AI applications in those industries.

Now, let’s explore and understand the different types of image annotation methods.

Bounding box

The bounding box involves drawing a rectangle around a certain object in a given image. The edges of bounding boxes ought to touch the outermost pixels of the labeled object.

Otherwise, the gaps will create IoU (Intersection over Union) discrepancies and your model might not perform at its optimum level.

💡 Pro tip: Read Annotating With Bounding Boxes: Quality Best Practices to learn more.

The 3D cuboid annotation is similar to bounding box annotation, but in addition to drawing a 2D box around the object, the user has to take into account the depth factor as well. It can be used to annotate objects such on flat planes that need to be navigated, such as cars or planes, or objects that require robotic grasping.

You can annotate with cuboids to build to train the following model types:

- Object Detection

- 3D Cuboid Estimation

- 6DoF Pose Estimation

Creating a 3D cuboid in V7 is quite easy, as V7's cuboid tool automatically connects the bounding boxes you create by adding a spatial depth. Here's the image of a plane annotated using cuboids.

While creating a 3D cuboid or a bounding box, you might notice that various objects might get unintentionally included in the annotated region. This situation is far from ideal, as the machine learning model might get confused and, as a result, misclassify those objects.

Luckily, there's a way to avoid this situation—

And that's where polygons come in handy. What makes them so effective is their ability to create a mask around the desired object at a pixel level.

V7 offers two ways in which you can create pixel-perfect polygon masks.

a) Polygon tool

You can pick the tool and simply start drawing a line made of individual points around the object in the image. The line doesn't need not be perfect, as once the starting and ending points are connected around the object, V7 will automatically create anchor points that can be adjusted for the desired accuracy.

Once you've created your polygon masks, you can add a label to the annotated object.

Apples annotated using the polygon tool un V7

b) Auto-annotation tool

V7's auto-annotate tool is an alternative to manual polygon annotation that allows you to create polygon and pixel-wise masks 10x faster.

💡 Pro tip: Ready to train your models? Have a look at Mean Average Precision (mAP) Explained: Everything You Need to Know.

Keypoint tool

Keypoint annotation is another method to annotate an object by a series or collection of points.

This type of method is very useful in hand gesture detection, facial landmark detection, and motion tracking. Keypoints can be used alone, or in combination to form a point map that defines the pose of an object.

Keypoint skeleton tool

V7 also offers keypoint skeleton tool—a network of keypoints connected by vectors, used specifically for pose estimation.

It is used to define the 2D or 3D pose of a multi-limbed object. Keypoints skeletons have a defined set of points that can be moved to adapt to an object’s appearance.

You can use keypoint annotation to train a machine learning model to mimic human pose and then extrapolate their functionality for task-specific applications, for example, AI-enabled robots.

See how you can annotate your image and video data using the keypoint skeleton in V7.

💡 Pro tip: Check out 27+ Most Popular Computer Vision Applications and Use Cases.

Polyline tool

Polyline tool allows the user to create a sequence of joined lines.

You can use this too by clicking around the object of interest to create a point. Each point will create a line by joining the current point with the previous one. It can be used to annotate roads, lane marking, traffic signs, etc.

Bike lane annotation using polyline tool in V7

Semantic segmentation

Semantic segmentation is the task of grouping together similar parts or pixels of the object in a given image. Annotating data using this method allows the machine learning algorithm to learn and understand a specific feature, and it can help it to classify anomalies.

Semantic segmentation is very useful in the medical field, where radiologists use it to annotate X-Ray, MRI, and CT scans to identify the region of interest. Here's an example of a chest X-Ray annotation.

AI chest X-Ray annotation analysis in V7

If you are looking for medical data, check out our list of healthcare datasets and see how you can annotate medical imaging data using V7.

Video annotation

Similar to image annotation, video annotation is the task of labeling sections or clips in the video to classify, detect or identify desired objects frame by frame.

Video annotation uses the same techniques as image annotation like bounding boxes or semantic segmentation, but on a frame-by-frame basis. It is an essential technique for computer vision tasks such as localization and object tracking.

Here's how V7 handles video annotation .

Tackle any video format frame by frame. Use AI models to label sequences. Interpolate any annotation.

Mockups of videos annotated on V7 platform

Text annotation

Data annotation is also essential in tasks related to Natural Language Processing (NLP).

Text annotation refers to adding relevant information about the language data by adding labels or metadata. To get a more intuitive understanding of text annotation let's consider two examples.

1. Assigning Labels

Adding labels means assigning a sentence with a word that describes its type. It can be described with sentiments, technicality, etc. For example, one can assign a label such as “happy” to this sentence “I am pleased with this product, it is great”.

2. Adding metadata

Similarly, in this sentence “I’d like to order a pizza tonight”, one can add relevant information for the learning algorithm, so that it can prioritize and focus on certain words. For instance, one can add information like “I’d like to order a pizza ( food_item ) tonight ( time )”.

Now, let’s briefly explore various types of text annotations.

Sentiment Annotation

Sentiment annotation is nothing but assigning labels that represent human emotions such as sad, happy, angry, positive, negative, neutral, etc. Sentiment annotation finds application in any task related to sentiment analysis (e.g. in retail to measure customer satisfaction based on facial expressions)

Intent Annotation

The intent annotation also assigns labels to the sentences, but it focuses on the intent or desire behind the sentence. For instance, in a customer service scenario, a message like “I need to talk to Sam ”, can route the call to Sam alone, or a message like “I have a concern about the credit card ” can route the call to the team dealing credit card issues.

Named Entity Annotation (NER)

Named entity recognition (NER) aims to detect and classify predefined named entities or special expressions in a sentence.

It is used to search for words based on their meaning, such as the names of people, locations, etc. NER is useful in extracting information along with classifying and categorizing them.

Semantic annotation

Semantic annotation adds metadata, additional information, or tags to text that involves concepts and entities, such as people, places, or topics, as we saw earlier.

Automated data annotation vs. human annotations.

As the hours pass by, human annotators get tired and less focused, which often leads to poor performance and errors. Data annotation is a task that demands utter focus and skilled personnel, and manual annotation makes the process both time-consuming and expensive.

That's why leading ML teams bet on automated data labeling.

Here's how it works—

Once the annotation task is specified, a trained machine learning model can be applied to a set of unlabeled data. The model will then be able to predict the appropriate labels for the new and unseen dataset.

Here's how you can create an automated workflow in V7.

However, in cases where the model fails to label correctly, humans can intervene, review, and correct the mislabelled data. The corrected and reviewed data can be then used to train the labeling model once again.

Automated data labeling can save you tons of money and time, but it can lack accuracy. In contrast, human annotation can be much more costly, but it tends to be more accurate.

Finally, let me show you how you can take your data annotation to another level with V7 and start building robust computer vision models today.

To get started, go ahead and sign up for your 14-day free trial.

Once you are logged in, here's what to do next.

1. Collect and prepare training data

First and foremost, you need to collect the data you want to work with. Make sure that you access quality data to avoid issues with training your models.

Feel free to check out public datasets that you can find here:

65+ Best Free Datasets for Machine Learning
20+ Open Source Computer Vision Datasets

Once the data is downloaded, separate training data from the testing data . Also, make sure that your training data is varied, as it will enable the learning algorithm to extract rich information and avoid overfitting and underfitting.

2. Upload data to V7

Once the data is ready, you can upload it in bulk. Here's how:

1. Go to the Datasets tab in V7's dashboard, and click on “+ New Dataset”.

2. Give a name to the dataset that you want to upload.

It's worth mentioning that V7 offers three ways of uploading data to their server.

One is the conventional method of dragging and dropping the desired photos or folder to the interface. Another one is uploading by browsing in your local system. And the third one is by using the command line (CLI SDK) to directly upload the desired folder into the server.

Once the data has been uploaded, you can add your classes. This is especially helpful if you are outsourcing your data annotation or collaborating with a team, as it allows you to create annotation checklist and guidelines.

If you are annotating yourself, you can skip this part and add classes on the go later on in the "Classes" section or directly from the annotated image.

💡 Pro tip: Not sure what kind of model you want to build? Check out 15+ Top Computer Vision Project Ideas for Beginners.

3. Decide on the annotation type

If you have followed the steps above and decided to “Add New Class”, then you will have to add the class name and choose the annotation type for the class or the label that you want to add.

As mentioned before, V7 offers a wide variety of annotation tools , including:

Auto-annotation
Keypoint skeleton

Once you have added the name of your class, the system will save it for the whole dataset.

Image annotation experience in V7 is very smooth.

In fact, don't believe just me—here's what one of our users said in his G2 review:

V7 gives fast and intelligent auto-annotation experience. It's easy to use. UI is really interactive.

Apart from a wide range of available annotation tools, V7 also comes equipped with advanced dataset management features that will help you organize and manage your data from one place.

And let's not forget about V7's Neural Networks that allow you to train instance segmentation, image classification , and text recognition models.

Unlike other annotation tools, V7 allows you to annotate your data as a video rather than individual images.

You can upload your videos in any format, add and interpolate your annotations, create keyframes and sub annotations, and export your data in a few clicks!

Uploading and annotating videos is as simple as annotating images.

V7 offers frame by frame annotation method where you can essentially create a bounding box or semantic segmentation per-frame basis.

Annotating videos frame-by-frame in V7 and labels stacking

Apart from image and video annotation , V7 provides text annotation as well. Users can take advantage of the Text Scanner model that can automatically read the text in the images.

To get started, just go to the Neural Networks tab and run the Text Scanner model.

Once you have turned it on you can go back to the dataset tab and load the dataset. It is the same process as before.

Now you can create a new bounding box class. The bounding box will detect text in the image. You can specify the subtype as Text in the Classes page of your dataset.

Once the data is added and the annotation type is defined you can then add the Text Scanner model to your workflow under the Settings page of your dataset.

After adding the model to your workflow map your new text class.

Now, go back to the dataset tab and send your data the text scanner model by clicking on ‘Advance 1 Stage’; this will start the training process.

Once the training is over the model will detect and read text on any kind of image, whether it's a document, photo, or video.

💡 Pro tip: If you are looking for a free image annotation tool, check out The Complete Guide to CVAT—Pros & Cons

Data annotation: next steps.

Nice job! You've made it that far 😉

By now, you should have a pretty good idea of what is data annotation and how you can annotate data for machine learning.

We've covered image, video, and text annotation, which are used in training computer vision models. If you want to apply your new skills, go ahead, pick a project, sign up to V7, collect some data, and start labeling it to build image classifier or object detectors!

💡 To learn more, go ahead and check out:

An Introductory Guide to Quality Training Data for Machine Learning

Simple Guide to Data Preprocessing in Machine Learning

Data Cleaning Checklist: How to Prepare Your Machine Learning Data

3 Signs You Are Ready to Annotate Data for Machine Learning

The Beginner’s Guide to Contrastive Learning

9 Reinforcement Learning Real-Life Applications

Mean Average Precision (mAP) Explained: Everything You Need to Know

A Step-by-Step Guide to Text Annotation [+Free OCR Tool]

The Essential Guide to Data Augmentation in Deep Learning

Nilesh Barla is the founder of PerceptronAI, which aims to provide solutions in medical and material science through deep learning algorithms. He studied metallurgical and materials engineering at the National Institute of Technology Trichy, India, and enjoys researching new trends and algorithms in deep learning.

“Collecting user feedback and using human-in-the-loop methods for quality control are crucial for improving Al models over time and ensuring their reliability and safety. Capturing data on the inputs, outputs, user actions, and corrections can help filter and refine the dataset for fine-tuning and developing secure ML solutions.”

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It’s pretty well known that machine learning (ML) is deeply involved in advanced technologies like autonomous vehicles, robotics, drones, medical imaging, and security systems. But what many don’t know is the key driver that brings many of these technologies to life — called image annotation. It is one of the most important components of computer vision and image recognition common in the inner-workings of these exciting fields.

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What Is Image Annotation?

Image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords in a digital image. Data labelers use tags , or metadata, to identify characteristics of the data fed into an AI or ML model to learn to recognize things the way a human would. Tagged images are then used to train the algorithm to identify those characteristics when presented fresh, unlabeled data.

Image annotations are important drivers of computer vision algorithms because they form the training data that is input to supervised learning . If the annotations are of high quality, the model will “see” the world and create accurate insights for the application. If they are low quality, ML models will not provide a clear picture of relevant real-world objects and will not perform well. Annotated data is particularly important when the model is trying to solve a new field or domain.

Types of Image Annotation

There are several key forms of algorithm-based image annotation methods that are used by ML engineers.

Bounding Box Annotation

Entails making a rectangular drawing of lines from one corner of an object to another in an image, based on its shape.

Polygon Annotation

Boundaries of an item in a frame are annotated with high precision, allowing the object to be identified with the right size and form. Polygon annotation is common for recognizing things like street signs, logo images, and facial recognition.

Cuboid Annotation

This 3D type of annotation involves high-quality labeling and marking to highlight 3D drawing forms. It is used to determine the depth or distance of items from things like buildings or cars and helps identify space and volume, so it’s common in construction and medical imaging.

Text Annotation

Language can be very difficult to interpret, so text annotation helps create labels in a text document to identify phrases or sentence structures. It helps prepare datasets for training so that the model can understand language, purpose, and even emotion behind the words.

Semantic Segmentation

Also known as picture segmentation, this type groups sections of an image that are part of the same object class. Pixels in an image are categorized to create a pixel-level prediction.

Use Cases for Image Annotation

With the help of digital photos, videos and ML models, computers can learn to understand visual environments as humans do. High-quality annotations help drive the accuracy of computer vision models that are used in an increasingly wide range of applications .

Autonomous Vehicles

ML algorithms for autonomous cars must of course be able to recognize things like road signs, traffic lights, bike lanes, and other potential road risks like bad weather. Picture annotation is common in various areas, such as advanced driver-assistance systems (ADAS), navigation and steering response, road object (and dimension) detection, and movement observations (such as with pedestrians).

Surveillance and Security

Security cameras are everywhere these days, and companies are throwing large sums into surveillance equipment to avoid theft, vandalism, and accidents. Image annotation is used in crowd detection, night and thermal vision, traffic motion and monitoring, pedestrian tracking, and face identification. ML engineers can train datasets for video and surveillance equipment using annotated photos to provide a more secure environment.

Agriculture

Even farmers are getting in on the game. Image annotation helps create content-driven data labeling to reduce human injury and protect crops. It also simplifies common agricultural tasks such as livestock management and the detection of unwanted or damaged crops.

Key Challenges for Image Annotation in ML

While the benefits of deploying image annotation are plentiful, there are also a number of key challenges ML engineers and data science teams face.

Selecting the Right Annotation Tools

ML algorithms must be taught to recognize entities within digital visual images the way humans do. Organizations must understand what aspects of data types they want to use for data labeling, and they will need the right combination of digital annotation tools and a workforce that knows how to use them optimally.

Choosing Between Automated and Human Annotation

Using human resources to conduct image annotation, rather than computerized tools, can take more time and can add costs of finding the right engineers with the proper skillsets. Digital annotation performed with computerized tools provides a better level of accuracy and consistency.

Ensuring Quality Data Outputs

ML business models rely heavily on high-quality data outputs, but those ML models can only build precise projections if the data quality is indeed trusted. Subjective data can be hard for digital labelers to interpret depending on where they are geographically located, for example.

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What Is Data Annotation: The Basics

Every machine learning and deep learning algorithm relies heavily on data, and the engine propels these very advanced algorithms to perform at the cutting edge. However, to construct reliable AI models, it is necessary to supply the algorithms with well-structured and well-labeled data.

Data annotation is a valuable tool in this context.

For machine learning algorithms to learn how to complete specific tasks, you must annotate the data they use for training.

In other words, annotating data is simple but not always easy. Fortunately, we're about to give you a hand by explaining all you need to know, including several tips and tricks that will shave significant amounts of time off your workload.

These are the topics we will go through in this blog article :

The definition of data annotation.
Types of data annotations
Annotating data automatically vs. manually
The benefits of using AI for data annotation

What is Data Annotation?

A lot of training data is needed to make an AI or Machine Learning model that acts like a human. A model must be trained to comprehend specific information to make decisions and take action.

Data annotating is the process of classifying and labeling data for AI applications. Training data must be correctly categorized and annotated for a specific use case. Companies can build and improve AI systems with high-quality data annotated by humans.

Supervised ML models are trained and learn using properly labeled data to address challenges like:

Classification is the process of sorting test data into subcategories. Classification problems include, but are not limited to this, determining the presence or absence of a disease in a patient and placing their health records into the appropriate "disease" or "no disease" categories.

Using a statistical method called regression, one can determine if there is a connection between two data sets. For instance, a regression problem could be used to estimate the impact of advertising spending on product sales.

Voice recognition, product suggestions, appropriate search engine results, voice recognition, computer vision, chatbots, and other improvements to the consumer experience are the final result. Text, sound, still images, and moving visuals are the most common forms of data.

Different Types of Data Annotation

Let's dive a bit deeper into different types of data annotation.

Image Annotation

Annotating images is essential for many uses, such as those involving computer vision, robotic vision, facial recognition, and other solutions that use machine learning to decipher images.

When building training datasets for learning systems, image annotation is often used. For use in training, images need to have information added to them, such as IDs, captions, or keywords.

There are many applications that necessitate large amounts of annotated photos, such as computer vision systems used by self-driving vehicles, machines that select and sort produce, and healthcare applications that automatically diagnose medical issues. Annotating images is an excellent way to train these algorithms, leading to greater precision and accuracy.

Differentiating between object classes requires drawing bounding boxes for detection and segmentation masks for semantic and instance segmentation.

The number of labels on an image can increase depending on the usage scenario. In its most basic form, image annotation can be broken down into two categories:

Image Classification

Machines that have been trained on annotated images can quickly and accurately identify the contents of an image by comparing it to a set of labels.

Object Recognition & Object Detection

It is an improved version of image categorization and accurately describes the quantities and relative placements of things shown in the picture. Unlike image classification, which classifies a complete picture, object recognition names individual objects. Image classification, for instance, entails assigning a "day" or "night" label to an image. When an image is processed with object recognition, multiple objects, such as a bike, tree, or table, are categorized separately.

Suppose you need AI-powered object detection and object recognition solution. In that case, Cameralyze is a no-code visual intelligence platform capable of recognizing and tagging multiple objects in any given picture, video, or live stream, as well as identifying your visual data and tracking moving items in a live video feed in real-time. Start to use it for free!

If you are interested in object detection and object recognition, we recommend the following blog articles:

Object Detection In 2022: The Definitive Guide Part 1
What Is Object Recognition And Where To Use?

Text Annotation

According to the 2020 State of AI and Machine Learning report, the text is the most widely used data type, with 70% of businesses depending on it.

Data annotation is also essential for Natural Language Processing (NLP) tasks. If you are interested, we recommend you read our article , which explains the basics of NLP, its techniques and methods, NLP applications, and use cases in 2022.

Text annotation means adding relevant information about the language data by adding labels or metadata. Several annotations, such as emotion, intent, and even queries, can be applied to texts.

Sentiment Annotation

Sentiment analysis relies on high-quality training data to accurately evaluate people's feelings, thoughts, and views. Human annotators are frequently used to gather this information since they can evaluate mood and filter content across all web platforms, including social media and e-commerce sites. Then they can tag and report on keywords that are profane, sensitive, or neologism.

Intent Annotation

Due to the rise in popularity of HMIs, it is essential that computers can comprehend not only human speech but also the underlying intentions of their human operators. It is possible to classify requests, commands, bookings, suggestions, and confirmations into their respective categories using multi-intent data collection and classification.

Semantic Annotation

Semantic annotation, in this way, can improve a machine learning system in its attempt to understand how to recognize and adequately categorize abnormalities.

Two benefits of semantic annotation are improved product listings and easier consumer discovery, and this increases the likelihood that site visitors will make a purchase. Semantic annotation services help in the training of an algorithm to recognize the many components inside product titles and search queries, thus increasing search relevancy.

Named Entity Annotation

Training data for Named Entity Recognition (NER) systems must be extensive and human-annotated. The main objective of named entity recognition (NER) is to identify and categorize specific words or phrases inside a text.

You can use it to look up things like people's names, places, etc., depending on the meaning of a set of words. Information extraction, classification, and categorization are all made more accessible by NER.

Audio Annotation

Audio annotation entails not only the time-stamping and transcription of speech data but also the identification of linguistic features such as language, dialect, and speaker demographics.

Tagging aggressive speech signs and non-speech sounds like glass breaking for use in security and emergency hotline technology applications is just one example of the specialized approaches needed for the wide variety of possible use cases.

Video Annotation

Video annotation is similar to annotating images in that it entails labeling segments of the video in order to detect and identify specific objects frame by frame. A crucial component of practical machine learning is data that humans have manually annotated. Computers can't compare to humans when it comes to handling nuance, nuanced meaning, and ambiguity.

By way of example, several individuals' opinions are required to reach an agreement on whether or not a search engine result is relevant. Frame-by-frame video annotation employs the same methods as image annotation, such as bounding boxes or semantic segmentation. The approach is crucial for localization and object tracking, two common computer vision tasks.

Humans are needed to manually identify and annotate data for use in training a computer vision or pattern recognition system, such as highlighting every pixel in a picture that contains trees or traffic signs. Machines can be taught to make these connections during testing and production with the help of this structured data.

Annotating Data Automatically vs. Manually

Human annotators have a tendency to fail and make more mistakes as the day progresses due to fatigue and lack of focus . Data annotation is a time-consuming and resource-intensive procedure that requires the full attention of knowledgeable workers.

This is why cutting-edge ML groups are relying on machine-generated labels for their data.

Here's how it works: after an annotation task has been defined, a trained machine learning model can be applied to an otherwise unlabeled data set. Labels for the new, unseen data set can then be predicted by the model. In the event that the model makes an incorrect labeling decision, however, humans can step in, examine, and rectify the mislabeled data. Once the errors have been fixed and the data has been verified, the labeling model can be trained again.

While automated data labeling can save significant time and resources, its accuracy is not always guaranteed . However, human annotation is typically more accurate, although being significantly more expensive.

What Are The Benefits Of Using AI For Annotation?

Machine learning relied mainly on human annotation for a long time. Businesses often outsource this process to third-party companies or employ in-house developed text annotation tools. To help their clients train their systems to mimic human thought, these firms would generate the requisite datasets.

In image annotation projects, human-annotated data can be generated manually and can include a wide variety of labels, such as those describing the image's color, texture, and overall appearance. Quantities of data are supplied to teach machine learning models how to reason like humans.

Human accuracy ensures high-quality results when manually labeling data; however, this method is labor-intensive, expensive, and time-consuming. There is a need for assistance with picture and video annotation, and this is where AI-assisted video annotation solutions can help.

Check our articles to learn more about Data and Computer Vision Technologies:

Image Classification Dataset
What Is Image Annotation?
Test Data and Data Training
Beginners' Guide to NLP
Machine Vision vs. Computer Vision

Plugger AI vs. Civitai: Simplifying Access to AI Models for All Use Cases

Plugger AI vs. Replicate: Simplifying AI Model Access and Testing

Plugger AI vs. Huggingface: Simplifying AI Model Access and Scalability

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What is annotation in machine learning?

Data annotation is an essential part of Machine Learning and Artificial Intelligence development because they direct and educate the computer to think similar to human beings. The expansion of smart devices is a huge representative model of what successfully annotated and processed data can initiate and produce.

Different types of data annotations are available on the market for dominant types of the data, such as:

Image annotations – Technology used for multiple image processing

Audio and text annotations – Commonly used for translations, summarization of the long text, and similar tasks.

Video annotation – Used for recognizing and tracking the objects in a video through every sequence of it.

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Why Data Annotation is Important for Machine Learning and AI

Last Updated on: July 31st, 2023

Data annotation, the workhorse behind AI and ML algorithms, creates a highly accurate ground truth that directly impacts algorithmic performance. Annotated data is critical for accurate understanding and detection of input data by AI and ML models.

Smart equipment and smart life have become an integral part of our daily life. Right from self-driving cars, smart and nudge replies to emails, estimating time of arrival through GPS app to next song in the streaming que – everything is powered by Artificial Intelligence (AI) and Machine Learning (ML).

If market gurus are to be believed, AI has the potential to deliver additional global economic activity of around $13 trillion by 2030.

To do all these, AI and ML models are to be fed with data; a lot of data. Data is the backbone of AI and ML algorithms. Computers can’t process visual information the way human brains do. A computer needs to be told what it’s interpreting and provided context in order to make decisions. Data annotation makes those connections.

Data labelling ensures that AI or ML projects are scalable. It is the human led task of identifying and labeling specific data, images, videos to make it easier for machines to identify and classify information like humans do – and to make predictions. If data labelling is not done, ML algorithms cannot compute the essential attributes with ease.

Data Annotation Challenges for AI & ML companies

Applications of artificial and machine learning platforms are becoming commonplace. However, a thick layer of hype and fuzzy jargons shadow the challenges AI and ML companies face in terms of feeding accurately annotated training datasets.

Higher quality training datasets: The quality of annotated data decides the fate of AI and ML project. To train a model to recognize patterns and relationships between variables; AI and ML companies have to feed accurately annotated datasets. Analytics companies cannot afford misaligned bounding boxes and confusion in the classifiers. These mistakes can prove to be disastrous. Not to forget, the ability of AI and ML models to deliver personalization and efficiency depends on precisely curated and labeled data.

AI and ML models are data hungry: ML projects typically require thousands or even millions of labeled training items to be successful. While the goals of machine learning projects can vary widely in complexity, they all share a common requirement: a large volume of high-quality data to train the model.

According to McKinsey Global Institute 75% of AI and ML projects demand learning datasets to be refreshed once every month, and 24% of AI and ML models require a daily refresh of annotated datasets.

Resources for data annotation projects: AI and ML companies don’t have adequate manpower to handle large-scale and complex annotation projects. Pulling in engineers or other team members off their core tasks to perform data labeling tasks proves expensive. In absence of progressive flow and accurately annotated data; AI and ML companies cannot develop models capable to rightly interpret important attributes or make accurate predictions.

No wonder the global data annotation market is about to skyrocket from US $695.5 million in 2019 to US $6,450.0 million by 2027.

Elevate Your AI with Data Annotation

Key advantages of employing data annotation for AI and ML models

Text annotation, image annotation or video annotation facilitates a deeper understanding of the meanings of the text or objects, thereby allowing algorithms to perform better.

A computer vision model operates with different levels of accuracy over an image in which several objects are labeled accurately as against an image in which objects haven’t been labeled or poorly labeled. So, better the annotation, higher is the precision of the model.

Machine learning project TAT reduced by 54% for a data analysis services provider. Data annotation company studied the footage of a traffic signal to identify and label vehicles by their category, model name, color, and the direction it is traveling into. It is only through this accurately annotated database that an AI & ML model understands what it needs to do with the data being fed to it. The model, thus, fast learns to apply the valid treatment(s) to the labeled data and generates results that make sense.

Annotation of any form of data streamlines preprocessing, which is an important step in the machine learning dataset building process. In a classic case, 40,000+ images were labeled and fed into machine learning models, using a blend of manual and automated workflows. It helped a Swiss data analysis solutions company resolve the issue of food wastage for leading hotels and restaurants. Regularizing data annotation services , as a result, leads to the creation of massive labeled datasets over which AI & ML models can functionally operate.

Well-annotated data offers altogether a seamless experience to the users of AI systems. An efficacious intelligent product addresses the problems and doubts of users by providing relevant assistance. The capability to act with relevance is developed through annotation.

The axiom that increasing data volume increases AI model’s accuracy and precision hold true only when there is a perfect data annotation process to supplement the models with labeled data. So, as the data volumes soar, the reliability of AI engines, too, increases.

Annotated data can easily accommodate sentiments, intents, and actions from multiple requests. It also facilitates the creation of accurate training datasets, thereby imparting AI engineers and data scientists the ability to scale the mathematical models for diverse datasets of any volume.

4 major types of data annotation and labeling

Data annotation for machine learning is a broad practice, but every type of data has a labeling process associated with it. Some of the commonly used annotation types include:

1. Text annotation

Text annotation is common in search engines, where words are tagged to enable search engine algorithms to load the pages containing the search keywords. Tagging helps in matching the keywords with URLs in the databases and allows search engines to fast produce desired results for searchers. Here is a practical insight:

2. Video annotations

Amongst many use cases, an autonomous vehicle is the one in which video annotation proves vital. Technically, it divides a video into frames, and each of them categorically identifies the object(s) of interest. As a result, video annotations offer tremendous visibility into the road traffic pattern, in-cabin driver actions, accident-prone spots, etc., and thereby significantly boost on-road safety.

A California based data analytics company hired a data annotation company to label pre-recorded and live video streams to power their machine learning models. It helped them successfully deploy a dashboard of directional traffic volumes that provided live data and alerts based on historical volumes for city traffic management.

3. Image annotations

200 million accurately annotated images empowered the world’s leading technology company to enhance search engine experiences for its clients in the U.S. and international markets. A highly accurate training dataset enabled users to find images on time; that is free of spam and relevant to the search query intent.

Applied using a range of techniques such as bounding boxes, polygons, tracking to masking, image annotation involves labeling objects of interest in an image. Elements are pre-determined by machine learning experts to supplement the computer vision models with the requisite knowledge. As decided by the context, a combination of techniques can be used to label objects in an image

4. NLP annotation for speech recognition

Transposing complex grammar rules into 14 languages, pronunciation checks, validated transcription was used to train a virtual assistant to better understand and respond to queries of 150 million+ active users per month.

In NLP annotation, the language is the focus, and tagging is used to unravel the deepest insights from the nature of the language. The NLP annotation process comprising of Parts of Speech (POS) Tagging, Phonetic Annotation, Semantic Annotation, Key phrase Tagging, Discourse Annotation, etc. capture properties of linguistic structure. It empowers ML systems to interpret meanings and understand contexts as humans do.

Future of data annotation with technological advancements

A study by Grand View Research has noted that the global data annotation market will reach 1.6 US billion dollars by 2025 and Research & Market’s report projects the global data annotation market at 6.45 US billion dollars by 2027.

Altogether, a massive positive forecast for the data annotation market can be attributed to following future technological trends in space.

Smart labeling tools will dominate the future AI and ML landscape. Backed with predictive analytics, data labelling capabilities will be fully automatic, detecting labels without any manual intervention.
Reporting frameworks will be an integral component of data annotation processes. Operational intelligence will offer an understanding of how annotation complexities are being handled. The reporting capabilities will be an essential add-on to monitor the annotation throughput and productivity.
With a need to sustain accuracy levels, automation plus strong quality control is essential to justifiably annotate high-volume data. This will be a key character of next-gen data annotation, where not sheer labeling but gauged and quality labeling will be the true focus.
Any type of annotation or labelling services are heavily relied upon to improve the performance of machine learning projects. They use a combination of skilled human annotators, annotation tools, and verified workflows to produce, structure, and label high volumes of training and testing data.

At HabileData, we provide leading-edge data annotation services. Our team provides the most precise and complete labeled datasets for your AI projects. Our data annotation services adapt to AI and machine learning projects. We stay current with industry capabilities to provide accurate, high-quality annotations that support your AI efforts. Our precise data labeling helps your AI models perform well and make a difference.

The right application of data annotation is possible only when you leverage the fine combination of human intelligence and smart tools to create high-quality training data sets for machine learning. MIT Technology Review Report rightly says that rightly annotated data has been the biggest challenge to employing AI. Enterprises should build strong data annotation capabilities to support AI & ML model building and prevent it from failing miserably. We, humans, are a notch above computers since we can better deal with ambiguity, decipher the intent, and several other factors that impact text, video or image annotation.

Accurately annotated data determines whether you create a high-performing AI/ML model as a solution to a complex business challenge, or you wasted time and resources on a failed experiment. And when lacking time and resources to build such capabilities, consulting data annotation companies is a smart move. Apart from time and dollar optimization, data annotation specialists allow you to rapidly scale your AI capabilities and conceptualize machine learning solutions to match the market requirements and meet customer expectations.

Experience the Power of Data Annotation

About Authors

Snehal Joshi heads the business process management vertical at HabileData , the company offering quality data processing services to companies worldwide. He has successfully built, deployed and managed more than 40 data processing management, research and analysis and image intelligence solutions in the last 20 years. Snehal leverages innovation, smart tooling and digitalization across functions and domains to empower organizations to unlock the potential of their business data.

Chirag Shivalker heads the digital content for HabileData , a global data management solutions outsourcing company, rated as one of the top BPO companies in India. Chirag's focus has been on enterprise wide data digitization, data governance, data quality, and BI capabilities.

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What is Data Annotation and How Applied in Machine Learning?
Why Data Annotation is Important for Machine Learning?
Data Annotation for Machine Learning: A to Z Guide
Different Types Of Text Annotation For Machine Learning
What Is Data Annotation for Machine Learning
Data Annotation: Types and Use Cases for Machine Learning

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Preparing and Exploring Data

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Machine Learning Essentials: What is Data Annotation?

Teaching Through Data

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Types of Data Annotation

The Written Word: Text Annotation

More than Meets the Eye: Image Annotation

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Data Annotation: Types and Use Cases for Machine Learning

What is Data Annotation?

Types of Annotation in Machine Learning

How is text annotated?

Use Cases of Data Annotation

Creation of facial recognition software

Creation of data for self-driving cars

Advances in the medical field

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Data Annotation Tools for Machine Learning (Evolving Guide)

Read the full guide below, or download a PDF version of the guide you can reference later.

Table of Contents

What’s data annotation?

What’s a data annotation tool?

6 Important Data Annotation Tool Features

2) Annotation methods

3) Data quality control

4) Workforce management

5) Security

6) Integrated labeling services

When to build your own data annotation tool

When to buy a data annotation tool

The open source option for data annotation tools

Growth stage as an indicator for buy vs. build

1) What is your use case?

2) How will you manage quality control requirements?

3) Who will be using the tool?

4) Do you need a vendor or a partner?

Commercial Data Annotation Tools

Open Source Data Annotation Tools

Why change data annotation tools?

How do I change data annotation tools?

Strategic Approach

Key Features

Machine Learning

The People + Process Component

Contact Sales

What is a data annotator?

What is data annotation?

What are data annotation tools?

What is an image annotation tool?

What’s the best image annotation tool?

What is a video annotation tool?

What’s an online annotation tool?

What are text annotation tools?

Is there a list of video annotation tools?

What’s the best text annotation tool?

What is Data Annotation in Machine Learning?

What is Data Annotation?

Importance of data annotation in machine learning

Types of data annotation techniques

Common challenges in data annotation

How to become a data annotator ?

Best practices for data annotation

Case studies showcasing the impact of accurate data annotation

Outsourcing data annotation services

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