How to Implement Your Data Annotation Project

Category: AI Insights

Published date: 14.08.2020

Read time: 8 min

Every AI is geared towards making machines think and act like human beings. Regardless of the end result you are trying to achieve, you will need to have images, texts or other annotated datasets for the machine learning algorithms to learn from. On the surface, having accurately annotated data may not appear to be the most important aspect, it is still a critical component of the entire project.

While data annotation is a very time consuming and tedious task, the success of your projects ultimately depends on it. Within the machine learning community, the phrase “ground truth” is used to refer to the accuracy of the data used to train the machine learning model. In order to help you get off to a good start, we compiled some best practices to follow based on a proven record of success.

Data Annotation Project

Determine Your Exact Needs

The types of data annotations span the gamut and will vary from project to project. The following are the most common types:

  • Image annotation
  • Speech recognition
  • Audio annotation
  • Video annotation
  • Text annotation
  • Optical character recognition
  • Data labeling
  • 2D bounding boxes
  • 3D bounding boxes
  • Semantic and instance segmentation
  • Key-point and landmarks
  • Lines and splines
  • 3D point cloud

Now that you know all of the various types of data annotations, you will need to decide how you will get all of this done.

Decide on Who Will Do the Annotations

When it comes to data annotation, you have several options to choose from when deciding how to get the data annotation done. Let’s explore the options you have:

  • Doing it all in-house – You can ask your developers and researchers to annotate the data in addition to their core functions, but this will not be a good use of their time. This will also be very expensive given that highly qualified specialists demand a good salary. Therefore it is a good idea to allow your staff to focus on their jobs and shift the data burden to a service provider.
  • Outsourcing – It is a good idea to hire an offshore team to handle all of your data annotation needs for many reasons. You will get a higher quality ground truth faster and at a lesser cost since you will be able to hire your team according to your criteria. In fact, as time goes on, you will start to think of your offshore team as an extension of the one you have in-house.
  • Crowdsourcing – This is when you hire people from around the world to help you annotate the data. However, there is no control over the quality of the completed tasks or whether they get done at all and high cybersecurity risks as well.. Crowdsourcing is hit or miss and there is no reason to take such risks given the amount of time and money you have invested in developing your product.
  • Synthetic – In the previous section, we talked about all of the various data annotation types which require human annotators to complete. Now, imagine machines producing images with 2D bounding boxes and other data annotation types already included in the image. While this may appear as a very intriguing option, certain data annotation methods, such as semantic segmentation, still needs to be done by humans to get the correct ground truth.
  • Programmed – Think of this as machines training other machines. Instead of humans doing the data annotation, machines are performing such tasks. If you decide to go with this option, you will need to hire a dedicated QA team to make sure all of the annotations were done correctly.

Set Up the Annotation Infrastructure

If you will be trusting a service provider to handle your data annotation needs, be sure to discuss the following points in great detail:

  • Quality level requirements – While everybody would like to have a 100% quality score, mistakes will happen due to the human factor involved and the sheer volume of the data. Be sure to specify the quality score you would like to see. Ask about the QA process in place to make sure this goal is reached. If a score of 99% and higher are required, data can be annotated in rounds to minimize human mistakes.
  • Type of data annotation you need – We mentioned all of the various types of data annotation. Ask the service provider if they have experience with the exact one you are looking for. Be sure to carefully study their case studies, if possible.
  • Deadlines – You need to come to terms with the service provider about the deadlines of your project. While you may already have a deadline for the project delivery, think about how quickly you need your team assembled. This will directly affect whether or not the project is completed on time.
  • Amount of data to be annotated – AI and ML projects usually require thousands and thousands of images to be annotated especially if you are in the automotive and healthcare industry. You need a partner who has experience actualizing such large-scale projects.
  • Security – You need to make sure that all of your intellectual property stays secure. Ask about the measures in place that reduce or even eliminate the possibility of leaked information.
  • Workforce – One of the first things you need to ask the service provider is whether or not they have the necessary personnel to get the job done on time. Ask them how quickly they can assemble the amount of people you need for your project.
  • Tools and output type – The tools used to annotate the data will be entirely up to you. Does the service provider have the necessary expertise using a particular software? Can they customize it to fit specific workflows? It is also critically important in what format the output will be provided.
  • Guidelines – It is important that the service provider has internal guidelines and processes in place to actualize your project. You should carefully evaluate them and tell them about any particular requests you have.

Trust Mindy Support to Organize Your Project

Thanks to our extensive track record of successfully actualizing projects of all sizes, we know how to put processes in place to improve the accuracy of the data annotation and, consequently, the AI algorithms. We have a rigorous QA process in place to make sure everything was done correctly including automated quality assurance. In fact, we were able to achieve a quality score of 98% on certain projects when our partners only had 95%. Since we are one of the largest BPO companies in eastern Europe, we can scale projects without sacrificing the quality, which allows us to assemble even the most sizable teams and get projects done on time.  Mindy Support is a trusted BPO partner for a number of GAFAM and Fortune 500 companies offering the best quality-price combination in the market. Contact us today to start your free trial in data annotation.

Posted by Il’ya Dudkin

How to implement your data annotation project


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