Data annotation is the art of labeling images, audio, video, and text data that is mainly used in supervised machine learning to train the datasets of a model. This helps a machine to understand the input data and act accordingly as an output. Simply put, there are multiple types of annotations, some of them are – bounding boxes, semantic segmentation, polygon annotation, polyline annotation, key points, 3D point cloud annotations, etc.
With the technological advancements in machine learning algorithms, computer vision, and NLP, the tech industry has greatly evolved and created wonders around the world of Artificial Intelligence (AI). Along with this Machine Learning (ML) has also grown. This has helped many industries to adopt Artificial Intelligence smoothly and make efficient use of this technology in various use cases.
There are multiple tools that are readily available for annotation of data that can be utilized. Data annotation tools assist in substantial improvement of the training input data used by Artificial Intelligence models, enabling them to address and solve complex data problems. Many of these platforms offer end-to-end machine learning services from data uploading, pre-processing, cleaning of data, its analysis/visualization, to distribution, production, and re-engineering to sum up a few.
The data annotation tools market is observing profound growth as companies are rapidly deploying these Annotation tools for labeling large volumes of Artificial Intelligence training data accurately.
Data labeling tools are enabling autonomous vehicle manufacturers in developing smart applications for vehicle to vehicle communication (V2X) and connected car technologies such as Natural Language Processing (NLP) and speech recognition. Data annotation tool providers are focusing on developing unique and specialized techniques for enabling the automation of 2D and 3D annotation for LiDAR and sensor data.
The manual annotation division held a majority of the market share, accounting for over 75% in 2019 due to the surging adoption of data tools to ensure high-quality input data. Data that is labeled manually is less prone to errors due to the involvement of highly trained experts, who can handle complex data labeling situations, where machine-based algorithms would perform poorly. Medical image labeling requires the expertise of specialist medical professionals in cases where the machine learning systems cannot accurately label data.
Data Annotation for Machine Learning
Data Annotation is the process of attributing labels to datasets that are used for the training of machines. About 80% of Artificial Intelligence project development time is spent on the preparation of this data. The success of any AI or Machine Learning model is directly proportional and linked to the quality of the annotated data nurtured to the algorithms for training them.
Data Annotation for Supervised & Unsupervised Machine Learning Algorithms
Data Annotation plays a very important role in the training of the machine learning algorithms more so in the case of supervised machine learning projects. Annotated data helps the machines to understand their surroundings effectively and identify the objects in their vicinity and surroundings.
When it comes to unsupervised machine learning projects, you would need annotated data sooner or later to improve the performance of your ML algorithms. Human data annotation plays a key role to increase the accuracy of an unsupervised ML algorithm that learns on its own by connecting the dots. In such cases, human annotators can manually review each image data to determine if the quality of the annotated image is good enough for the algorithms to learn or not.
RMSI – Fulfilling Human Data Annotations Needs
Building comprehensive custom datasets for training AI/ML models
RMSI annotation specializes in building comprehensive datasets that are perfect for training your AI and ML models. Even though data annotation is a very important part of training the AI/ML undertaking, we will do the heavy weight-lifting part for you, while you focus on optimizing the AI/ML models to perfection and smooth functioning. Write to us at email@example.com for customized training datasets of AI/ML projects.
Data Annotation Growth in the Forthcoming Future
According to ResearchAndMarkets’s report, the global data annotation market was valued at US$ 695.5 million in 2019 and is projected to reach US$ 6.45 billion by 2027. The market is expected to grow at a CAGR of 32.54% from 2020 to 2027, the booming data annotation market is witnessing incredible growth in the forthcoming future.
This is not a surprising trend. The rapid growth of the data labeling industry can boil down to the rising integration of machine learning into various industries.
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