Have you ever done a captcha to identify some mundane object? This thing could be a stop sign, a road, a car, or a bird.
Now imagine trying to identify stop signs in a set of 30 million images, by hand. That’s the arduous process of data labeling; an essential component of machine learning involving object identification in unstructured data.
Thankfully, we can use AI to assist us in annotating images in data sets so we can get to building models faster.
Are you interested in working with cutting-edge AI image recognition and AI deep learning? Read on to find out about the latest technologies available for labeling large data sets.
First of all, we should answer an essential question.
Why is it important that we annotate images when we work with large amounts of data?
Say we are training a computer to classify images for our AI in the medical field. Eventually, we would want the AI to be able to identify different diseases on sight. To do so, while we are training the AI we would have to annotate each and every image that we feed it so that it can identify and predict diseases accurately.
Another example is a self-driving car. The car needs to be trained on millions or more images of things like pedestrians, stop signs, highway signs, and more. Mistakes in labeling can be devastating.
The success of your computer vision project completely depends on how successful you are at getting your unstructured data annotated.
As we mentioned, you don’t have to do all the labeling by hand these days. There are a number of AI image recognition and annotation tools that you can use to train your model.
These image annotation tool features can include the capacity to service models for image classification, object detection, or image segmentation.
Often, these tools will let you work within their platform or download the tool for offline use.
Keep in mind that you will still need a human user to pilot the AI-assisted image identification; it is just that the tool will speed the process along. For example, it has a wealth of options letting users outline objects to be identified in shapes like polygons or rectangles. It is also able to intelligently suggest similar images based on a small subset of pre-labeled images.
Minimize Work by Hand
Can you believe that to this day, most advanced machine learning algorithms were trained on data labeled by hand? This means that some poor data scientist out there was drawing rectangles around millions of cars for Tesla.
With AI-assisted image annotation, users will no longer have to go to such lengths and can work on refining their model.
Image Annotation a Must
Annotating images is a must for any computer vision project. The process is often laborious and requires a human to be there to label the data accurately, which is expensive and time-consuming.
Machine learning experts can thus use AI-assisted labeling technologies to speed up the process. The more these tools are used, the better they will get.
Let’s leave the archaic process of labeling by hand behind. Read more about advances in technology on our blog today.