Image Annotation

The best machine learning capabilities is produced through high-quality picture annotation, which creates ground truth datasets. For picture annotation, there are many different kinds of deep learning applications across several sectors, such as autonomous technology & transportation, medical AI, business, GIS, finance, government, and more.

Semantic Segmentation

Every pixel must be connected to a labelled picture, such as a car, road, pedestrian, etc., in semantic segmentation. This technology is commonly utilised to give an overall understanding of how the visual system functions and what its limitations are in the automotive, healthcare, agriculture, retail, and many other industries.

Benefits of Semantic Segmentation

• Boosts algorithmic effectiveness
• Eliminates backdrop, which improves accuracy

Tagging

Labeling the individuals and objects in a picture is referred to as tagging. These industries—retail, e-commerce, marketing, and advertising—all often employ this type of picture annotation.

Tagging Advantages:

• Quickly sort and organise the photographs.
• Easily locate a certain image using keywords.

3D Cuboid annotation

Regular 2D pictures and movies are converted into 3D models using 3D Cuboid annotation by adding different aspects like height, width, rotation, depth, etc. In the automotive, retail and e-commerce, healthcare, agricultural, and real estate industries, this enables the computer to comprehend the precise proportions of an object.

Benefits of Cuboids:

• Measures the separation between various items to determine their size and environment
• Assists in analysing the traffic condition in the automobile sector

Polygon Annotation

Our polygon annotation tool will apply segmentation masks if you come across the precise shape of an irregular item to obtain the most accurate picture annotation. The automobile, real estate, information technology, and healthcare sectors all use polygons.

Polygon Advantages:

• Guarantees accurate localization
• Determines the precise dimensions of the items.

Landmarks

Marking important locations in a predetermined region allows one to annotate all of the different landmarks that are there. It was created to make it easier to get accurate human forms and postures in 2D photos and movies. Numerous industries, including healthcare, automobile, insurance, agriculture, retail, and e-commerce, might benefit from landmark annotation.

Landmark Advantages:

• Can identify facial features, emotions, and gestures

2D Bounding Boxes

Bounding boxes are used to place a box around an object in an image that you want the computer to recognize. It is applied in the fields of real estate, computer technology, retail and e-commerce, insurance, automobile, and agricultural.

Benefits of 2D Bounding Boxes:

• It guarantees the quickest annotating time and costs the least.

Image Masking

A mask picture representation where pixel values carry ground truth information can be helpful for many applications that handle annotated data, such as training machine learning algorithms. The image is divided into layers and masks using Adobe Photoshop or a comparable programmed for this approach. Two methods are often used to create such images:

• From an area determined by user-specified coordinates, create a mask image.
• Create mask images using annotations found in annotations for regions of interest. This entails making a separate picture for each of these regions of interest and mapping annotations to them.