Top 5 Data Labelling Tools For Machine Learning [2021]- Best Data Labelling Tools

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Overview Of Data Labelling:

1. Labelling training data acts as the first step in the machine learning development cycle under Computer Vision. On completion of labelling, the dataset can be exported and used in further machine learning development.

2. Consider we need to train a machine learning model to identify a specified category of objects from the collection of data.

3. 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.

4. Labelling typically takes a set of unlabelled data and augments each piece of it with informative tags.

Why Data Labelling Is Important For Machine Learning?

Classify: The solution allows you to categorize any dataset (image/videos) along with tagging to customizable classification.

Segment: Make faster and accurate image segmentation to support specific use case including instances, custom attributes, etc.

Detect: Automate identification process using tools such as box, polygon, point and line to construct a predictable pattern of high-quality training data which trains ML-powered computer vision system on finding or identifying objects in image and video data.

According to Grand View Research, the global data collection and labelling market size was valued at USD 1,307.7 million in 2020. It is expected to expand at a compound annual growth rate (CAGR) of 25.6% from 2021 to 2028.

Top 5 Data Labelling Tools For Machine Learning:

A) SAIVI

1. Data drives digital initiatives and enterprise data has a typical journey all the way from sourcing to intelligent visualization.

2. A-Data journey comprises four stages: Data Scraping, Data Labelling, Leveraging Machine Learning & Artificial Intelligence, and Data Visualization.

3. Saivi is a comprehensive offering that covers end to end data related services — all the way from sourcing to visualization!

4. Saivi offer custom solutions in each of these phases that will accelerate your digital journey and realize the power of new oil (Data).

B) CVAT

1. Computer Vision Annotation Tool is a free, open-source, web-based image and video annotation tool that is used for labelling data for computer vision algorithms.

2. It was developed for use by a professional data annotation team, with a user interface optimized for computer vision annotation tasks.

3. It supports the primary tasks of supervised machine learning: object detection, image classification, and image segmentation. CVAT allows users to annotate data for each of these cases.

C) Super Annotate

1. Super Annotate provides a complete solution for your computer vision products.

2. It is a platform to build and scale your annotation and computer vision projects of all sizes using the smartest and most intuitive tools.

3. It is a Streamlined collaboration and quality management tool.

AI-powered image and video annotation software cover a multitude of use cases across many industries.

D) Dataturks

1. ML data annotations made it super easy for teams using Dataturks.

2. They have partnered with 1000s of companies from all over the world, having the most experienced ML annotation teams.

3. Dataturks provides a power-packed tool to do object tracking in videos.

It allows you to work with private links to images and text files. Instead of uploading raw data files, you can choose to upload a text file containing URLs to your images or text files.

E) Playment

1. Playment is a complete data labelling platform that helps machine learning engineers build high quality ground truth datasets for training and validating machine learning models.

2. It breaks down large problems into micro-tasks and distributes them among its large community of trained annotators.

3. Annotators can browse through existing tasks and complete them in exchange for points, the points can be further exchanged for vouchers on online e-commerce sites such as Flipkart, Amazon(India) and Paytm.