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Publish Date: April 12, 2021 10:08 AM
Outsourcing image annotation services is one of the most crucial tasks for AI companies that seeks training to develop the models. Actually, machine learning training data is a kind of fuel works for algorithms to learn from various patterns and predict in the same way.
And you need to be very careful while outsourcing your project to such companies to get the best quality data at the least cost. So, in the context of the same, we brought here the key points to be considered while outsourcing the image annotation services.
On the basis of your project needs, it is important to properly identify how your data will be handled. So, here you need to decide before handing over your data, how you want your image annotation to be verified.
Image classification is the best example you can consider while checking the task of the annotators with selecting the appropriate label from each image. Here the human-powered image classification process can be affected by biased decisions.
So, to avoid such preconceptions check the data processing from one stage to another stage. As one annotator labels the image while double-blind verification, two annotators label the image without sighting each other’s task. And if the labeling is not up to the mark, then it is the responsibility of a supervisor to observe the cases and decide the correct label to ensure the quality.
And finally, multi-blind verification, at least three or more annotators label the images without viewing each other’s work. And here if labels don’t match each other’s data, consensus or adjudication is used. With consensus, majority rules and the most common label among the annotators are chosen. While with adjudication, a supervisor is brought in and decides the correct label.
Different companies have different standard systems used in data entry and annotation. But the hard-and-fast verification process takes a long time and maybe available at additional costs that can spike your budget and spending on your project.
Evaluating the quality of the services of a company you need to check the historical background and work done by the company in the past. Yes, check the portfolio or data sample produced by the company. And if possible for the demo or similar approaches to making sure it has the capability to meet your data annotation needs.
To check that you can go to the website of the company, check the portfolio, clientele to examine the image quality and annotation precision. Most of the companies providing the graphics in various formats to represent their workbench. And few of them already have samples with annotated images from the different fields.
Apart from the verification of data, you also need to clearly share the standard of quality you are looking in each annotated image. Many companies claim to provide accurate training data but what is the meaning of accurate in your terms.
Actually, there are multiple types of image annotation techniques like bounding box, cuboids, polygons, and semantic segmentation but what kind of annotation you are looking for your AI or ML project.
Actually, in some annotations, the picture needs to zoom at a very large size to annotate the objects at the very edge to adjust the annotation at pixel levels. Though, few projects may allow the margin of error, while many of them not allow a single margin and need 100% accuracy in each image to get accurate results.
So the important point here is, that you need to clearly define your quality of standard at the time of assigning the project to image annotation companies. And also give an example of what kind of quality or standard level you expect in image annotation.
You also need to explain the exact format and type of file with the data batched and quality control system you want to implement into your company.
For that, you can request a trial project while paying some small charges to check the quality and accuracy level. It will help you to check their speed, quality, and other aspects while performing image annotation on a real-time basis.
Every machine learning training data company has its own business model and workflows, and staffing system with specialization in particular annotation types and fields or industry to determine who is exactly going to work on your project.
Actually, companies provide onsite, remote, and both types of image annotations as per the needs and feasibility of the clients. You need to ask these things to companies providing or not, what are the qualifications of annotators, and their training level. So, you should ask here these questions to such companies.
Although, for image annotation, certain specialized qualification is not required, except medical images like CT Scan, MRI or X-rays. If you are outsourcing healthcare-related such fields for medical imaging analysis, make sure the company has all the required resources and such experts from medical background to ensure the accuracy.
Last but not the least, it very important to decide the right platform for image annotation. As some companies have their own platform while few of them use other third-party platforms to annotate the images and charge additional fees for using that.
However, annotating with the company’s platform has its own benefit like the company owns the platform and can customize the functions as per your project. And another advantage is the annotators are full-aware with user-interface and functions of the platform and they don’t need additional training to learn how to operate.
While on the other hand, if you have your own custom platform or you are using a third-party platform that you want to use. If you require annotated images on any other external platform as per your choice, you may have to pay the extra fees to train your staff to operate such software, but make sure to inquire about platform fees.
However many of the companies use the best software to annotate the images with their annotators. And if they are meeting your needs, you should go on with their sources to optimize the cost of your AI project development.
Publish Date: August 24, 2020 10:14 AM