To produce image-based AI systems, machine learning (ML) engineers train or fine-tune models with annotated images to develop AI systems capable of analyzing, predicting, and generating accurate, reliable, and helpful results. Image annotation, by itself, contributes to the cost of developing such AI systems.
So, what's the optimal image annotation price you should allocate in your budget? In this article, we'll explore various factors that influence the cost of annotating images. More importantly, we'll reason why image annotation price shouldn't be your sole consideration when training and fine-tuning computer vision models.
Factors to Consider for Estimating the Cost of Your Data Labeling Project
Determining data annotation costs for images is not straightforward. Often, your price is influenced by several factors.
Projects requiring enormous image datasets to be labeled will naturally cost more than those with fewer sources. You will also pay more if you use data labeling services that strictly rely on human labelers. Some labeling providers offer multi-tiered pricing that offers discounts for high-volume labeling. Despite that, larger projects will inevitably incur higher annotation costs. Hence, some ML teams augment human labelers with active learning models that automate the process to reduce cost.
Image labeling fees also vary according to the type of annotation performed. Some labeling tasks are more straightforward compared to others. More complex jobs will require human labelers to spend more time on each file. For example, an image classification task involves assigning the entire image with an inexpensive tag. However, other functions that enable object detection, entity recognition, and pose estimation will cost more.
Below are the different annotation types and an approximate cost based on publicly available sources:
A bounding box is a rectangular boundary that labelers draw to encapsulate a specific object. The image bounding box task is one of the most straightforward image annotation tasks, costing approximately $0.035 per unit.
Polygons are connected lines that labelers draw according to the object's shape. Unlike bounding boxes, it takes more effort to map a polygon, particularly if the object has uneven boundaries. Hence, annotation tasks involving polygons are pricier, costing $0.036 per unit or more.
Semantic segmentation is a tedious process of labeling each pixel in an image with the class it belongs to. Also known as dense prediction, ML engineers use semantic segmentation to help image models analyze and correlate different objects in the spatial representation. It is also one of the most expensive image annotation tasks, costing about $0.84 per unit.
Instance segmentation is a subset of semantic segmentation. Instead of tagging pixels by class, the human labeler differentiates them by specifying which object they belong to. So, expect a price unit that equals, if not higher, than what semantic segmentation would cost.
Keypoint annotation enables AI models to identify specific parts of an object. It enables pose estimation, movement tracking, and other feature detection applications. Because of its simplicity, keypoint annotation starts from a modest amount of $0.20 or less per task.
Generally, the price for image annotation depends on the task's complexity. Labeling abstract features, or those that require domain experts, will likely cost more than more straightforward annotation jobs. For example, annotating tumors, fractures, or other pathological symptoms on DICOM images is pricier than tagging cars from the street camera feed.
A trained labeling workforce will incur higher annotation costs than labelers with average annotation skills. Skilled annotators pay attention to details, reducing mistakes that may affect the model's performance. The cost difference you pay is significant when performing complex annotation tasks like semantic segmentation and polygons.
Annotation projects with tight deadlines will cost more than those with a more flexible timeline. When undertaking such projects, data labeling service providers commit more labelers and increase the labeling pace while maintaining annotation quality. This translates into higher costs that they pass on to AI project teams.
Image Annotation: Comparing all options
ML project requirements don't solely define the overall cost that you bear for image annotation. Infrastructural and operational expenses, such as building your annotation tool and contracting labelers, also determine the expenditure in an image labeling task.
In-house vs. third-party labeling tool
Some organizations choose to develop their in-house labeling tool. This approach gives organizations control over data security, flexibility, and annotation quality. However, smaller and mid-sized companies might find building their own data labeling platform not financially viable because of the substantial upfront cost. Instead, they sign up with third-party labeling tools and request human labeling when needed.
Third-party labeling services abstract the complexities in setting up the annotation pipeline. They provide a comprehensive annotation platform that project managers can use to assign, automate, and review image annotation tasks. For example, Kili Technology provides an interactive semantic segmentation feature that applies an ML model to aid human labelers. This way, you can reduce the time it takes to segment objects without compromising accuracy.
Pricing model that third-party tools provide
If you choose to work with third-party tools, be mindful of the fee structure that they impose and the associated terms. Choose a pricing option to meet your annotation requirements. Below are several common ones.
Free. Open-source tools don't impose charges on usage. Similarly, some labeling providers apply a freemium model, allowing users to get started without payment. However, these free-to-use plans may impose limitations, such as basic annotation types, dataset size, or project users.
Pay as you go. Some tools provide access to annotation features and only charge ML teams when exporting the annotated images. Like free data labeling tools, the flexibility might be tied to certain feature limitations, accessible only by upgrading to a more expensive plan.
Subscription-based. With this option, you may pay a fixed fee to access labeling features and capacity stipulated in the plan. This option may benefit ML teams that require annotating larger datasets each month.
Custom pricing. Large enterprises seeking the most cost-friendly plan from external data labeling providers sign up for a customized plan. This model includes bulk pricing and long-term commitment that results in savings for enterprises when they submit a data labeling request.
In-house vs. outsourced labeling workforce
To annotate images, you need human labelers, reviewers, and domain experts to work with the labeling tool you subscribe to. Some companies assembled their labeling team to seek more control over the labeling process. Budget-constrained startups may annotate images to minimize the expense of engaging third-party labelers.
Either way, in-house labeling is a good starting point for smaller annotation projects. With this option, companies are responsible for training, coordinating, and setting up the entire labeling pipeline to ensure quality annotation. However, in-house annotating can be challenging to scale once project requirements grow.
Therefore, organizations outsource their annotation tasks to third-party labeling service providers for larger projects. Outsourcing gives you access to high-quality datasets delivered by a team of trained labelers. Professional labeling service providers connect businesses to a diverse industry-trained workforce and implement robust security measures to protect data privacy.
Pricing model for outsourced data labeling
Outsourced labelers may charge ML teams per annotation units generated and the number of human labelers involved. Some provide tiered pricing, where customers are entitled to discounts for larger volumes of jobs.
For example, Amazon offers data labeling via its Amazon Mechanical Turk workers, with rates starting from:
Image classification: $0.012 per label.
Bounding box: $0.036 per label.
Semantic segmentation: $0.84 per label.
However, this does not include the price for each object that needs labeling or the number of labelers included. Projects that require less than 50,000 units will cost $0.08, while projects that require more than 50,000 units will cost $0.04.
For example, if a company wanted to do image classification on 60,000 images to build a computer vision model and agreed to have five human labelers sourced from Amazon Mechanical Turk to ensure its accuracy, the formula would look like this:
Total Cost = (50,000 x $0.08 per image) + (10,000 x $0.04 per image) + (60,000 human-labeled images x $0.012 per post x 5 labelers per object) = $8,000
As a comparison, Google charges the following rate for image annotation tasks below 1,000 units per task.
Image classification: $0.035 per unit.
Bounding box: $0.063 per unit.
Semantic segmentation: $0.87 per unit.
These prices are multiplied depending on the number of human labelers outsourced from their team. So, for a similar case, the pricing would be:
Total Cost = (($0.035 x 50,000) + ($0.025 x 10,000)) x 3 = $6,000
Image annotation price varies amongst service providers because of differing workflow, technologies, and capabilities. Explore Kili's data labeling tool and professional labeling workforce for a custom quote tailored to your specific image annotation needs.
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The hidden costs of low-quality data
Choosing price over quality when it comes to data labeling has serious consequences. Our clients have discovered this before turning to our image annotation tooling and workforce solutions.
A global manufacturing company faced production bottlenecks and increased lead times due to manual inspections. They turned to AI for automated defect detection but quickly realized the critical importance of high-quality training datasets. Poorly labeled data could result in defaulted components going unnoticed, leading to customer complaints, returns, and costly replacements.
We have prevented faulty components to be assembled and defective products to be shipped – which allows us to reduce cost by 25%. I certainly see the impact, positive results are here.
An insurance company aimed to transform its car damage assessment process through AI. The company faced challenges in building a relevant training dataset with high-quality labels. Any inaccuracy in the training data could lead to miscalculations in repair cost estimations, resulting in either company loss or customer dissatisfaction. The company found that high-quality data annotation was crucial for speeding up the claim process and improving customer satisfaction.
I believe we reduced more than 32% of the number of what we describe as “follow-up calls” where the customers ask for the status update of their claims while damage assessment is ongoing. What once took ages, we made it real-time. We keep on getting positive feedback from customers.
Other costs of low-quality data:
Poor data quality can lead to inaccurate model training, causing algorithms to make incorrect predictions or decisions. This necessitates increased iteration cycles to correct the flaws, consuming more time and computational resources.
Low-quality data can inflate validation efforts, requiring more rigorous testing and quality assurance processes to ensure the model's reliability. These factors contribute to higher operational costs, as more human resources and computational power are needed to rectify the issues.
The time spent correcting these issues results in delayed time-to-market, which can be a critical disadvantage in today's fast-paced business environment.
The costs of low-quality data are not just immediate but have a cascading effect that can compromise the long-term success of data-driven initiatives.
Pricing for image annotation projects depends on several factors, including complexities, annotation types, and timelines. Labeling tools and service providers offer differing prices for project teams seeking an alternative for setting up their own labeling pipelines. Regardless of your choice, annotation quality takes top priority as it determines the model's eventual performance.
Therefore, take your time with a decision when choosing a data labeling tool and service provider. Compare their offerings, particularly how they uphold guarantees on quality when annotating complex or large volumes of images. Also, check out their past projects and how other clients felt collaborating with the providers.
Then, engage the provider for a small pilot to gauge their deliverables. Expand to larger projects when you're confident that they provide values worthy of the price you pay.
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