Manual vs. Automatic Data Labelling Services: Top 4 Things You Must Know

Are you noticing frequent bounce back of visitors on your ecommerce site? Did you try to find out the reason behind their poor experience? Are they not getting accurate results on repeated product searches? Then, there might be an issue with your machine learning model. More specifically, the datasets used to train them might not be perfectly correct. That’s where the role of an experienced data labelling company becomes vitally important. They ensure that the data you get for training your AI models are correctly labelled and are easy to recognize for machines through Computer Vision. Since they offer manual and automatic data labelling services both, how will you determine which one you need?

To find the correct answer to that question, you need first to understand the differences between manual and automatic data labelling services.

Manual vs. Auto Labelling 

Two methods are widely popular for image annotation: manual data labelling and automatic data labelling. If you don’t know what annotation means here, it is a process of adding explanations or comments to a text or diagram. These comments work as labels for the objects in the images. Your AI model needs to learn from these labelled images to identify your products or items correctly.

Besides that, you must be clear about an important thing from the get-go: both manual and automatic data labelling services have their benefits and drawbacks. It is the main reason you need to be wise enough to choose one from both the available options based on the quality of training datasets each produces for machine learning, ok?

Manual Data Labelling

Though manual data labelling looks like a simple process, it is pretty time-consuming. The annotators need a massive amount of effort and skills to label the objects in the images manually. They get a myriad of raw and unlabelled data like videos and pictures in this labelling method. Then they have to label them following a specific set of rules and data labelling techniques.

Do you know what methodologies do annotators use to prepare training datasets for machine learning models? Let’s understand it through an example. Suppose an annotating professional has to label tons of images. Now, what will they do? They will use various types of image annotation techniques, including bounding boxes, semantic segmentation, polygon, and point cloud annotation, to label the objects in the images so that ML-powered machines could recognize them using Computer Vision Technology.

Since you might not know about any of the mentioned data labelling methods, we must help you understand that bounding box or polygon annotations are two of the most accessible techniques for image labelling. Why? It’s because they take less time and effort, while other methods like semantic segmentation take more time due to fine-grade annotations. Now let’s see –

How does manual data labelling differ from the automated labelling process?

Let’s assume that an annotating staff needs to make the objects in multiple images easily understandable and recognizable for ML-based machines. If they take an average of 10 seconds to draw a bounding box around an object while selecting the object class, it will take them around 1500 hours to do the same job for 100,000 images with five items per image. Thus, they will have to spend roughly $10K to get the whole job done.

Moreover, this process doesn’t end here because the annotators also need to examine the accuracy of the labelled images in the next stage. Now, suppose that if a trained annotator takes even one second to check each bounding box annotation, the manual data labelling process will consume enormous time until it gets complete. Hence, it will affect the total cost at the end of the day, resulting in a 10% increase in labelling price.

So, you have seen how data labelling and verification are the two most crucial steps required to perform in the manual method. When it comes to automatic data labelling, both these steps consume less time than what the manual process requires.

How reliable is the automatic data labelling method in the current scenario?

In recent times, the AI and Machine Learning automated data labelling process has evolved dramatically. Though not all automated machines have high performance, there are many cases where the AI-based labelling methods require more human intervention to correct the datasets quickly using artificial intelligence and machine learning technologies. Hence the concerned companies and authorized heads need to put conscious effort to streamline the AI-based workflow.

Last thoughts 

So far, you would have got enough idea whether to avail automatic or manual data labelling services, right? However, suppose you are still unsure about your decision and want to consult an industry expert before making a decision. In that case, the representatives of the leading data labelling company are just a call away.


You may also like...

Leave a Reply

Your email address will not be published. Required fields are marked *