Image Annotation

Excellent picture annotation services from 24x7offshoring underpin machine learning, artificial intelligence, and data operation techniques. Picture annotation is the process of strategically identifying an image, which occasionally uses computer assistance in addition to human labor. It is a crucial stage in developing computer vision models for applications including object identification, picture segmentation, and classification. Every set of pixels in an image can be annotated, or a whole picture might be labelled with just one label. High-quality annotation is the foundation of successful computer vision-based picture annotation initiatives. The use case the project is intended for will determine the sort of annotation required.

Why 24x7offshoring for Image Annotation?

Bounding boxes, polygon annotations, key point annotation, LiDar, semantic segmentation, and image classification are just a few of the many image annotation services that 24x7offshoring offers to meet the objectives of a client’s project. As you iterate, the 24x7offshoring team collaborates with the client to calibrate the job’s quality and throughput and give the optimal cost-quality ratio. Before launching complete batches, we advise running a trial batch to clarify instructions, edge situations, and approximate work timeframes.

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


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.


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.
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With great features comes great success.

Prioritise Quality & Security

We give top-notch services to our clients and a dedicated FTP


We handle difficult projects with ease and are quite conscientious about meeting our deadlines.

Market Experience

Large international organizations are among our oldest and most renowned clients

CSAT: 98.7

What they say?

Yang Fang Project Manager at Alibaba

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    Image classification, at its very core, is the task of assigning a label to an image from a predefined set of categories. Practically, this means that our task is to analyze an input image and return a label that categorizes the image.
    The applications include automated image organization, stock photography and video websites, visual search for improved product discoverability, large visual databases, image and face recognition on social networks, and many more; which is why, we need classifiers to achieve maximum possible accuracy.
    Two popular algorithms used for unsupervised image classification are ‘K-mean’ and ‘ISODATA.’ K-means is an unsupervised classification algorithm that groups objects into k groups based on their characteristics. It is also called “clusterization.”
    The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorithm the software will use and the desired number of output classes but otherwise does not aid in the classification process.