Today, we are living in an era in which we will be guests in our own vehicles. From aerospace to agricultural solutions, AI offers a vast area of application since it has been integrated into every aspect of our lives. Unlocking your phone with Face ID or talking with your voice assistant for turning the lights on may seem like magic, but all of these incredible products that are powered with AI have been created under favor of one thing: data annotation.
Machine learning algorithms, especially supervised ones, need well-defined data for training themselves to distinguish various inputs and detect the information that it is looking for. At this point, it is crucial to reach data that went through data preparation processes. Through these processes, we can be sure about the data we aim to use is clean, accurate and related with the purpose. Moreover, the data must be annotated properly to categorize and label it for training the AI model.
Even though it is taken no notice by most of the people, correctly categorized high-quality data is the key aspect for training machine learning algorithms in a desired way. On the other hand, analyzing data to prepare it for the project may take longer time than expected. According to Cognilytica report that published in 2019, 80% of time for an AI project is consumed by data preparation, besides data annotation consumes 25% of time single-handed. It can be seen that labeling data fast and accurately is essential for developing successful AI projects.
To annotate different types of data like video, image and voice in an efficient way, various methods can be used. For image annotation, the bounding box technique shines out with its simplicity and rapid usage potential. Let’s deep dive in and find out how it works.
Bounding boxes are simple rectangles that are tangent to the edges of the object we want to label. By using this method, we can classify the object and determine its position on the image to help AI for recognizing it in an easy way. Considering it is quite straightforward, the bounding box technique is the most used image annotation method because of this convenience.
The Bounding box method can be used for numerous application fields that take advantage of artificial intelligence. Annotating different products in a market shelf to conduct researches about shopping habits or classifying vehicles and pedestrians for developing an autonomous car software can be given as daily life examples that bounding box method is used. Even though fast and accurate image annotation can be achieved with this method, sometimes bounding boxes cannot be the best choice. There might be too many background pixels inside the bounding box that is not related with the item we want to label, or it might not be useful for annotating 3D objects. Trying other annotation methods may offer a better solution for these issues.
Data annotation is a vital step behind training the most effective AI algorithm and bounding box method is a simple yet effective way to label data as you want. Here, at Co-one, we offer agile and blazingly fast data annotation service with an extremely high correctness rate.Are you keen to experience the privileges of Co-one? Get in touch to scale your AI!