Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags. Labeled data is significantly more expensive to obtain than the raw unlabeled data.
What Is Labeled Data?
After obtaining a labeled dataset, machine learning models can be applied to the data so that new unlabeled data can be presented to the model and a likely label can be guessed or predicted for that piece of unlabeled data.
The global data collection and labeling market size are expected to reach USD 8.22 billion by 2028, according to a new report by Grand View Research, Inc.
Data Labeling – 6 Best Tools
1) Data loop is a one-stop-shop for building and deploying powerful computer vision pipelines – data labeling, automating data ops, customizing production pipelines, and weaving the human-in-the-loop for data validation.
2.This platform streamlines the process of preparing visual data for machine learning.
3. This platform also eliminates data challenges for companies, allowing them to focus their resources on their core business.
2) A Data journey comprises four stages: Scraping or Aggregating Data, Data Labeling, Leveraging Machine Learning & Artificial Intelligence, and Data Visualization.
3) Saivi offers custom solutions in each of these phases that will accelerate your digital journey and realize the power of new oil (Data).
4) Embark the Journey of Data! Scrape-Annotate-Intelligence-Visualization.
2) It supports the primary tasks of supervised machine learning: object detection, image classification, and image segmentation. It allows users to annotate data for each of these cases.
3) It has many powerful features, including interpolation of shapes between keyframes, semi-automatic annotation using Deep Learning models, shortcuts for most critical actions, a dashboard with a list of annotation projects and tasks, LDAP, and basic access authentication, etc.
2) Scale annotation and computer vision projects of all sizes using the smartest tools, robust data management systems, and best-in-class outsourced services.
3) Scale your image and video annotation projects with advanced and easy-to-use tools to address even your most sophisticated annotation needs.
2) Their API allows you to seamlessly integrate the labeled dataset in your ML training or evaluation flow.
3) One can use this tool continuously to track the model performance by uploading model outputs and have the labeling team validate it and generate week-on-week accuracy numbers.
2) Label box is a training data platform built with three core layers that let you orchestrate the entire process from labeling and collaboration to iteration.
3) It is the simplest platform to train and operate machine intelligence. It is one of the fastest ways to annotate data to build and ship artificial intelligence applications.