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Data labeling for algorithmic model training (AI, ML, CV, DL) is the process of labeling and annotating raw data, such as images and videos, to train a model. In this Encord ultimate guide, we cover types of data labeling, how to implement it, use cases, and best practices.
Accuracy and the effectiveness of your algorithmic models, such as artificial intelligence (AI), computer vision (CV), or machine learning (ML) models, are directly impacted by the quality and quantity of data you train them on.
If you put high-quality data in, you will get more accurate results.
High-quality labeled data is your model's fuel to learn and generate the real-world results you need.
Data labeling is widely used across dozens of sectors and industries, such as medical and healthcare, manufacturing, and satellite images for environmental or defense use cases.
Data labeling and annotation are essential for creating successful outcomes from projects involving algorithmic models in every use case. Images and videos are labeled according to a project's outcome, goals, objectives, and what the training model needs to learn before it’s production-ready.
In this ultimate guide, we cover:
Let’s dive in . . .
Computer vision, AI, and ML models can’t do much with raw data.
Once you label data, such as images, videos, text, and audio, an algorithmic model starts to understand what it’s seeing; it can train and learn from labeled data.
Data labeling is the process ⏤ a largely manual or AI-supported task ⏤ of adding labels, tags, and descriptions to raw data, such as images and videos. These labels describe the objects and content of the datasets you use for a project.
Human annotators and annotation teams need to show AI and CV models what’s in the images, videos, or other datasets. Data labeling is how you describe the contents of a dataset so that an algorithmic model can train and then go into production.
As every ML engineer, data scientist, data ops professional, and annotator knows, there are several ways to label data: supervised, semi-supervised, automated, in-house, and outsourcing. We cover all of these and more in this article.
The importance of data labeling in machine learning and computer vision can’t be understated. Data labeling and annotation is a mission-critical part of the process. A step that can’t be missed.
Without this process, no AI-based model understands what you want it to learn.
We can say the same about poor-quality data. Or not having enough data. If you’re training a model on poor-quality or low-volume data, then you might not get the results you’re hoping for.
Here’s a list of some of the best open-source datasets for machine learning: A great way to test a new ML model.
(Source: KTH Multiview Football Dataset I & II)
Training data is the labeled and annotated data that gets fed into a machine learning or computer vision model that helps it learn about the dataset.
Labeled and annotated training data is known as the ground truth. With this ground truth, you can use that as a benchmark for attaining accurate results during the training data.
Labeling objects in datasets, ⏤ we cover the various ways to go about that next ⏤ enables an ML or AI model to identify numerous classes of objects when it encounters ones without labels. This is crucial because unless you’ve only got a small dataset, it’s almost impossible for annotators to label every single object manually.
When enough of the data’s been annotated or labeled, and it’s high-quality, you can use it to train machine learning algorithms to produce the outcomes and results a project needs.
Let’s say we’ve got a dataset containing images of cats and dogs. The project goals are to have a computer vision model accurately identify and classify different breeds and sizes of these animals, starting with whether an image is of a cat or dog.
Every training dataset and how it’s labeled depends on the context and goals of the machine learning task you’re focusing on and the intended AI model’s outcomes.
Labeling the data is how we achieve this. Depending on the project goals, these labels can be as accurate and detailed as you need, such as: the name of the object, color, size, weight, etc.
There are no labels, tags, descriptions, or annotations with unlabeled data. In most cases, it is difficult for an algorithmic model to learn anything from a training dataset provided.
Data labeling is the most effective way to achieve the most accurate outcomes from a computer vision, ML, or AI project.
There are numerous approaches and ways of labeling data. Machine learning and data ops team leaders need to pick the right solution for their project outcomes.
The options include supervised or unsupervised, semi-supervised, Human-in-the-Loop (HITL), and programmatic data labeling. There are operational considerations, such as whether data labeling is in-house, outsourced, automated, or even crowd-sourced. We cover all of these considerations next.
Every computer vision project has options for how data labeling is done. Here are the four most popular approaches:
In-house data labeling is expensive, especially if you need to recruit an internal team of data scientists and engineers in countries and regions such as North America and Europe.
However, for sectors such as healthcare, where quality and accuracy are crucial, it gives ML and data ops leaders complete control of the data labeling process.
Working in-house with experts and specialists ⏤ people with in-depth subject matter knowledge and training ⏤ ensures high-quality outcomes for data labeling and annotation projects. It’s especially useful to have an in-house team when there are large volumes of data to clean, process, annotate, and label.
Providing your organization has the budget, this is an expensive option, as there are more cost-effective options. However, quality and expertise considerations need to be factored in too.
Outsourcing data labeling is almost always more cost-effective than recruiting and retaining an in-house team of data annotators.
Data annotation tasks and projects can be outsourced to one or more freelancers or a data labeling service provider. In that scenario, you contract the work to a company, usually in developing regions, such as Central & Eastern Europe (CEE), South East Asia, India, or Latin America.
Providing you’ve checked references and reviews, and seen examples of their work, then you can put the project into motion. For outsourced data annotation projects, it’s crucial that you’ve got the right tools and processes for managing quality control, data security, compliance, and workflow scalability.
Outsourcing is something we can help companies with: Here’s our guide for onboarding 100s of annotators to produce high-quality data labels.
If you’re recruiting, training, and onboarding 100s of annotators (either in-house or outsourced), it’s worth checking out Encord’s Annotator Training Module.
One of the easiest and fastest ways to work through data labeling tasks is to use automated annotation and data labeling tools and platforms, such as Encord.
Automated, AI-powered, and assisted data labeling can dramatically accelerate the process without sacrificing quality.
Annotation tools usually come with a range of features to support active learning, supervised, semi-supervised, programmatic, and Human-in-the-Loop (HITL) data labeling
Automated labeling doesn’t mean you don’t need human annotators, data scientists, data ops, and ML engineers. But it does mean that human annotator workloads can be reduced, saving you time and money and ensuring ML models can go into production faster.
Crowdsourcing is another option and one that more organizations are turning to, if only for cost-effectiveness. You still need a budget, but it’s normally cheaper than the in-house or outsourced approach.
Data operations leaders can set up data annotation and labeling projects on crowdsourced platforms, such as Clickworker, Amazon Mechanical Turk (MTurk), and even sites such as Upwork, and dozens of others.
However, quality, accuracy, and even reliability could be hit-and-miss. It’s not the same as working with an outsourced provider, where they’ve got staff and are contracted to deliver what you need.
It takes time and effort to source, train, and retain a reliable group of people for data annotation and labeling tasks, and you will need the right tools to monitor quality very closely.
AI-supported data labeling is a widely-used solution for data annotation and labeling tasks. It’s more cost and time-effective and helps organizations get their projects production-ready quicker.
There are numerous software platforms on the market, including open-source, low-code and no-code, and customizable active learning SaaS (Software as a Service) annotation solutions, toolkits, and dashboards, such as Encord.
Before selecting the right tool based on features, functionality, and use case, you need to know what type of AI-supported data labeling you’re going to use for your computer vision project.
The most common options are:
Let’s compare them now . . .
Supervised learning is the most common type of AI-assisted labeling and annotation. Data annotation tasks such as image classification and segmentation fall into this category.
Supervised learning involves annotators applying labels to objects in image and video-based datasets, supported by AI-assisted tools to automate and accelerate the process.
After that, the training data is fed into the machine learning or computer vision model, and its accuracy is tested, initiating a quality assurance iterative feedback loop. Once the AI model’s performing as expected against labeled and unlabeled data it can be put into production.
Unsupervised learning is where unannotated data is fed into an algorithmic model without labels. Unsupervised algorithms use things such as autoencoders to train inputs and outputs.
Unsupervised learning is mainly used for analysis, with a limited number of use cases, and the most common algorithms for this include K-means, clustering, and hierarchical clustering.
As the name implies, semi-supervised learning is a hybrid of the two AI-based approaches to data labeling. A mix of labeled and unlabeled data is used. It reduces the cost of data annotation.
However, to achieve success with this approach the parameters and assumptions applied to the training data need to be as precise as possible. It’s often used for things such as protein sequence image classification in the healthcare sector.
Human-in-the-loop (HITL) is an iterative feedback for data labeling. Human annotators, data scientists, and quality assurance engineers provide feedback on the data labels and annotations, constantly updating how an algorithmic model understands, interprets, and analyzes the data.
Programmatic data labeling is an advanced approach that leverages computer algorithms to automate the process of generating labeled data for computer vision and machine learning applications. By using predefined rules or pattern recognition techniques, programmatic data labeling allows for increased efficiency and scalability in data annotation compared to manual processes.
In this context, programmers create scripts or use machine learning techniques to identify and label objects within images and videos automatically. Examples of programmatic data labeling methods include rule-based systems, template matching, and natural language processing. This approach can significantly reduce the time and resources required for data annotation while maintaining quality and accuracy.
Programmatic data labeling can be employed in combination with other learning techniques, such as supervised, unsupervised, or semi-supervised learning, to enhance the overall effectiveness of the machine-learning pipeline. It is particularly useful in domains with well-defined patterns or recurring structures, such as industrial inspection, satellite imagery analysis, and optical character recognition.
Now let’s look at the most common use cases for data labeling, how it works, and best practices.
Computer vision is an exciting and innovative field of artificial intelligence. Computer vision models and algorithms are used for meaningful, often commercial outcomes from image and video-based datasets.
Computer vision models are used across dozens of sectors, including automotive insurance, medical and healthcare, satellite imagery, manufacturing, and retail.
Data labeling is an essential part of any computer vision project. It can be time-consuming. Having the best data operations teams and annotators will ensure high-quality labels are applied to the datasets a project will use to train a model.
There are numerous ways to annotate and label image and video data, such as:
With AI-assisted labeling tools, you can do all of these and numerous other ways to label and annotate data. Producing faster, more accurate, and cost-effective results than a team of annotators can manage with the right solution to accelerate labeling tasks.
Data labeling is a process. It involves numerous stages and can be quite time-consuming. Every project might have a slightly different approach, depending on the goals, sector, use cases, datasets, and models being used. However, the chronological process for most data labeling projects is quite similar.
Here’s how data labeling works, also known as an organization's data pipeline:
With the right tools and dashboards, you can make this whole process easier, faster, and more cost-effective while producing higher-quality labels, annotations, and data to train an ML model on.
Here are five tried-and-tested best practices for data labeling.
Before data can be labeled, it needs to be sourced and cleaned according to the project requirements and any commercial goals. There are thousands of ways to source data, from buying it to scraping public data to using open-source datasets.
Once you’ve got the data you need, operations specialists and data scientists need to clean it. Removing duplicates, blurred images or video frames, any sensitive personal data, and anything else that could impact your CV model performance.
As part of this process, you need to identify a subset of the dataset to train your ML model on. As we cover in this article, factors such as size, representativeness, quality, and computational resources all need to be considered.
With the right AI-based data labeling platform, your project will go much smoother.
Here’s what to look for:
When you’ve got an AI-assisted tool with all of these features and functionality, your annotation team can deliver better results, and you can get your CV model into production faster.
Once you’ve picked an AI-assisted labeling tool and the way you’re going to have data labeled (in-house, crowdsourced, or outsourced), you need to design and implement an annotation and quality control workflow before the project commences.
Designing this mid-project is a headache no ML leader needs.
Make sure you’ve got an operational plan for the entire end-to-end annotation process. Especially if you’re working with an outsourced annotation provider. You need to be sure they will align with your workflow and processes.
Ensure this workflow fits with how you’re going to introduce training data to the ML model. Integrate quality control and iterative feedback loops within this operational plan.
With all of that ready, annotation and data labeling work can begin.
High-quality training data is crucial, especially for data-centric model training. The most common quality control issues in computer vision include inaccurate, missing, mislabeled images, or unbalanced data, resulting in bias or insufficient data for edge cases.
Poorly labeled or inaccurate labels will cause algorithmic models to struggle to identify objects correctly. Even in best practice and benchmark datasets, 3.4% of labels are incorrect and inaccurate, according to MIT research.
To improve the quality and accuracy of labeled data, you should:
For more information, here are 5 ways to improve the quality of your labeled data.
Once you’ve got enough labeled training data, you can start feeding it into your computer vision model. high-quality training data is so important for the success of a CV project, but you rarely get that to begin with. Don’t expect the best results at this stage.
A training dataset might produce an accuracy score of 70%. Naturally, data ops and ML leaders need better results than that, ideally 90%+ or even 99% for the production model.
Ensure an iterative feedback loop, whether automated, semi-automated, or using the HITL format, is established to constantly and consistently improve the quality of the labels and annotations in the datasets.
Every improvement that’s made within the datasets should result in a corresponding improvement in the accuracy and outputs of your computer vision model. Model performance and quality can always be improved. Inaccuracies are corrected, and datasets can also be tested against benchmarks to increase accuracy further.
Encord improves the efficiency of labeling data and managing a team of annotators. Encord Active is an open-source active learning framework for computer vision: a test suite for your labels, data, and models.
With Encord, you can get to production AI faster with AI-assisted labeling, model training, and diagnostic tools to fix dataset errors and biases.
Encord is a collaborative active learning platform with an extensive suite of tools, making it easier for ML teams to work with data ops to receive high-quality training and production-ready datasets for computer vision models.
Algorithmic models, such as AI, ML, and CV, need labeled and annotated data to train, learn, and ultimately go into production.
Sourcing, cleaning, and producing high-quality labels and annotations is an essential part of the process. ML and data ops teams can use AI-assisted tools and platforms to accelerate this manual, time-consuming process.
Data labeling involves numerous steps, such as deciding which approach to take (in-house, crowdsourced, or outsourced).
Alongside those options, once you’ve got annotators, data scientists, and quality assurance engineers, you need to pick whether a learning and training format: supervised, unsupervised, semi-supervised, human-in-the-loop, or programmatic data labeling.
There are several steps every data annotation project goes through, from sourcing and cleaning to creating the labels, and finally, iterative feedback and QA process to ensure the labels are high-quality, both for training a model and for the production phase of the project.
And there we go, the Encord ultimate guide to data labeling!
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