Hackernoon logoWhat's The Best Image Labeling Tool for Object Detection? by@divyesh.aegis

What's The Best Image Labeling Tool for Object Detection?

divyesh.aegis Hacker Noon profile picture


An image labeling or annotation tool is used to label the images for bounding box object detection and segmentation. It is the process of highlighting the images by humans. They have to be readable for machines. With the help of the image labeling tools, the objects in the image could be labeled for a specific purpose. The process of object labeling makes it easy for people to understand what is in the image. The labeling tool helps the people to mark the items in an image. There are several image labeling tools for object detection, and some of them use varied techniques for detection of the object, like a semantic, bounding box, key-point, cuboid, semantic and many more. In this article, we will talk about image labeling and the best image labeling tools.

The purpose of using the image labeling/annotation tool for object detection

As the name suggests, the image labeling tool is used for detecting the objects in an image. The main purpose of the tool is to allow the users to highlight or capture a specific object in a picture. The images are highlighted to make them readable for the machines. Image labeling or image annotation are specifically used for artificial intelligence and machine learning. As the tool allows the users to use the highlighted images as the training data sets. The data sets are further while feeding with a deep learning algorithm. Therefore, with the help of the image labeling tools, we can develop a functional artificial intelligence model.

Artificial Intelligence and Machine Learning for image labeling

Artificial Intelligence centric models are made using machine learning. The models are trained efficiently so that they are capable of working on their own. They do not require the intervention of humans. Several image annotation tools are used to feed a large volume of the training data. The data is needed for computer vision. As using this tool, the users can identify the objects in the images. As a result, the machines find it easier to identify the same set of images even they are used in real-life.

Building artificial intelligence tools that functions perfectly in real-world scenarios isn’t very easy. The experts would have to first collect high quality and volume of the right kind of data. Sometimes a wide range of annotated images helps artificial intelligence-based tool to identify patterns which eventually help it understand. Therefore, AI-based tools can understand how humans look like. Shortly, we may expect artificial intelligence to become smarter and smarter. The tools would be able to draw boxes around pedestrians without any human intervention.

Listed below are a few of the top image labeling tools for object detection :

  • LabelMe

LabelMe is one of the most popularly used image annotation tools. Written in JavaScript, it is an exceptionally brilliant annotation tool. The tool is used specifically for online image labeling. There are several benefits of using this tool, one of them is that it is much more advanced. It has the latest features. The users would be able to access the tool from anywhere. You would be able to label the objects without installing a huge database. LabelMe assists the users in building image databases, which are specifically for computer vision research. LabelMe is not only available in the desktop, but there is also an application that can be used as well. It contains 2 galleries, Labels, and Detectors. They exhibit the functionality of the tools. The galleries are used for a variety of purposes, like storage of the image, labeling, storage, etc.

  • Imglab

Imglab is one of the other image detection tools. It is a web-based tool. The tool is used to label images for objects. Thus, the tool is mostly used by people to train dlib. Also, at times it is used to train object detectors for ML purposes. Also, the company has evolved the tool. Therefore, the latest version of imglab is adopted by many users. It is platform-independent. Therefore, you can run the tool directly from the browser. Also, you won’t need any prerequisites. Additionally, you would not need any high memory space or the CPU to use them too.

  • Semantic Segmentation Editor

Semantic Segmentation Editor specifically supports the annotation of bitmaps. Also, the labeling of the point clouds. It is one of the most renowned web-based labeling tools. Mostly, people use this tool to create artificial intelligence training data sets. Therefore, it is used for 2D and 3D. An editor is a fantastic option for autonomous driving research. Also, it supports .jpg as well as .png images. It is an application, which is quite easy to use.

  • BeaverDam

BeaverDam is one of the most popular video annotation tools. The tool is used for computer vision training labeling. It is a superb tool that is used by engineers across the globe. It runs as a local Python Django server. Also, it can be easily integrated with mturk. Although, you may have to study the use of this tool. Especially, when it comes to downloading the annotations, you would have to research about the tool. The tool will make it super easy for the people to label the videos, however, it is just that you would have to learn to use it efficiently.

Image labeling and deep learning

Deep learning, artificial intelligence, and artificial intelligence have a connection too. Image annotation for deep learning is specifically required for image detection. Also, it leads to more precision and clarity. Mostly, things that are used for image annotation are Semantic Segmentation as well as 3D Cuboid Annotation. Therefore, we can expect a lot in the future in regards to deep leaning and image labeling.

There is a wide range of image labeling tool for object detection, however, choosing the best is very important. Therefore, make sure you research thoroughly before selecting the top tool.


Join Hacker Noon

Create your free account to unlock your custom reading experience.