Annotation of data: what is it and how does it work?
In machine learning, data annotation refers to the process of labeling data to demonstrate the outcomes you want your machine learning model to predict. Labelling, tagging, transcribing, and processing a dataset means annotating it with the characteristics you want your machine learning system to learn to recognize.
What accurately is a data annotation tool?
Annotating commercial-grade machine learning training data is a problem that can be solved with the help of a data annotation tool. It may be hosted in a cloud, on-premise, or inside a container. Important characteristics of data annotation programs include the ability to annotate various data formats (such as text, images, videos, audio, time series, and sensor data). They are compatible with a wide variety of formats and annotation methods, including 2D, 3D, video, audio, transcription, and text.
A data annotation tool’s essential features.
- Management of datasets:
Learning the ins and outs of the dataset you plan to annotate is the first and last step in the annotation process. Before committing to a tool, make sure it can import and support a large amount of data and file formats you’ll need to label. This technique can be used to perform a variety of operations on datasets, including searching, filtering, sorting, duplicating, and merging.
- The Quality of Data:
The efficacy of your machine learning and AI models is directly proportional to the quality of your data. Tools for annotating data can streamline quality assurance (QA) and verification procedures. The ideal annotation tool will incorporate quality assurance steps into the workflow.
- Data safety:
Whether you’re working with PHI (protected personal information) or your valuable intellectual property, you’ll want to ensure the safety of your annotations. Tools should limit an annotator’s ability to download data and view data that isn’t theirs.
The Big Three Free Annotation Tools for Data
- The Isahit Lab:
Isahit is an AI and data processing platform that employs transparent and moral data labeling. Their no-cost annotating tool, Isahit Lab, is quick, comprehensive, and simple to use. There’s a straightforward step-by-step guide, a comprehensive settings panel, a sleek and contemporary user interface, and the option to quickly and easily invite teams and users.
It’s a standalone web app, so you can use it with any browser you like.
For machine learning tasks, you can turn to DataTurks, a startup that facilitates the tagging of data forms like images, text, and video. It’s a set of tools and procedures that streamlines collaboration on massive teams to generate high-quality datasets.
- Tool for Annotating Computer Vision (CVAT):
Annotating images and videos for use in computer vision algorithms, CVAT is a free, open-source, web-based tool.
Interpolation of shapes between keyframes, shortcuts for the most important activities, and a dashboard with a list of annotation projects and tasks are just some of its features. CVAT helps with the main supervised machine learning jobs. It is possible to perform tasks such as object recognition, image classification, and image segmentation using visual data.
Annotation Tools: 3 Paid Tools
You can enhance your training data iteration loop with the help of Labelbox’s training data platform. It is based on three main features: the capacity to annotate data, evaluate model health, and set priorities in light of results. Labelbox is cutting-edge labeling automation that will help you save 50-80% on annotation costs, iterate 3x faster on your AI data to build more performant models, and collaborate more effectively with data scientists, labelers, and domain experts.
V7 is an automated annotation platform that handles labeling tasks mechanically by combining dataset management, image/video annotation, and autoML model training. Image, video, medical data, microscopy images, PDF, and document processing are just some of the types of data that can be stored, managed, annotated, and automated in V7.
Isahit automates AI training with a platform that generates and manages high-quality AI training data at scale, and it does so ethically and transparently. They have a competent team that keeps a human involved at all times for optimal performance.
You and your data team will need to adjust your workflow, quality assurance procedures, and other aspects of your data work based on the sophistication and features of your data annotation tool.
Time and effort will be wasted figuring out workarounds for functionality that should be built into a tool that doesn’t cater to your team or your procedures. Whether you’re an experienced pro or just getting started with machine learning, Springbord has the speed, automation, and ease of use you need for your annotation projects.