Best Image Labeling Tools For Computer Vision
Image labeling or image annotation is the process of identifying or recognizing different units in an image. This process helps us to make images readable for computer vision. There are different types of image annotations.
Some of the most common types of image annotation for computer vision are bounding boxes, polygonal segmentation, line annotation, landmark annotation, 3D cuboids, semantic segmentation, etc.
A number of image labeling tools for computer vision are available. All the annotation platforms offer a variety of features and tools. Here is a review of some of the best labeling tools for computer vision.
Top 5 Best Image Labeling Tools for Computer Vision
1. SuperAnnotate
is an end-to-end image and video annotation platform that streamlines and automates computer vision workflows.
Backed by renowned professors Pieter Abbeel and Trevor Darrell, SuperAnnotate is helping companies build and boost their computer vision pipelines.
The tool offers both vector annotations (boxes, polygons, lines, ellipses, keypoints with templates and cuboids) and pixel-wise annotation with a brush. It provides both image and video annotations.
SuperAnnotate also provides advanced features, such as automatic predictions; active learning; transfer learning; and flexible user, quality, and data management systems which allow computer vision engineers to accelerate a given task by 5–10x.
SuperAnnotate is available with the following pricing model: Free for the first 100 images and for academic research, paid versions including the Starter package (up to 10.000 images), Pro (unlimited images), and Enterprise (unlimited, custom).
Recently, as part of the partnership with OpenCV, SuperAnnotated launched its best-in-class free annotation tool for the computer vision community.
2. VGG Image Annotation Tool
VGG Image Annotation Tool (VIA) is an open-source, easy to use and independent manual annotation software. It can be used for the annotation of image, audio and video. HTML, CSS and Javascript have been used to develop this tool.
It is a standalone software, which means it is not dependent on any external libraries. This is a very lightweight software, having a size of less than 400 KB.
This tool can run on any web browser without any installation or setup. It offers a vast number of features. It comprises a variety of tools, supporting lines, dots, circles, polygons and eclipses.
You can also add objects and there is also an option to introduce image attributes or tags. All the annotations are contained in one JavaScript Object Notation (JSON) file or a Comma Separated Value (CSV) file. You can download the annotations.
Though it does not contain any advanced features in dataset management, the interface used by this software is simple, efficient and user-friendly. Some of the commonly used features of this tool are basic image annotation, face annotation, remote image annotation, fast track annotation, video annotation and audio annotation.
It is one of the best tools for annotation of polygons, as you can view the lines of the polygon and nothing else. It also supports a large number of hotkey shortcuts. VIA is a free annotation software too.
3. Supervise.ly
Supervise.ly is one of the best web-based platforms, where you can not only access an advanced annotation interface but can also learn about the whole process of computer vision training and the various models library that can be directly improved within the platform. It uses Python SDK to import plugin for custom data formats, carry out neural network models, and to run various tools such as Data Transformation Language.
Some of the important features of Suprevise.ly are:
You can read, change and write the Supervise.ly projects on the disk.
You can develop Supervise.ly plugins so that your focus remains only on the core of your custom logic.
You can perform various functions regarding labeling data, geometric objects, and tags.
A number of tools are present, such as boxes, lines, dots, polygons, bitmap brush, etc. There is
Also an important feature of drawing holes within the polygons. You can also add images, object tags and order figures in layers. There is also a feature that helps you perform data transformation directly on the software.
The software provides a huge number of options for project management on different levels like teams, workspaces and datasets. It also offers many options for annotator management.
However, a few things are missing such as time statistics and quality control mechanisms. It also supports various customizable hotkey shortcuts. This tool charges no price for the community edition but prices are charged for self-hosted versions.
4. Labelbox
Labelbox is one of the most popular advanced data labeling tools. It was created in 2018. It offers a free community version as well as an enterprise version. The free version is limited to 5000 images.
It offers various impressive and useful features and tools. Some of the tools for annotation are boxes, polygons, points, lines, etc. It also has a superpixel coloring option — a newly added feature for semantic segmentation brush. All the annotations are saved in the form of a single JSON or CSV file.
Labelbox also comprises of an easy and user-friendly interface. There are several options for monitoring performance, quality control mechanisms, invite users and review the work of each one.
Some of the other key features of Labelbox are customizable labeling interfaces, tiled imagery support (maps), advanced labeling tools, concurrent labeling queue, private and secure data, flexible collaboration and management tools, quality assurance, fully-featured API, measure performance and export your labels.
5. Visual Object Tagging Tool (VoTT)
Visual Object Tagging Tool is an open-source annotation and labeling tool, developed by Microsoft. Different features of this tool are:
a. It allows to label images and videos.
b. It allows you to import data from various local and cloud storage providers.
c. It allows you to export data to different local and cloud storage providers.
Various advantages of using this tool are:
a. The code of this software is well written and the interface is easy to work on.
b. It has a feature of deep learning algorithms, which automatically detect the objects.
c. It is available as a web app as well as an electronic app.
Various disadvantages of using this tool are:
a. You have to host your data on Azure to use the web app of this tool. Azure is the cloud computing service of Microsoft.
b. This tool does not have a built-in API.
c. You can only draw bounding boxes and polygons but cannot label a picture. Therefore, this tool is not perfect for making a classification dataset.
d. The main objective of image annotation is to enable your computer to interpret images and videos in a more efficient way. There are a number of applications for image annotation. Some of them are face recognition, robotics, text recognition, autonomous vehicles, security system, AgTech, image retrieval, etc. Go through this image labeling tools for computer vision to get an idea about the different annotation tools.
Sources:
https://github.com/tzutalin/labelImg/blob/master/README.rst
https://github.com/microsoft/VoTT
https://github.com/supervisely/supervisely/blob/master/README.md
Website — https://superannotate.com/
Pricing — https://superannotate.com/pricing
Documentation — https://docs.superannotate.com/
Blog — https://blog.superannotate.com/
Case study -
Case study — https://blog.superannotate.com/how-altris-achieved-increase-model-accuracy-using-superannotate
