Image Annotation

Keymakr provides accurate AI training datasets for enterprises in different industries. Our in-house teams and studios enable us to offer data creation, generation, and annotation services for projects of any scope and complexity.

Image Annotation
Types

We use a number of different techniques when applying information to AI training images. We can create training data that reflects the diversity of the real world by using these different options:

01. Bounding Box

The “bread and butter” of image annotation, bounding boxes are the most common way of annotating objects. You simply place them over what you need and label it.

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02. ROTATED BOUNDING BOXES

A type of bounding box that we place at an angle to more accurately portray the position of our object in the real world. This helps understand positioning, orientation, and facing.

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03. CUBOID ANNOTATION

We use this method to extrapolate 3D space on 2D images. They give us a more nuanced understanding by adding depth and height to our object’s parameters.

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04. POLYGON ANNOTATION

This method helps us precisely define shapes by connecting small lines to define our object. It’s used for non-standard objects and added accuracy.

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05. SEMANTIC SEGMENTATION

We separate and classify everything in an image along with labeling backgrounds and assigning color to objects for easier comprehension. This helps AI understand everything happening in a scene.

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06. SKELETAL ANNOTATION

We attach connected lines to mark limbs or other parts of our object - usually to mark the position of a human or an animal and understand their exact pose and shape.

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07. KEY POINTS

We mark individual points on an image and give them a separate label in a granular way. For example, marking specific facial features for our model to understand human faces or emotions.

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08. LANE ANNOTATION

We trace lanes across an image to specify linear structures. This method helps us mark roads, pipelines, tracks, and other parts of infrastructure - often used in logistics and autonomous driving.

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09. INSTANCE SEGMENTATION

Further improves classification by assigning a separate label and color to every individual instance of our object. It exists to highlight object properties and add precise differentiation.

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10. BITMASK ANNOTATION

More of an assistance tool than its own method, this helps us mark separate parts of an image as the same object - for example, if we are dealing with a pitch that’s separated by something else in the foreground.

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11. CUSTOM ANNOTATION

We combine different annotation techniques on the same image to achieve the best result. This helps get the best possible precision tailored to the specific needs of your model.

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12. 3D POINT CLOUD

Usually applied to LiDAR and RADAR data, this is one of the most complex methods that helps us mark real-world relationships between objects in a 3D space. Often used in the aerial and autonomous vehicle industries.

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13. Automatic Annotation

We use machine learning to automatically detect objects and annotate them for us. This is only possible with advanced tools such as Keylabs and requires human supervision and quality control.

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Image annotation types

Bounding box annotation

Bounding boxes are one of the most commonly used and recognizable tools when it comes to image processing for image and video annotation projects.

Polygon annotation

Polygon annotation is the go-to when it comes to irregular and complex shapes. It captures more lines and more angles than a regular bounding box.

Semantic segmentation

Semantic segmentation treats multiple objects of a particular category as one collective entity. Objects shown in an image are grouped together based on defined categories.

Skeletal annotation

Reveals body position and alignment. These are commonly used in sports analytics and security applications.

Key points annotation

Identify and mark the key points of any object. For example, eyes, nose, and lips on a human face.

Lane annotation

Most commonly used for annotating roads, pipelines and rails.

Instance segmentation

Instance segmentation detects the instances of each category. It identifies individual objects within these categories. Categories like “vehicles” are split into “cars,” “motorcycles,” “buses,” and so on.

Custom annotation

If your project requires a specific combination of annotation types, we can easily do that for you. Our innovative and experienced R&D team can evaluate your project and create a completely new annotation type based on your specific requirements.

Image labeling

Let us handle your image labeling needs. Our team of in-house professional annotators can work with large amounts of data without sacrificing speed or quality. Our workforce and our proprietary annotation tools form a winning combination for any project. We can assure you that no matter how big your project is, we will have all the necessary resources and solutions to facilitate your work.

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Professional Data Annotation
For Any industry!

01. IMAGE ANNOTATION WITH BOUNDING BOXES

Bounding boxes are rectangles used by annotators to frame objects in images so that they can be located by machine learning models.

Bounding boxes are used to define objects across a variety of industries, for example identifying cars and trucks in images of traffic for autonomous vehicles.

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02. IMAGE ANNOTATION WITH POLYGONS

Polygon annotation adds additional granular detail to object annotation. Specific points are created around the outline of an object that are then connected with vertices. This allows annotations to capture complex and irregular shapes in images.

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03. SEMANTIC SEGMENTATION

Semantic segmentation divides images into sets of pixels that correspond to real-world categories. Using polygon lines annotators split images into classes, such as: road, sidewalk, building, sky.

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04. INSTANCE SEGMENTATION

Instance segmentation adds additional levels of detail to annotations. Instance segmentation defines each instance of each object appearing in a given image. In practice this means that, for example, every car in an image of traffic will be outlined and labeled.

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05. IMAGE ANNOTATION WITH POINTS

This technique allows annotators to mark key points on any image. This annotation type is commonly deployed to locate facial features like eyes, noses, and lips.

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06. IMAGE ANNOTATION WITH LINES

Line annotations are often applied to images of linear objects, such as: roads, pipelines, and electrical wires.

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07. SKELETAL ANNOTATION ON IMAGES

This technique draws lines between points of articulation on the human body. Images annotated in this manner are used to indicate body position and alignment.

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Industries
We Serve

Keymakr has extensive experience providing affordable data tailored to different industries. Find what works best for you:

Automotive • Aerial • Retail • Robotics • Sports • Agriculture • Agriculture • Security • Medical & Healthcare • Livestock management • Waste management • Document annotation • Construction • Logistics

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"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...