Data Annotation Service

As mentioned before, data labeling tasks are accomplished by humans manually. Unsupervised learning or semi supervised learning are machine learning approaches that do not rely on labeled data. However, they are not the best performing solutions for most current machine learning applications. For more, feel free to read our more detailed explanation.

There are 4 common resources for data labelling. Companies can rely on a combination of these resources for their data labeling needs.

• Full/Part-Time Employees
• Managed Workers
• Contractors
• Crowdsourcing

Feel free to explore the pros and cons of each approach

Data labeling service companies provide data annotation services for machine learning. They achieve this by using pre-trained machine learning models and human-powered skills to label (i.e. annotate) an image, text, video or audio.

Data labeling is used in machine learning model training.

To enable machine learning, data labeling tasks are completed by humans who manually label and classify objects. There are different types of labeling. Below are the most common ones for videos and images:

• Semantic segmentation is the process of labeling each pixel in an image to a class. Autonomous vehicles, robot vision and medical applications are common areas for semantic segmentation.
• Polygon Annotation detects irregular shapes and uneven shaped objects by creating shapes and outlines with an arbitrary number of sides on image data. Annotators draw lines by placing dots around the outer edge of the object they want to classify.
• Bounding Box: Annotators are given an image and are tasked with drawing a box around objects for in-depth recognition of objects in the image data. The most common usage of bounding box annotation type is autonomous vehicles. Entities such as vehicles, pedestrians, traffic lights are identified by bounding boxes so that vehicles can distinguish these entities. Image tagging for e-commerce, retail and damage detection for insurance companies are other application areas for the bounding box method.
• 3D Cuboids: Cuboids are similar to bounding boxes with one difference. An annotator illustrates the length and width of the object as in the bounding box method. However, 3D Cuboid method adds one more dimension, which is the depth of the object.
• Lines and Splines: Annotators draw lines along the boundaries such as lane separators on the road. It is also used to train warehouse robots so that robots can accurately place boxes in a row.
• Landmark Annotation : Annotator labels key points at specified locations. It is generally used for facial recognition applications and counting applications. It helps to understand the movement trajectory of each point motion in the targeted object.

Technologies such as Internet of Things (IoT), robotics and predictive analytics all rely on Machine Learning (ML) and Artificial Intelligence (AI). Modern machine learning approaches rely on labeled/annotated data and data annotation companies create labeled data.

Raising interest on autonomous vehicles is another reason why data annotation services are growing in importance. The annotated data allow autonomous vehicle computer models to recognize objects.

Feel free to read more here