Annotation is a process where both cost and quality can be difficult to predict - especially if you are unfamiliar with how it works. As a result, many companies struggle to decide where to begin and whether to outsource or keep annotation in-house.
In this article, we will explain the concerns associated with in-house annotation work, the benefits and considerations of outsourcing to a company that provides annotation services, and how to choose one.
We will also cover the burden of in-house production, the cost effectiveness of each option, and more, so if you are unsure between in-house and outsourcing, please read this article.
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Are you considering in-house annotation to reduce costs? Here is an overview of the operational costs involved in in-house annotation.
Creating annotations for high-precision AI typically requires thousands to tens of thousands of images. For example, annotating objects in photos (such as object detection or segmentation) can require several hundred images for simpler tasks and hundreds of thousands for more complex ones.
Of course, the number of images required depends heavily on a specific task and the desired level of accuracy. If you are trying to build an AI with good accuracy, or if you are building an AI model to be used in medicine, you will need much more data.
When you are to produce data in-house, you must first collect the raw data and then annotate each piece one at a time. Even with the use of annotation tools, it takes about an hour to annotate 100 images, meaning that creating a dataset of 10,000 images could take about 100 hours.
Thus, annotation requires a lot of manual work, and even with tools, the amount of work is enormous. While the use of some sophisticated AI tools for semi-automatic annotation simplifies the task, there is still a need for humans to review the annotations.
If you have access to a sufficient amount of unbiased data, enough time for annotation work, and internal AI talent who can properly select and manage data, then handling in-house annotation may be a viable option.
However, it is also true that many companies in Japan lack a shortage of AI talent and find it difficult to build high-quality AI models. In a survey conducted with 1,144 companies worldwide, about 30% of companies have concerns about the quality of AI.
Source: Ministry of Internal Affairs and Communications and Mitsubishi Research Institute Research and Study on Innovation and New Economy Formation by ICT
We also find that many Japanese companies are apprehensive about implementing AI due to a lack of human resources to lead the implementation of AI. Considering the cost of hiring advanced AI talent, it may affect the speed of business development and cost performance.
If you have the right environment that allows for in-house annotation, you can have your company's AI team build AI models on an ongoing basis, but outsourcing is often more cost-effective.
While in-house annotation may seem less expensive, the reality is that there are labor costs to complete a large amount of data, training costs to bring annotators up to speed, and ongoing management costs to oversee the work.
On the other hand, companies that specialize in annotation can produce high-quality results more quickly and efficiently. For teams without annotation experience, the process often takes much longer and impacts overall project timelines. Outsourcing helps reduce not only cost, but also time to deployment.
2. What are the benefits of outsourcing to an annotation company?
There are three main advantages to outsourcing the annotation process:
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The development of an AI system can be broadly divided into the following five steps:
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Annotation agencies are often outsourced to do the annotation in step 2 above, but there are also specialized companies that provide services starting from data collection in step 1.
Since annotation is the process of creating materials for training in step 3, the quality of annotation work is very important for the implementation of the AI system.
Each annotation work is a simple task, but to improve the accuracy of the AI model, it is necessary to verify the accuracy at different stages and add specific data according to the bias of the results.
For example, when building an AI model for automated driving, pedestrian recognition annotation requires collecting image data of pedestrians from various situations and angles for the annotation process.
A company specializing in annotation can handle such data volumes more easily.
Therefore, if you want to build a highly accurate AI model, it is better to outsource the work to a company specializing in annotation from the data selection stage.
In the case above, it is advisable to go with an annotation company with AI development expertise among annotation companies, as the same companies that only perform annotation "work" may not know how to annotate the AI model to make it highly accurate.
Nextremer offers annotation services to achieve highly accurate AI models. If you are considering outsourcing annotation, free consultation is available. Please feel free to contact us.
By outsourcing annotation, your in-house AI team can stay focused on core development tasks, such as algorithm tuning and model optimization, rather than spending time on time-consuming annotation work.
The company can expect to build highly accurate AI models and reduce overall project time by allowing employees to focus on tasks more directly related to AI development.
A company that specializes in annotation can produce large volumes of annotated data in a short amount of time, using efficient tools and experienced staff. Of course, there are costs involved in outsourcing, but the total cost is often lower than producing the same data in-house.
This is because in-house production tends to slow down progress—not only due to the speed of the work itself, but also because of training time, labor costs, and the added effort required to manage annotators.
It’s easy to imagine that annotation work moves more efficiently when handled by teams who are already skilled and familiar with the process.
Furthermore, identifying what data should be added or what additional labels should be annotated can be challenging. This is especially true when biases in the data are discovered while evaluating an AI model, unless you have the necessary skills.
Thus, if you are not familiar with annotation, not only does each task take time, but it also takes time to consider. This makes it more efficient to outsource the work and reduce overall costs.
This section explains how to select an annotation company from the following perspectives.
Annotation methods vary greatly depending on the type of data being annotated, such as images, video, audio, or text.
For example, audio annotation requires skills in audio-to-text conversion and labeling emotions. In addition, complex video annotation and speech annotation involving natural language require advanced skills.
Most companies that offer annotation services can handle just about anything. That’s been said, if you entrust them with data that is not their specialty, they may not be able to achieve the desired results.
Try to make sure that the field in which you are requesting annotation services on behalf of the company is one of their strengths.
Even for the same annotation work, different companies charge different prices per unit. Take a common image annotation, bounding box, as an example. The differences are as follows:
| Companies | １枚あたりの価格 |
| Company F | ８Yen |
| Company A | 5 Yen |
| Company T | 7 Yen |
The difference in price per unit from one annotation company to another is due to the annotator's working conditions, the availability of the tool, and the way in which the annotation is requested. Therefore, if you want to know the market price for your company's needs, you should ask several annotation companies for quotes and compare them.
Nonetheless, selecting a company based on price alone may result in an AI model with poor annotation quality and accuracy. If you do not consider the balance between quality and cost, you may need to add or replace data later. If you have a large number of objects or complex images, the cost may be higher as a result.
There are three main quality aspects to consider:
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Annotation tools and review systems vary considerably among annotation companies. Examples include annotation tools with automatic annotation capabilities, language-specific annotation tools, and cloud-based annotation tools that can efficiently handle large volumes of data.
Quality also varies depending on the tool and review system (single or double) used by the annotation company. A double review system is generally considered to be of higher quality than a single review system. If the checking system is single-check, the quality tends to be lower than with double-checking. Therefore, double-checking should be chosen, all other things being equal.
Of course, it is also important to know what kind of people are working on the annotations. If the annotation work is done by someone with little experience, the quality will be lower.
In particular, annotation companies that sell inexpensive annotations may hire crowd workers with little or no experience to do annotations.
In such cases, the quality of the annotation will be poor, which in turn can affect the accuracy of the AI, so be sure to check what kind of people are doing the annotation.
Annotation data may contain sensitive information such as personal details or proprietary business data. When choosing an annotation company, it is therefore important to confirm that they have appropriate security measures in place.
If a cloud-based annotation tool is being used, you should also check whether proper security protocols are implemented, such as where the data is stored and whether encryption is applied during data transfer.
If you plan to outsource annotation work to external cloud workers, there is a risk that internal information could be leaked to outside parties. To reduce the risk of data breaches, be sure to thoroughly review the company’s security framework, including the type of outsourcing contracts they have in place.
This article explained whether it is possible to outsource annotation and how to choose an annotation company.
If you already have AI talent and tools, it may be possible to handle the work in-house. However, annotation is a task that requires specific knowledge and skills, and specialized tools and experience are especially important for efficiently processing large amounts of data.
In many cases, outsourcing the work to a professional annotation company can be advantageous in terms of time and cost.
That said, choosing an annotation company based on cost alone can lead to issues with data quality and accuracy, potentially preventing the successful development of an effective AI model.
Be sure to evaluate annotation companies from a number of perspectives, including quality assurance, security systems, and areas of expertise, and select the one that best fits your project needs.