In deep learning, bounding boxes are one of the most commonly used image annotation techniques. This approach will save resources and improve annotation performance as opposed to other image processing approaches. Annotators would be told to draw bounding boxes around entities like vehicles, pedestrians and cyclists within traffic images.

Building a 3D representation of the world from 2D images is one of the major concerns in Computer Vision and that is why we are here to help the industries with. Cuboid Annotation is the task of labelling objects in 2D images with cuboids. The 3D cuboids help to determine the depth of the targeted objects such as vehicles, humans, buildings etc.

In the era of data annotation, precision is the crucial aspect for the accurate results of your autonomous machine. Several types of data annotation can be applied as per the case. However, polygon annotation is the best way to ensure pixel-perfect precision. But it is imperative to have the right tools and trained staff for accurate training datasets.

This is used to recognize basic points of interest within an image. Such points are referred to as landmarks or key points. Landmarking is important in face recognition. Landmark annotation is used to detect small objects and shape variations by creating dots across the image. This type of annotation is useful for detecting facial features, facial expressions, emotions, human body parts and poses.

Semantic Segmentation is the process of further classifying the pixels of the same object or label. With the help of our well-versed team, we provide an impeccable experience in terms of semantic Segmentation and labeling multiple classifications of an image along with pixel-wise annotation. We strive to deliver the most efficient and optimum result before the promised deadline at a very reasonable cost.