Data annotation or data labeling is crucial in machine learning and finds its application in various sectors. There are different types of annotation, such as images, videos, text, etc. In this blog, let us know more about image annotation.
Image annotation
Image annotation plays an integral role in the development of computer vision and applications that require image recognition. It is also used in the development of AI (Artificial Intelligence) and Machine Learning (ML) models. Image annotation involves recognizing objects, extracting information about the object, describing the object, and interpreting results from images or videos. Computer vision is used in various sectors, and self-driving cars, security, and medical imagery are a few examples.
For supervised ML models to work effectively, the data has to be labeled carefully with precision. As discussed at the start of this blog, image annotation is a subtype of data labeling. Here, the labeling process aims only at visual data such as images and videos. So, image annotation requires manual effort periodically. An engineer or data scientist finds the labels, also known as tags, and sends this image-specific information to the computer vision model that is being trained. This is how image annotation works! This process is something much similar to how we, humans, understand things in this world.
Significance of image annotation
Artificial intelligence and computer vision are growing at an incredible pace and are transforming the way industries operate. Healthcare, automotive, marketing, and other such industries are getting benefited from the use of computer vision.
Process of annotating images
In order to annotate images, you need a tool to label the visual data. You can find a multitude of software in the market that are available free of cost. Alternatively, you can also choose to utilize open-source image labeling tools that are present online. Not finding a tool that matches your business needs? No worries! There are various tools that are modifiable, meaning you can tailor them to match the needs and requirements of your business.
Developing and customizing your own image annotation tools can be an excellent alternative to purchasing software. Ensure you have resources and the latest technologies in place to design and develop a new tool. However, this would be much more time-consuming, require tremendous manual effort, and a huge capital.
Conclusion
Many industries of today are quickly advancing by utilizing Artificial Intelligence, Machine Learning, and data labeling. Especially, image labeling is helping healthcare, automotive, and other sectors. To label images effectively, you can make use of software that is readily available in the market. But, if you think the templates that are available do not fit your requirements and you are equipped with the resources, then you can develop a tool on your own. Companies are also outsourcing image annotation tasks to specialized agencies as it has to be one with utmost accuracy and precision. Agencies like Springbord can handle the data carefully and process them without errors. Want to see your business prosper? Talk to us today to know how our services can help leverage your business.
