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Automated data annotation is a way to harness the power of AI-assisted tools and software to accelerate and improve the quality of creating and applying labels to images and videos for computer vision models.
Automated data annotations and labels have a massive impact on the accuracy, outputs, and results that algorithmic models generate.
Artificial intelligence (AI), computer vision (CV), and machine learning (ML) models require high-quality and large quantities of annotated data, and the most cost and time-effective way of delivering that is through automation.
Automated data annotation and labeling, normally using AI-based tools and software, makes a project run much smoother and faster. Compared to manual data labeling, automation can take manual, human-produced labels and apply them across vast datasets.
In this ultimate guide, we cover everything from the different types of automated data labeling, use cases, best practices, and how to implement automated data annotation more effectively with tools such as Encord.
Let’s dive in...
Data annotation, also known as data labeling ⏤ as these terms are used interchangeably, ⏤ is the task of labeling objects for machine learning algorithms in datasets, such as images or videos.
As we focus on automation, AI-supported data labeling, and annotation for computer vision (CV) models, we will cover image and video-based use cases in this article.
However, you can use automated data annotation and labeling for any ML project, such as audio and text files for natural language processing (NLP), conversational AI, voice recognition, and transcription.
Data annotation maps the objects in images or videos against what you want to show a CV model. In other words, what you’re training it to understand. Annotations and labels are how you describe the objects in a dataset, including contextual information.
Every label and annotation applied to a dataset should be aligned with a project's outcome, goals, and objectives. ML and CV models are widely used in dozens of sectors, with hundreds of use cases, including medical and healthcare, manufacturing, and satellite images for environmental and defense purposes.
Labels and annotations are an integral part of the data that an algorithmic model learns from. Quality and accuracy are crucial. If you put poor-quality data in, you’ll get inaccurate results.
There are several ways to implement automated data annotation, including supervised, semi-supervised, in-house, and outsourcing. We cover those in more detail in this article: What is Data Labeling: The Full Guide.
Now, let’s dive into how annotation, ML, and data ops teams can automate data annotation for computer vision projects.
Manual tasks, including data cleaning, annotation, and labeling, are the most time-consuming part of any computer vision project. According to Cognilytica, preparation absorbs 80% of the time allocated for most CV projects, with annotation and labeling consuming 25% of that time.
Automating data annotation tasks with AI-based tools and software makes a massive difference in the time it takes to get a model production-ready.
AI-supported data labeling is quicker, more efficient, cost-effective, and reduces manual human errors. However, picking the right AI-based tools is essential.
As ML engineers and data ops leaders know, there are dozens of options available, such as open-source, low-code and no-code, and active learning annotation solutions, toolkits, and dashboards, including Encord.
There are also a number of ways you can implement automated data annotation to create the training data you need, such as:
We compare those in this article in more detail.
Now let’s consider one of the most important questions many ML and data ops leaders need to review before they start automating data annotation: “Should we build our own tool or buy?”
Building an in-house tool takes time ⏤ anywhere from 6 to 18 months ⏤ and usually costs anywhere in the 6 to the 7-figure range. Even if you outsource the development work, it’s a resource-hungry project.
Plus, you’ve got to factor in things like, “What if we need new features/updates?” and maintenance, of course. The number of features and tools you’ll need correlates to the volume of data a tool will process, the number of annotators, and how many projects an AI-based tool will handle in the months and years ahead.
Buying an out-of-the-box solution, on the other hand, means you could be up and running in hours or days rather than 6 to 18 months. In almost every case, it’s simply more time and cost-effective. Plus, you can select a tool based on your use case and data annotation and labeling needs rather than any limitations of in-house engineering resources.
For more information on this, check out: Buy vs build for computer vision data annotation - what's better?
Some of the most common automated data annotation tasks in computer vision include:
Let’s explore all three in more detail...
Image annotation is an integral part of any image-based computer vision model. Especially when you’re taking the data-centric AI approach or using an active learning pipeline to accelerate a model’s iterative learnings.
Although not as complex as video annotation, applying labels to images is more complex than many people realize.
Image annotation is the manual or AI-assisted process of applying annotations and labels to images in a dataset. With the right tools, you can accelerate this process, improving a project's workflow and quality control.
Video annotation is more complex and nuanced than image annotation and usually needs specific tools to handle native video file formats.
Videos include more layers of data, and with the right video annotation tools, you ensure labels are correctly applied from one frame to the next. In some cases, an object might be partially obscured or contains occlusions, and an AI-based tool is needed to apply the right labels to those frames.
For more information, check out our guide on the 5 features you need in a video annotation tool.
Medical image file formats, such as DICOM and NIfTI, are even more complex and nuanced than images, or even videos, in many ways.
The most common use cases in healthcare for automated computer vision medical image and video annotation include pathology, cancer detection, ultrasound, microscopy, and numerous others.
The accuracy of an AI-based model depends on the quality of the annotations and labels applied to a dataset. To achieve this, you need human annotators with the right skills and tools that are equipped to handle dozens of medical image file formats with ease.
In most cases, especially at the pre-labeling and quality control stage, you need specialist medical knowledge to ensure the right labels are being created and applied correctly. High levels of precision are essential, with most projects having to pass various FDA guidelines.
As for data security, and data compliance, any tool you use needs to adhere to security best practices such as SOC 2 and HIPAA (the Health Insurance Portability and Accountability Act). Project managers need granular access to every stage of the data annotation and labeling process to ensure that annotators do their job well.
With the right tool, one designed with and alongside medical professionals and healthcare data ops teams, all of this is easier to implement and guarantee.
Find out more with our best practice guide for annotating DICOM and NIfTI Files.
We’ve recently made updates to our DICOM annotation tool: Check them out here.
Automated data annotation and labeling for computer vision and other algorithmic-based models include the following:
Manually annotating and labeling large datasets takes time. Every hour of that work costs money. In-house annotation teams are more expensive.
But outsourcing isn’t cheap either, and then you’ve got to consider issues such as data security, data handling, accuracy, expertise, and workflow processes. All of this has to be factored into the budget for the annotation process.
With automated, AI-supported data annotation, a human annotation team can manually label a percentage of the data and then have an AI tool do the rest.
And then, whichever approach you to use for managing the annotation workflow ⏤ unsupervised, supervised, semi-supervised, human-in-the-loop, or programmatic ⏤ annotators and quality assurance (QA) team members can guide the labeling process to improve accuracy and efficiency.
Either way, it’s far more cost effective than manually annotating and labeling an entire dataset.
Speed is as important as accuracy. The quicker you can start training a model, the sooner you can test theories, address bias issues, and improve the AI model.
Automated data labeling and annotation tools will give you an advantage when training an ML model. Ensuring a faster and more accurate annotation turnaround time so that models can go from training to production-ready more easily.
Humans make mistakes. Especially if you’re performing the same task for 8 or more hours straight. Data cleaning and annotation is time-consuming work, and the risk of errors or bias creeping into a dataset and, therefore, into ML models increases over time.
With AI-supported tools, human annotator workloads aren’t as heavy. Annotators can take more time and care to get things right the first time, reducing the number of errors that must be corrected. Applying the most suitable, accurate, and descriptive labels for the project's use case and goals manually will improve the automated process once an AI tool takes over.
Ultimately, automated annotation tools and software improves the productivity of the team involved and make any computer vision project more scaleable. You can handle larger volumes of data, annotate, and label images and videos more accurately.
With the right automated labeling tools, you should be able to easily automate most data annotation tasks, such as classifying objects in an image. The following is a list of data labeling tasks that an AI-assisted automation software suite can help you automate for your ML models:
Now let’s look at the features you need in an automated data annotation tool and best practices for AI-assisted data labeling.
Here are 7 features to look for in an automated data annotation tool.
Naturally, if you’ve decided that your project needs an automated tool, then you’ve got to pick one that supports model or AI-assisted labeling.
Assuming you’ve resolved the “buy vs. build” question and are opting for a customizable SaaS platform rather than open-source, then you’ve got to select the right tool based on the use case, features, reviews, case studies, and pricing.
Make a checklist of what you’re looking for first. That way, data ops and ML teams can provide input and ideas for the AI-assisted labeling features a software solution should have.
Equally crucial is that the solution you pick can support the various file types and formats that you’ll find in the datasets for your project.
For example, you might need to label and annotate 2D and 3D images or more specific file formats, such as DICOM and NIfTI, for healthcare organizations.
Depending on your sector and use case, you might even need a tool to handle Synthetic-Aperture Radar (SAR) images in various modes for computer vision applications.
Ensure every base is covered and that the tool you pick supports images and videos in their native format without any issues (e.g., needing to reduce the length of videos).
Considering the number of people and stakeholders usually involved in computer vision projects, having an easy-to-use labeling tool with a collaborative dashboard is essential.
Especially if you’ve outsourced the annotation workloads. With the right labeling tools, you can keep everyone on the same page in real time while avoiding mission creep.
When sourcing image or video files for a computer vision project, data ops teams need to consider data privacy and security. In particular, whether there are any personally identifiable data markers or metadata within images or videos in datasets. Anything like that should be removed during the data cleaning process.
After that, you must put the right provisions in place for moving and storing the datasets. Especially if you’re in a sector with more stringent regulatory requirements, such as healthcare. It’s even more important you get this right if you’re outsourcing data annotation tasks. Only then can you move forward with the annotation process.
Comprehensive platforms ensure you can maintain audit and security trails so that you can demonstrate data security compliance with the relevant regulatory bodies.
When a project involves large volumes of data, an easier way to automate data pipelines is to connect datasets and models using Encord’s Python SDK and API. This way, it’s even easier and faster to train an ML model continuously.
Make quality control (QC) or QA workflows customizable and easy to manage. Validate labels and annotations being created. Check annotation teams are applying them correctly. Reduce errors and bias, and fix bugs in the datasets.
Using the right tool, you can automate this process and use it to check the AI-assisted labels being applied from start to finish.
Every training dataset includes errors, inaccuracies, poorly-labeled images or video frames, and bugs. Pick an automated annotation tool that will help you fix those faster.
Include this in your quality control workflows so that errors can be fixed by annotators and reformated images or videos can be re-submitted to the training datasets.
Now let’s take a quick look at some of the most efficient automated data annotation best practices.
In the same way that ML models can’t train without accurately labeled data, annotation teams need guidelines before they start work. Create these guidelines and standard operating procedure (SOP) documents with the tool they’ll be using in mind.
Align annotation guidelines with the features and functionality of the product and you’re organization's in-house data best practices and workflows.
Using the above as your process, incorporate an iterative annotation workflow. So this way, there are clear steps for processing data, fixing errors, and creating the right labels and annotations for the images and videos in a dataset.
In data-centric model training, quality is mission-critical. No project gets this completely right, as MIT research has found that even amongst best-practice benchmark datasets, at least 3.4% of labels are inaccurate.
However, with a collaborative automated dashboard and expert review workflows, you can reduce the impact of common quality control headaches, such as inaccurate, missing, mislabeled images, or unbalanced data, resulting in bias or insufficient data for edge cases.
For more information: Here are 5 ways to improve the quality of your labeled data.
Encord was created to improve the efficiency of automated image and video data labeling for computer vision projects. Our solution also makes managing a team of annotators easier, more time, and cost-effective while reducing errors, bugs, and bias.
Encord Active is an open-source active learning platform of automated tools for computer vision: in other words, it's a test suite for your labels, data, and models.
With Encord, you can achieve production AI faster with ML-assisted labeling, training, and diagnostic tools to improve quality control, fix errors, and reduce dataset bias.
Make data labeling more collaborative, faster, and easier to manage with an interactive dashboard and customizable annotation toolkits. Improve the quality of your computer vision datasets, and enhance model performance.
AI, ML, and CV models need high-quality and a large volume of accurately labeled and annotated data to train, learn, and go into production.
It takes time to source, clean, and annotate enough data to reach the training stage. Automation, using AI-based tools, accelerates the preparation process.
Automated data labeling and annotation reduce the time involved in one of the most crucial stages of any computer vision project. Automation also improves quality, accuracy, and the application of labels throughout a dataset, saving you time and money.
Ready to accelerate the automation of your data annotation and labeling?
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Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.