- Labeling training data acts as the first step in the machine learning development cycle under Computer Vision.
- To train a machine learning model to identify a specified category of objects from the collection of data, we would need to collect representation data samples which have to be classified and analysed along with a Machine Learning algorithm for handling each sample.
- The key aspect to make Artificial Intelligence / Machine Learning models work is to have a properly organized and precisely labeled data.
- DLS is used to generate accurate and high-quality labels using AI and ML models based on data collection.
Data Annotation – Different Types
The 4 different types of annotation are,
- Text Labeling
- Image Labeling
- Audio Labeling
- Video Labeling
Data Annotation – Market Trends
Data Annotation Tools Market size is set to surpass USD 7 billion by 2027, according to a new research report by Global Market Insights, Inc.
Data Annotation – 3 Step Process
- As an initial step, Machine Learning engineers shall start annotating items in the dataset according to the instructions provided.
- On completion of labeling, the dataset can be exported and used in further machine learning development.
- The labeling services shall replace the manual annotation process with automation via a user-friendly interface that eases the annotation and parameter defining process.