Automated Data Labeling vs Manual Data Labeling and AI Assisted Labeling
Data is the fuel for Machine Learning-based AI developments that provides the set of patterns that can be recognized and understood by the machines thereby, storing such information for future references. A huge amount of such datasets is required to ensure and widen the horizon of Machine Learning, to analyze the various scenarios and predict with accurate results.
But the problem here lies in producing many quality datasets. This is one of the biggest challenges for 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.
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 tools and software. But this process takes time and lots of effort to produce the Machine Learning training datasets as per various needs.
Manual vs Auto Labeling
Technically, there are two approaches – Automated data labeling and Manual data labeling used to annotate the images. Both approaches have their advantages and disadvantages. So, to select one, we need to be wise in distinguishing which one is the best approach to produce high-quality training datasets for Machine Learning.
Today in this blog post we will discuss manual versus automatic data labeling processes. We will also shed some light on the 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 it is very time consuming and it requires more skills and effort 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 two of the easiest and cheapest annotation techniques, taking less time and efforts while semantic segmentation takes more time and fine-grade annotations. In the manual data annotation process, objects are selected from a given list.
Compared to the automated labeling process, if you would 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 it would cost around $10K to label this data
Also Read: What Are The Applications of Image Annotation in Machine Learning and AI
Apart from the main annotation process, quality check is another stage where the manual data labeling process takes enormous time. During this, it would take a trained user approximately about one second to check-off each bounding box annotation, resulting with an increase of 10% in the cost of labeling.
While few workflows may choose to adopt consensus-based workflow, the time and the money spent are proportional to the number of users that work on overlapping tasks for consensus. To simplify, if you have deployed three users to label the same image three times, you have to pay for all three annotations thereby, 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, data labeling, and second, checking and verifying to ensure the quality of annotations. In automated data labeling, the data labeling and verification process takes lesser time compared to the manual annotation.
But now, thanks to AI and Machine Learning automated data labeling process has come a long way. Though not all 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 needs to be a conscious effort, to ensure how the overall workstream is affected by an AI-enabled system.
So, in the next clause, we will discuss 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 time and efforts in data labeling, we need to understand the workflow process and find out the key areas, wherein 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, this data is considered valuable for re-training the Auto-label AI and a human labeler who will proceed to correct the errors on trial basis.
- 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 the various models.
Let’s assume that a well-trained AI model is available and is 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 needs 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 to improve model performance.
Hence, AI companies are in a quest of developing the most accurate and the highest performing Machine Learning model. However, the sizes of datasets are not matters of concern, 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 in labeling the high-value data. Thus, the AI-assisted data labeling process provides a better capacity to produce the true datasets making AI possible in 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 with ideas on which object in the image needs to be annotated. 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 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
The use of the 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 to utilize their skills in ensuring the quality and accuracy making every data understandable to machines for appropriate predictions.