Data is the fuel for the machine learning-based AI developments providing the set of patterns that machine can recognize and understand storing such information for future references. And a huge amount of such datasets are required to make sure machines learn everything to analyze the various scenarios and predict with accurate results.

But the problem right here is producing a large number of quality datasets that becomes one the biggest challenge for the machine learning engineers. Data labeling, especially for visual perception AI models like self-driving cars, drones or robotics needs annotated images to understand the surroundings better and take actions accordingly.

And data labeling is the process of making the objects recognizable to machines through computer vision. Image annotation is the technique used to annotate the images manually using the tools and software. But this process takes time and lots of effort to produce the machine learning training datasets as per the various needs.

Manual vs Auto Labeling

Actually, there are two approaches – Automated data labeling and Manual data labeling used to annotate the images. Both approaches have their advantages and disadvantages, and which one is better to produce high-quality training datasets for machine learning.

Today in this blog post we will discuss manual versus automatic data labeling processes with more advanced auto data labeling methods used in the industry for a better and more efficient way of producing the machine learning training data sets at the best pricing.

Manual Data Labeling

The manual data labeling process seems very simple but time taking and requires more skills and efforts to manually annotate the objects in the images. In this process, annotators are presented with a series of raw, unlabeled data like images or videos and are tasked with labeling it according to a set of rules or specific data labeling techniques.

Let’s take an example while annotating images, various types of image annotations like bounding boxes, polygon, point cloud annotation and semantic segmentation are used to make the objects in the images recognizable to machines through computer vision technology.

Also Read: How to Build Training Data for Computer Vision

Bounding box or polygon annotations are the easiest and the cheapest annotation takes less time and effort while in semantic segmentation takes more time and fine grade annotations. In the manual data annotation process, objects are selected from a given list.

Compare to the automated labeling process, if you assume that it takes a user 10 seconds to draw a bounding box around an object, and select the object class from a given list. If there are datasets of around 100,000 images with 5 objects per image, it would take around 1,500 man-hours to label and this would cost around $10K to label this much of data.

Also Read: What Are The Applications of Image Annotation in Machine Learning and AI

Apart from the main annotation process, checking the quality is another stage where the manual data labeling process takes time. And during this approximately it would take a trained user about one second to check-off each bounding box annotation, resulting in a 10% increase in the cost of labeling.

While few workflows may choose to adopt consensus-based workflow, the time and money spent are proportional to the number of users that work on overlapping tasks for consensus. Simply understand, if you have deployed three users to label the same image three times, you have to pay for all three annotations increasing the annotation cost.

Also Read: What is the Importance of Image Annotation in AI And Machine Learning

In manual data labeling, these two steps are very crucial – first the data labeling and second, checking and verifying to ensure the quality of annotations. And in automated data labeling the data labeling and verification process takes lesser time compared to manual annotation.

But now thanks to AI and machine learning automated data labeling process has come a long way. Though, not all the automated systems are developed equally but in many cases, the use of AI-based labeling needs more human participation to correct promptly by the AI. Hence, there need to be very conscious, how the overall workstream is affected by AI-enabled system.

So, in the next clause, we will discuss the automated data labeling and how more advancement into this field is making the data labeling task more efficient and easier.

Automatic Data Labeling

To reduce the time and efforts in data labeling, we need to understand the workflow process and find out the key areas where we can make the system automated.

  • Auto-Label AI labels raw, unlabeled data.
  • A human user verifies the label.
  • If data is properly labeled, it is added to the pool of labeled training data.
  • If data is incorrectly labeled, the data is considered valuable for re-training the Auto-label AI and a human labeler will proceed to correct the errors.
  • Once the data is labeled at a satisfactory level, it is then used to re-train the Auto-Label AI and subsequently added to the pool of labeled training data.
  • Finally, ML teams use the compiled labeled training data to train various models.

Let’s assume a well-trained AI model is available and making accurate predictions in most of the cases and only the edge-cases need human assistance to check and correct the errors. Data Labeling Process contains most of the manual process which is usually done by humans.

Also Read: How Model Predictions are used to Increase Data Labeling Speed and Improve Accuracy

Even for AI models like autonomous vehicles that need a huge amount of training datasets to train the machine learning algorithms, most data points only have the marginal value to improve the performance of the model. Though, it is rare, edge-cases in the long-tail distribution of data (like car accident scenes) that helps improve model performance.

Hence, AI companies in quest of developing the most accurate and the highest performing machine learning model, but the sizes of datasets are not matters enough, instead, the game of finding and gathering the most edge-cases is important to train the AI model.

Also Read: What are the various Types of Data Sets used in Machine Learning

Hence, it is more important for a company to set aside the unimportant cases of data that AI models are already well-trained on, and devote more time and effort sensibly to labeling the high-value data. Thus, the AI-assisted data labeling process provides a better capacity to produce the true datasets making AI possible into various emerging fields.

AI-Assisted Data Annotation for Faster Speed with Accuracy

Though the automated data labeling process provides multiple times faster speed labeling and the advancement of technology brings more efficiency and quality. Human-in-the-loop machine learning is important to ensure quality and accuracy while labeling the data for machine learning.

Using the AI-assisted labeling process makes the labeling task easier and provides them ideas on which object in the image needs to be annotated and once the data is annotated, annotators can manually check or fill the area left behind or need proper annotation.

Cogito is one of the top data annotation companies, providing the AI-assisted data labeling services to deliver the most advanced annotation platform to produce high-quality training datasets for machine learning and deep learning based AI models.

Also Read: How to Measure Quality While Training the Machine Learning Models

Use of right AI-enabled tool and data labeling software is important to produce a huge volume of training data for different types of models. Data labeling with machine learning is not only multiple times faster but also gives an edge to annotators utilize their skills in ensuring the quality and accuracy making every data understandable to machines for the right predictions.