Introduction: Label Images Properly for AI
Scientists are addressing Artificial intelligence as the last human invention which will offer formidable and all-powerful solutions. Time will tell how much this proves right. At least, at present, AI experts are grappling to have functionally viable datasets to build those invincible models.
AI algorithms like computer vision models can emulate human intelligence, however, only when supplied with quality image datasets in required quantities. Machine learning engineers and data scientists looking to actively leverage AI want impeccable resources to generate impeccable images. The entire hassle is for achieving seamless prediction accuracy. And accuracy matters in decision-making and ultimately in developing long-term success strategies.
Against the unprecedented shifts and rapid rise of unconventional solutions, organizations are keen to leverage human-augmented automation-enabled labeling systems.
Here, we have discussed in-depth the challenges in image annotation and the solutions, while also taking you through image annotation best practices.
Understanding top 5 challenges in image labeling
Image annotation is easy is a misnomer; It starts not just with tagging of designated parts and doesn’t end with thrusting those tagged images into the AI model training dataset. Always focused on maintaining 100% labeling accuracy, image labelers have to encounter different challenges along the entire labeling cycle. Discussed below are such 5 most crucial challenges in image annotation.
1. Ensuring data relevancy and quality
Yes, image data quality is a concern for image annotators, mostly when the project moves towards its mature phase. Extracting relevant insights i.e. target areas of images, as a result, turns out to be an expensive affair. Data quality management requires considering different points and applying different standardization techniques.
It’s always a cumbersome process for image annotators to create a foundational framework that can guarantee annotation quality. Since image annotation requirements vary from problem to problem, image annotation must be capable of working with any image type. Erroneous labeling causes a ripple effect, impacting downstream activities that rely on initial labeling executions.
To accurately capture image properties is grueling for human labelers, since when handling hundreds and thousands of images, they may miss tagging important image objects. As such maintaining consistent accuracy is a tough thing to follow and is also a potential source of bias.
2. Managing costs and optimizing time
Images are unstructured data types, and building quality training datasets out of unstructured datasets require you to invest heavy amounts. You have to implement strong quality assurance to justify the worth of your investment. So, mere investment in image data annotation doesn’t work.
Even when you know you can invest and build your own in-house team, calculate the time to build the ideal in-house setup. Of course, this takes several days and months together. Apart from this, you also have to identify and choose the best data annotations tools, develop robust human-in-the-loop processes, and be ready to tweak your infrastructure to make the entire framework flexible to scale.
Ultimately, it all comes to your estimation abilities and compels you to make estimations that don’t squander your resource budget. Overestimation takes a toll on your other important business processes while labeling activity suffers due to underestimation.
3. Sustaining data confidentiality
Image labelers commonly face challenges posed by the sensitive nature of image data. Lack of robust data security makes images vulnerable to misuse because images pass through stages in the annotation cycle and are processed by different stakeholders.
AI stakeholders are always concerned about image security and don’t want the confidential information the images carry to be used wrongfully. So, this makes them accountable to have security mechanisms that store and process images securely. In terms of security standards, such a security framework needs to meet GDPR, CCPA, and other compliances, to act as a licensed framework for image annotation.
So, maintaining data confidentiality means having a secure data storage system. However, even when you decide to not image annotation yourself, the security factor becomes a headache. The obvious reason is you are handing over all your data to an external agent and you validate the annotator’s security mechanism.
4. Not letting the process slow down
Image annotation requires stakeholders to follow a feedback loop to achieve 100% perfection. Despite desiring to have perfectly annotated images, it is never ideally possible to have perfectly annotated images in one go, when you have to annotate several thousand images in a stipulated period.
Poorly labeled images – incompletely labeled, incorrectly labeled – make computer vision experts engage with image annotators. Though communication is essential, operationally, repeated communication cycles waste technical experts’ time. Computer vision algorithms cannot work on poorly annotated datasets, and so AI engineers have to detail the nitty-gritty in the modeling process to labelers.
The process slows down, making you annotate lower than the target number of images or the predicted number of images. Down the line, this impacts training speed, as ultimately the entire AI modeling process suffers.
5. Building a scalable support system
An image labeling framework comprises several components. Human image annotators, automation software applications, AI/computer vision experts, industry experts are a few of them. Encountering a need to scale creates a need to scale each resource, in a different manner.
But scalability is extremely difficult to achieve when you have to simultaneously scale each resource. This poses several challenges – financial, operational, and strategic. How?
Suppose you are annotating five thousand images on a day-to-day basis, and have been executing this activity for over half a year now. You are pretty sure that at most the number of deliveries will alter by a few hundred. However, suddenly, your AI team has new members who suggest complete revamping of the existing models. This also impacts the way you have been labeling your images. Now, your AI team knows how to develop the proposed AI model, but wants you to supplement their efforts by providing them twenty thousand images. Don’t now you find yourself bottlenecked with the existing situation? Having scalable image labeling is thus a big gamble.
4 best ways to address challenges in image labeling
Discussed below are the top four ways to easily address the commonly encountered challenges, seen above.
1. Follow a standard labeling protocol
Protocols are essential because however tricky an image labeling problem maybe when you build standard protocols, you easily correlate the problem context. The nature of the AI model governs the process to label images. So, you might require to label images for classification, prediction problems, etc.
Protocols comprise helping annotators with detailed instructions as well as requisite measures to follow for consistent results. They align themselves throughout the AI lifecycle and affect the output. With protocols, you optimize each step in the annotation process and thereby simplify the process to churn out quality training datasets. Quality datasets lead to quality models which lead to accurate predictions and classifications.
2. Crowdsource to scale
Crowdsourcing is one solution that helps you when handling image annotation assignment that has high-quantity delivery commitment. It is a great option to tap into remote talent based across diverse geographies. Thus this helps you control costs and achieve in-time executions.
True that crowdsourcing requires very strong quality assurance, since image annotators work from disparate locations, and operate independently. But managing this workforce as your in-house team fetches results that you can cherish. Crowdsourcing is a ready-to-go option when you are budget constraint and don’t have a good image annotation team.
3. Synthetic labeling
Synthetic labeling offers solutions to almost all possible errors in the image labeling process. Cost-effective and faster, synthetic labeling guarantees pixel-perfect annotations and helps you adhere to ground truth requirements. With synthetic images, you don’t face issues of scarce data, and so your AI modeling doesn’t halt for the lack of insufficient image quantity.
Models that are trained on synthetic images convert a time-consuming and laborious process into a performance-driven image annotation loop which yields extraordinary results. Synthetic images allow you to adjust objects to match real-life dimensions.
4. Outsource image labeling
To develop a heavy-duty AI model you need a very strong image labeling workforce. Such projects usually span several days and also require real-time data input. Introspect your skillsets and if you realize that you cannot meet the requirements of the assignment with your in-house team, outsource image labeling. In most circumstances, this is the most viable option from a cost optimization perspective.
Successfully managing large-scale image annotation projects demands a combining operational and strategic expertise. The easiest way to avail of these competencies is to hire a professional image annotation expert. So, outsource image annotation to a reputed image annotation company for assured operational excellence and justify your strategic actions.
Best practices for labeling images for AI
Prescribed below are the best practices to develop a successful image labeling practice.
1. Build an annotation framework
To trigger the right start to your data annotation project you must possess a very clearly outlined data annotation framework. You should address those using standard frameworks (SOPs) for each important process and sub-process. A data annotation framework should be your guide to help you in the selection of annotation techniques.
Apart from providing the right technique which could be any from semantic segmentation, lines and spines, polygons, the framework must clearly assign a role to each stakeholder in the annotation lifecycle.
A robust data annotation framework is marked by a robust tagging taxonomy. Broadly, annotation taxonomies are of two categories – horizontal and flat. Flat taxonomy suits low-volume, single-type images while horizontal taxonomy helps in high-volume multi-type images.
2. Measure and track process quality improvement
To improve your image labeling efficiency continuously, you must leverage quality intelligence that monitors image labeling quality. Your image labeling quality determines the effectiveness of AI algorithms i.e. how accurately an AI model will function to produce credible results.
Quality issues are caused by several factors and you must analyze all such factors. For instance, if you label images to train an AI model built to categorize moving cars from non-moving cars, then both categories must exist in 1:1 proportion. If moving cars make 80% of the sample, then that creates imbalanced datasets.
Image labeling quality as such doesn’t restrict itself only to the labeler’s efficiency. So, this makes you have a data assurance process that can help you to measure data consistency and accuracy. Set benchmarks for labelers as well as for processes. Also do not forget to build a consensus framework to achieve agreement amongst all system components.
3. Ensure streamlined communications
Define a clear communication framework that assists each stakeholder – data labeler, AI engineer, domain expert in the AI model building process to coordinate easily. Data labeling doesn’t stop at data labelers proving labeled image datasets to AI engineers, rather it should be accountable till output generation.
Outline intra-process as well as an inter-process communication strategy. Establish a protocol to enable effective communication between data labelers and AI engineers – to enable AI engineers to explain their requirements to data labelers who can then chart the right course.
Without the right communication, stakeholders work in a siloed environment, thereby maximizing the chances of failure. As against this, smoother communication successfully minimizes risk, complete project within deadlines, and streamlines task management. When your data is speaking visually, right oral and verbal communication matters in the labeling success.
4. Encourage review and feedback
A dynamic image labeling process has several percentage higher chances of succeeding than a static labeling process. Reviewing labeled images and rightful feedback makes your image labeling dynamic. Undoubtedly, image labeling is susceptible to errors, and review and feedback help mitigate them.
Error communication in image annotation boosts training datasets. Feedback should come from AI engineers to domain experts, so as to tagging efficiency can be enhanced. Feedback allows your workforce to revisit the guidelines and sustain the attained knowledge to ensure higher accuracy.
Update the review mechanism after you capture an error hitherto not encountered. As such, the feedback and review mechanism updates the framework itself. So, this works iterative framework that progressively updates and expands itself.
5. Execute pilot implementations
Don’t directly venture out to starting image annotation. You are not just labeling images, but building a high-quality image training dataset for a practically viable AI model. This means you must first go for pilot implementation to gauge labeling process efficiency.
The pilot implementation offers an opportunity to leverage the framework for testing its capability to manage real-life scenarios. A pilot project helps you to gauge the strength of your image annotation skills. This offers direct feedback about the efficiency of your existing workforce.
Overall, pilot implementations give you the right insights into the gaps in your image labeling, and thereby help you to take corrective steps, improve actions. This increases the success chances of your image labeling. Now you know what processes to improve, what resources to bring, whether to depend on your in-house setup or to outsource image annotation. This feedback comes in handy in actual project execution.
Why does human-in-the-loop matter for high image annotation quality?
Image data quality is the cornerstone of any AI model. Your computer vision algorithm becomes operationally functional only with a quality image dataset. Since image annotation heavily relies on human experts in the labeling process, human-in-the-loop efficiency plays a pivotal role in image quality assurance.
Notably, human experts must assure quality across all three important stages in AI development, which are:
- Data collection
- AI model training
- Model fine-tuning
Despite automation making headway in the annotation process, human intelligence still reigns supreme and supersedes automation-based quality assurance. However, human expertise must be applied at all important junctions in the labeling process. Progressively this eliminates blind spots, perfectly trains for edge cases, and accurately trains for new tags.
Adopt the best path to building the right AI models using flawlessly annotated images
A perfect data labeling framework makes your AI model while a haphazard approach collapses it. Every image annotation problem is characterized differently and so the challenges too differ. Based on your capabilities, choose the course that can help you boost your ROI through AI implementation.
Develop a clear and in-depth understanding of the possible challenges and come up with the best solution. Enable a streamlined communication channel for hassle-free collaboration across functions. Technically, use automation along with human-in-the-loop, so that your solution is versatile and scalable.