Do you ever wonder how we identify different objects and differentiate between one object and another? That’s a tricky question on its own -now think of how we can help a machine learning model do the same? Machines cannot see as we do. They can only understand the language of numbers. But how can we visualize an item through its number? The machine-learning algorithm uses vectors to help machines understand the data they collect, which they cannot see as humans do. They use annotations to understand and recognize an object. Let’s understand the concept of annotation and types of annotations:

What are Annotations?

When you are building a model, you need to make your model think like a human. This process requires a lot of data, so your model can make decisions by differentiating between the kinds of data. However, the algorithm will help your model process that data.

Data annotation helps you categorize, highlight, and label the data for a machine learning model. However, you need to train your model with accurate training data. We can enhance the implementation of AI in every industry through data annotation.

Annotation can find solutions to numerous problems and help us improve our customer experience drastically. Furthermore, you can use this technique for chatbots, computer vision, speech recognition, engine results, and many more applications. You can use this technology for various types of data, such as video, image, audio, and text.

Types of Annotations

There are numerous types of annotations, depending on the tasks you want to perform. Some examples include polygons, landmarks, 2D, 3D, Bounding box, masking, tracking, polyline, etc. Below, you will find some types that you can use for your machine learning model. This list will help you understand the concept. However, there are various other types of data annotations as well.

  1. Polygons

Polygon annotation helps in representing the true shape of an object. To create a proper shape, the annotators change the direction when they need it. Annotators can create polygon annotations by clicking on various points and plot vertices. Polygon captures more angles and lines than other annotations.

After mapping the object, the annotator will tag it with a label describing its properties. With these labels, a model can identify the object inside the polygon annotations. If the label’s description is not correct or incomplete, your model will not provide accurate data. You can use data annotation for warehouse robots to identify the address, stock, and packages. Here are some applications of Polygon annotation.

  • Autonomous Driving
  • Drones and Satellites
  • Agriculture
  1. Landmarking

Landmark annotation labels the object by placing the points around the object in the image. This helps in annotating the small objects. Furthermore, the annotator also uses multiple lines to outline the details. Examples of landmark annotation include objects, bodies, faces, and maps.

Computer vision projects also use a landmark to pinpoint features of a face with accurate facial recognition. The annotator adds numerous points on the face of a person with unique features. This helps the model to differentiate one face from another. Mobile phone manufacturers use the same technique as the face unlocking feature of smartphones.

  1. Bounding Boxes

You can use 2D and 3D bounding box annotations to highlight the object in deep and machine learning. With bounding box annotation, the annotator will use rectangular lines from one point to another. The starting point of the object meets the ending point, making the object completely recognizable.

  • 2D Bounding Boxes

You can use a 2D bounding box annotation to train the model to annotate the image in machine learning and AI. This type of annotation helps in making real-life predictions and recognizing the objects accurately.

These annotations help with projects that require creating the object’s visual perception in AI and machine learning. You can use this annotator for retail, eCommerce, and self-driving cars. The 2D bounding box can help the model create a visual-based perception of various objects. Many industries use this technique.

  • 3D Bounding Boxes

3D bounding boxes are advanced versions of traditional bounding boxes. These annotations are cuboids. These annotators add additional depth to the dimension of the object. This technique enables the model to highlight the object in 3D space. Furthermore, this annotator can also define the volume of the object.

Every bounding box technique uses the same anchor point technique. They mark the edges of the object with anchors. Once the model places the anchor points, they fill the spaces between every other anchor point with the line’s help. This creates a 3D box around the object. This can also define the depth of the object along with the location.

  1. Polyline

When the starting and ending points of the object’s shape are different, you can use line annotation instead of polygons. Lines are composed of different coordinates (x and y). When an object has multiple points, and every point has different coordinates, we are talking about polylines. For instance, you can track the road’s lane mark, etc.

  1. Tracking

Tracking helps in labeling the movement of the object in different frames. Various image annotation tools help you in the interpolation of the object. Interpolation means that the annotator will label the object in one frame and then identify its new position in the next frame. So you can track and interpolate the object among various frames.

Conclusion

Now that you are aware of the basic types of annotations that machine learning and AI models use to identify and label different objects. The annotations help in recognizing the text, images, faces, and other objects. You can use annotations to enhance your machine learning model’s quality with the algorithm’s help for a better user experience. Every machine learning model can only collect and utilize the data by encoding the object and text in numbers or vectors through data annotation. The model encodes and decodes instantly through data sets present in the neural networks.