Auto annotation vs Manual annotation: Can AI be outweighed human?
Updated: Feb 16
There are many science fiction fantasies like self-driving cars that are now turning into reality thanks to annotation tools. However, as humans are prone to errors and have limited productivity time, when the data volume becomes bigger, is manual annotation still effective enough? The answer may be no. So, it’s the time for us to take a look at auto annotation alternatively.
What is an auto annotation?
Annotation is the process by which a computer automatically adds captions, labels, or keywords to an image. Typically, the annotating task still requires manual labor, occasionally with computer assistance. By automating this step of the annotation process, projects can be completed more quickly, more accurately with minimal manual clicks.
Then, why is the auto image annotation tool so important in the high-tech world?
Our lives have already been revolutionized by image annotation techniques used in various aspects like self-driving cars, healthcare, manufacturing, and marketing. In 2022, the computer vision market is expected to be worth roughly $50 billion. Meanwhile PWC estimates that by 2030, driverless cars may account for 40% of all kilometers driven. In tandem with technological advancement, people's expectations for better services are also growing. That is creating a need for technology that can deliver results in real time. Only auto annotation tools can and does fulfill this expectation for you.
Auto Annotations vs. Manual Annotation
The traditional annotating procedure is labor-intensive and entirely manual. However, we all know that humans have a low productivity rate and are prone to mistakes despite the high accuracy rate of their annotations. Meanwhile, a fully automated approach to data annotation, in contrast, completely eliminates human involvement.
So, which results do this difference make? To get to know better, let's first dive into the difference between the manual approach to annotation and the automatic alternative.
As we currently understand it, annotation or data labeling is done manually by a group of people who work diligently to recognize things and add pertinent labels to the photos, video frames, text graphics, or even audio data. It is the most widely accepted and applied way of labeling. However, manual data annotation takes a lot of time and effort and wouldn't work well in the current fast-paced digital environment.
Additionally, annotators examine thousands of photos (and other forms of data) in an effort to gather comprehensive, superior training data for machine learning. The multiplicative effect of having large amounts of data could cause a backlog to form and delay a project. This is the justification for why AI engineers have adopted expert data annotation services widely.
However, given the reliability and excellence of training datasets, expert human annotators remain the best choice. Manual labeling can effectively spot the edge instances that automatic methods keep missing. Additionally, with qualified human annotators, massive amounts of data can be subjected to quality control.
Automatic annotation refers to any data annotation performed by software rather than a human. Automated methods have the advantage of being economical, and labeling is a rather efficient operation in and of itself.
These criteria might, however, become obsolete or even flawed over time if the structure of the relevant data changes, which would reduce the labeling accuracy. Also, it can make the algorithm useless until the changes are taken into account. Human labelers can easily distinguish between a dog and a cat in a picture, for instance, but they are unsure of the particular processes the brain takes to do so.
The majority of easily recognizable labels can be handled through automatic data annotation. This strategy can speed up the tagging process considerably. However, automatic annotation still has a lot of flaws. One of them is this might cost a lot of money when fed into an ML model.
Auto annotation with human-in-the-loop: The Best of Both Worlds
Automation is good for data annotation and machine learning in general, but it's crucial to know whether it's the best choice for your AI project. Automation has been found to quicken the labeling procedure and help ML specialists complete their tasks. Applications that need to be updated often are easier to maintain without manual annotation.
The majority of people believe that handling training data requires only physical labor. By including a machine in this laborious process, predictions provide a challenge to this notion. Due to the similar style of training and prediction data, a model's output can be utilized to quickly annotate raw data in real-time. After that, the annotation specialists can evaluate the data, clean it up, and re-feed it to the model through the pipeline that holds the training data. As a result, they achieve superior outcomes and precise predictions. This is what we do in Pixta AI, make auto annotation cooperate with human-in-the-loop, or also call making a semi-automatic annotation workflow.
What can be your choice?
After all, it seems using an auto annotation tool is an ideal solution for you if you want to get higher accuracy in quality with results delivered in real-time. However, it’s almost impossible to eliminate the need for human manpower in this process.
Because of this, our team chose to combine both the skilled human annotators and the automation might of AI, which turned into a semi-automated workflow. In another words, in Pixta AI, we apply auto annotation in the combination with human-in-the-loop.
We use labeling tools and predictive models to direct and aid our team of human labelers. The model suggests labels and automates simple tasks in the labeling pipeline. It learns by monitoring previous annotation decisions. After that, our skilled annotators provide the option to give the model the rest of the labeling tasks at each checkpoint.