AI Image Annotation Services

For Computer Vision to Detect Images like Humans

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image annotation services
15+

Years of Experience

1500+

Annotators Working 24x7

100%

Data Security

99%

Accuracy Achieved

24X7

Availability

Three Key Features of Our Image Annotation Services

#1

We Excel in Annotating Images for Machine Learning

We annotate all kinds and sizes of images using our tools to make them recognizable to machines. We offer training data solutions for machine learning in varied fields.

#2

We Offer Accurate Imaging Tagging and Labeling Services

Our annotation services involve the use of deep learning to help computers and machines in readily recognizing objects via dimension and outlined boxes. In involves supplying it with data for future reference whilst recognizing similar objects.

#3

We Outsource Annotation Services at the Right Price

Our experts can offer you satisfactory results using methods like bounding boxes which depend on object type and usability to offer you satisfactory results.

Image Annotation Tools

2D Bounding Boxes

2D Bounding Boxes

It is used to define objects in a two-dimensional space through graphical representations. It is generally used in computer vision and machine learning applications for classifying objects in pictures and videos.

3D Cuboid Annotation

This technique is used for detecting and recognizing 3D objects within images. It assists machines in figuring out the depth of objects including vehicles, people, buildings, etc.

3D Cuboid Annotation
Landmark Annotation

Landmark Annotation

Companies use this technique to determine the shape and emotion of natural objects like facial features and emotions using a sequence of points. It also helps in building accurate datasets for determining the shape of items of different sizes. It also permits computers to detect the smaller ones or target points therein.

Skeletal Annotation

It is used for highlighting body movement and alignment. It is used for identifying and annotating key skeletal features in images for producing large datasets to train medical diagnostics for AI models.

>Skeletal Annotation
Polygon Annotation

Polygon Annotation

It is used to draw precise contours in polygon shapes over the products for identifying and localizing videos and images. This technique is great for annotating complicated and non-linear shapes and objects for image segmentation or object detection tasks.

Semantic Segmentation

This technique is used for segmenting images in computer vision. An image dataset is semantically segmented to locate all categories and classes.

Semantic Segmentation
3D Point Cloud Annotation

3D Point Cloud Annotation

It provides an opportunity to detect and classify objects in greater detail so that dimensions can be accurately measured. One can readily match 3D Point Cloud data to camera images for labeling various kinds of objects.

Polyline Annotation

Machine learning algorithms, the driving force behind autonmous vehicles can interpret and navigate their environment due to their training with annotated video and image data. It is used in autonomous cars and other vehicles for detecting lanes on roads and move accordingly.

Polyline Annotation

Use Cases for Image Annotation

Robotics
Robotics

3D object detection is widely used in robotics to avoid collisions with dynamic objects like humans, animals and other characters.

Self-Driving
Self-Driving

Bounding boxes are used to annotate everything around a vehicle for detecting objects such as pedestrians, vehicles, traffic signs, and barriers.

Healthcare
Healthcare

Embedding annotations & appropriate labels in AI is a major part of discovering links between genetic codes, powering surgical robots, and optimizing healthcare processes & productivity.

Retail
Retail

Appropriately performed image annotation & data labeling can play a crucial role in AI implementation while also helping retailers to enhance the customer’s shopping experience.

Autonomous Flying
Autonomous Flying

AI implementations enabling automated or assisted flight can be made easier and more accessible through image annotation performed at the backend with autonomous flying training data.

Agriculture
Agriculture

IoT sensors and bounding box annotations can provide real-time data for AI algorithms to contribute to agricultural efficiency and yield improvement with real-time insights from their fields.

Frequently Asked Questions

Anolytics is a leading image annotation company known for delivering quality training data that has helped AI innovators nullify data challenges in their machine learning operations. We keep everything in-house to ensure that our client’s data is safe and secure. Outsourcing AI training data development operations to Anolytics can be a way to a successful AI model with purpose-specific data.

Anolytics has thousands of successful accomplishments of annotation projects to its credit since it stepped into the AI and machine learning space as a data annotation and labeling company. experts here boast sound knowledge of AI training data and how it works for machine learning models.

Our annotation approach has always been tool and platform-agnostic, which enabled us to trigger automation in business and industrial work processes through functional AI-driven systems, applications, and machines.

A skilled workforce is required to annotate the images if you are working with a lot of data. You can use commercial, open-source, or freeware data annotation tools. Streams, frames, and images can be annotated using tools that provide feature sets with a variety of capabilities, so your workforce can annotate frames or streams of images, multi-frame images, or videos in an efficient manner. It is possible to scale an image annotation process using crowdsourced or professionally-managed team solutions, depending on whether you do it internally or through contractors.

The most common use of image annotation is to identify objects, boundaries, and segments in an image and classify the objects, segments, and characteristics contained in an image with the appropriate marking of boxes and labels.

1. Bounding Box: This technique is used for drawing a box around relatively symmetrical objects, such as cars, pedestrians, and road signs. It is used when the shapes of objects are not as important as occlusion and when occlusion is not a problem. Two-dimensional bounding boxes are known as two-dimensional bounding boxes, and three-dimensional bounding boxes are known as three-dimensional bounding boxes.

2. Landmarking: This is used for analyzing body positioning and alignment, facial expressions and emotions, and plot data characteristics. For example, when annotating images for sports analytics, it is possible to determine how baseball pitchers’ elbows, hands, and wrists are oriented.

3. Masking: This is used for image masking. It can be used to hide certain areas of an image like a house, a land area, or a plant and reveal other areas of interest making it much easier to focus on certain parts of the image.

4. Polygon: It is used to mark the highest points (vertices) of images by annotating the edges of an object with a more irregular shape. You can mark each of its highest points (vertices) and annotate its edges.

5. Polyline: It involves a continuous line made up of one or more segments plotted over a wide area. Linear structures are defined in images and videos by connecting small lines at the vertex of the shape. Annotators use annotation platforms and labeling tools to apply these lines to images.

6. Tracking: It can be used to label and plot an object’s movement over time in multiple video frames. Interpolation is a feature in some image annotation tools, which allows annotators to label a frame, then jump to a later frame, shifting the annotation to the new position at a later time in the image.

7. Transcription: Transcription is a word-for-word description of an audio recording. Generally, it refers to the process of converting an audio or video record of an important conversation into editable text.

Interested in Working with Us?

In today's tech-driven world, a career in Artificial Intelligence (AI) can be highly rewarding. Join our team of annotation specialists, and be a part of the company that creates high-quality training datasets.

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