Bounding boxes are defined as the coordinates that enclose an object of interest in an image. In the context of data annotation for AI and machine learning, bounding boxes are used to annotate objects in an image so that algorithms can learn to identify them.

Yes, it’s true! This is because bounding boxes can be used to annotate a wide variety of objects, including people, animals, vehicles, and more. Additionally, bounding boxes are relatively easy to create and can be done with a variety of different software tools.

Bounding boxes are important in image annotation for AI and Machine Learning because they allow for precise localization of objects in an image. This is especially important when training algorithms to detect and recognize objects, as it allows for more accurate results.

There are many different use cases for bounding box annotations, but some of the most common ones include object detection and tracking, facial recognition, and text recognition.

Object detection and tracking is often used in security applications, such as surveillance cameras, to detect and track objects of interest. Facial recognition is used in a variety of applications, such as security and law enforcement, to identify individuals. Text recognition is used in many different fields, such as optical character recognition (OCR), to recognize and process text.

There are a few different ways to annotate images with bounding boxes. One way is to use a computer program that will automatically generate bounding boxes around objects in an image. Another way is to manually annotate images by drawing bounding boxes around objects of interest.

For manual bounding box annotation, to add annotations, hover over an object and draw a box. The Bounding Box column on the right will display the box. The label will now be displayed on the image. Next, you must repeat this process for all objects in the image. Once you are done, export all annotations using Export Labels. Select the output format you prefer and click "Export".

We are constantly expanding our team of skilled data annotators in order to keep up with the increasing demand for our services. However, the quality process in Kotwel is very strict, but that is what ensures that the data annotated is of the highest quality, so we're always on the lookout for detail-oriented and motivated individuals who can meet our high standards.

If you are interested in joining our team, please submit your application via our Job Portal.

We are sure that you will find Kotwel to be a rewarding and fulfilling place to work.

It depends, as the level of experience required for a data annotator position will vary depending on the specific job requirements. However, in general, it is possible to work as a data annotator with no prior experience, as long as you have the required skillset and are able to learn new concepts quickly.