Image Annotation Outsourcing: A Step-by-Step Guide

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Facial recognition for security, self-driving cars, and robot assistants are no longer just sci-fi movie plot devices. These are some of the groundbreaking technologies bound to shape our future. Computer vision based AI systems can now truly “see” the world and function sufficiently in our daily lives. 

Artificial intelligence (AI) and Machine Learning (ML) models are only as good as the data they’re fed. In order to successfully power computer vision, a massive amount of visual data must be annotated and inputted into the model. Image annotation is the human-powered task of labeling images to train computers in recognizing visual data on their own. This process is often prohibitively expensive, complicated, and time-consuming.

Building a team in-house labeling teams doubles the difficulty of conducting image annotation projects. Not only is it tedious, but it is also difficult to scale. As a result, many businesses prefer to outsource image annotation, data collection and other human annotation services.

What Are The Advantages of Outsourcing Data Annotation?

Save Time

Data collection and image tagging takes a huge amount of time, and it takes even longer to create and train a team for it. By working with an experienced data annotation company, you’ll be able to rapidly recruit annotators with specific requirements. Moreover, since most of the image annotation tasks are project-based, you can quickly adjust your staffing levels as required. This streamlines the whole process while ensuring a higher quality of your datasets.

Scale Easily

Data volume upheavals, resource constraints, human resource gaps and excesses, and other factors can affect computer vision projects. Outsourcing data annotation can adjust to these fluctuations and help you easily cope with change. 

Higher Data Quality

Data annotation companies are business experts with access to the right people, tools, and techniques that fit your project. With experience, they have judgment to quickly identify certain techniques suitable for a ML model.

When you’re decided to outsource image annotation for your next ML project, now you can start planning for the next big steps. 

If you are looking to outsource image annotation, there are many points to consider before choosing a partner. With an influx of outsourced image annotation companies, choosing the best one for your project can be a difficult task. Follow these steps to get started:

Eliminate Internal Bias

Bias in machine learning is one of the major challenges any AI company faces as inputting any incorrect or unbalanced data into the machine can cause erroneous assumptions and unfair outcomes. That’s why to prevent any internal bias, it’s advisable to outsource data annotation work with diverse and experienced annotators who can eliminate bias in training data.

How To Outsource Image Annotation

Step 1: Identify Your Needs

There are different types of image annotation services for specific computer vision requirements. You must first determine what your project needs and the goals you want to achieve. 

Here are some questions to guide you in planning for outsourcing image annotation project: 

  • What types of data are you working with?
  • What type of image annotation best suits your project?
  • What is the main objective of data annotation outsourcing?
  • Are you working on a predetermined budget?
  • How do you measure the efficiency of your project?

Knowing what you need and what you want to achieve enables you to effectively communicate your requirements to potential partners.

Step 2: Choose the Right Vendor

Choosing the right partner for your project will help you ensure its success. Here are some questions to consider in shortlisting your potential partners:

  • Do they have relevant experience in the field? 
    Expertise in image annotation outsourcing can vary. Annotating images for a specialized field requires specific knowledge of who will be involved with the work. Look for companies with a solid track record that fits your requirements.
  • What tools and tech do they use?
    Different tools can be used in data annotation depending on the artificial intelligence model you are working on. It is important to look into the tech capabilities of each potential outsourcing company as they can advise on the proper tooling software to execute data annotation tasks and quickly scale. 
  • Do they have security certifications to handle sensitive data? 
    You are not only looking for a partner that can help you scale, but also a partner that can keep your investments safe. Breaches in security systems can cause a lot of damage to your company. Find a partner that is qualified and strongly capable of protecting your business. 
  • Are they committed to diversity & inclusion and promote unbiased representation in their projects?
    Biases in AI and ML systems1 can ruin lives and businesses. You must consider a potential partner’s culture and how they embrace an inclusive, working environment to avoid biases in your AI models. 

Step 3: Monitor & Manage for Success

An important aspect of data annotation outsourcing is quality assurance. Your employees should have the knowledge, training, and integration required for your data services. 

Evaluate vendors on their ability to deliver high quality data by asking questions like:

  1. How accurately placed were the labels?
  2. How many data samples was the vendor able to process per day/week/month?
  3. How quickly were they able to source and train new annotators? How many different annotators worked on the data?

Your Partner of Choice

With over a decade of outsourcing expertise, TaskUs is the preferred partner for human capital and process expertise for image annotation outsourcing. We have plenty of experience collecting, annotating, and evaluating large volumes of visual data to train computer vision models at scale. Data tagging for autonomous vehicles, real-time image annotation for retail tech, and video annotation for a social media company are just a few of our image annotation credentials. 

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