Infosearch BPO offers image annotation services with our custom tools for machine learning purposes.
Image annotation is one of the tasks to train a computer vision model; the choice of the correct tool plays a role in the effectiveness of the data labelling process. As it has been mentioned there are tools for free image annotation and there are paid tools and each has its pros and cons. After reading this comparison you shall be in a position to determine which type of tool suits your need depending on other factors such as price, functionality, expansibility and user-friendliness.
Free Image Annotation Tools
Free image annotation tools are those which are mostly open-sourced
basic tools and services are free and can be accessed without any cost. It is
recommended for startups and independent projects as well as situations when
a tight budget is the key limiting factor.
a. If we look at the working of free image annotation tools then we will come to know about more advantages that as follows:
- Cost-Effective: The first advantage is that they are
available for free; therefore, one can see viability for a small business, a
student, or even a researcher.
- Open-Source Customization: Some of the tools, and many of
the web services, are free, and if you are knowledgeable in programming, you
can adapt the code that underlies the tool to suit your purpose or to fix any
bugs that it may contain.
- Community Support: There are active online groups for most
of the free tools available and they include forums for support and new
releases. There is a way to share specific plugins, scripts and other resources
that may add functionality to the tool.
b. Drawbacks of free Image Annotation Tools
- Limited Features: Freeware has the least capabilities in
comparison with the shareware version of a program. Basic features such as
group work, verification, or machine learning-based annotation can be missing.
- Scalability Issues: The issues that free tools face in
mainly scaleable projects or terabytes of data include storage, lowered speed
of working or no direct compatibility with the cloud.
- User Support: Since free tools are in most cases casual projects, they do not feature professional support. There is no other
way but to find solutions from the community forums or documentation if issues
are experienced on the site.
c. Most used Free Image Annotation Tools
1. LabelImg:
- Features:
LabelImg is developed for users to add bounding box annotation to images which
is an open-source tool. The software is very simple to install and supports several operating systems.
- Use Case: This is
mostly used for small-scale projects especially when training object detection
models are being trained.
- Limitations:
LabelImg does not support features such as polygon annotation, teamwork, and user
interface for AI labelling.
2. CVAT (Computer Vision Annotation Tool):
- Features: CVAT is
a web-based, open-source image annotation tool that was designed by the
specialists of Intel. Some of the annotations it supports include box, polygon
and key points.
- Use Case:
Designed for annotating campaigns and projects that may require a higher level
of differentiation, and for projects involving small groups of users.
- Limitations: CVAT
can be more difficult the set up as an app and can require more technical
knowledge and has no customer support section in the app.
3. VGG Image Annotator (VIA):
- Features: VIA is
a lightweight and open-source annotation tool which can be used without
installation on a computer. This supports the region-based annotation and
attributes for labelling.
- Use Case: It is
well suited for small simple projects and for researchers who require easy and
quick setup.
- Limitations: It
cannot be used collaboratively and has no option for the at handlingbig data set.
Paid Image Annotation Tools
Free image annotation tools are aimed at large-scale relatively
simple tasks, while paid tools are developed for solving complex tasks, for
large scale projects and teams. They equip the software with abilities that
enable it to enhance the process of annotation making it more efficient.
a. Benefits of Paid Image Annotation Tools
- Advanced Features: Tools that are paid contain the feature
of AI labelling which reduces the time used when labelling. The additional
functions include the check of created products for compliance with certain
standards, sharing tools, and compatibility with artificial intelligence
systems.
- Scalability: Paid tools are designed for high-volume annotation tasks. They give cloud storage, collaboration in real-time, and the
possibility to organize data effectively.
- Customer Support: As for paid tools, there is a team of
customer support, who will help to solve any problems in the shortest amount of
time possible, and software-free time should not be taken into account.
b. Disadvantages of Paid Image Annotation Tool
- Cost: The biggest disadvantage in most of the cases is the
affordability of the approach and the tools for implementation. Prices can
moreover depend on the number of users, the provided extras, and accessible
restrictions in usage.
- Less Customization: The disadvantage of paid tools is that
they often offer not an open-source solution, and the level of customization is
rather low. Utility users are forced to organize themselves in a way that
complements the features of the tool being developed.
c. Most used paid image annotation methods
1. Labelbox:
- Features: For
annotation options, Labelbox provides bounding boxes, polygons, and
segmentation. It also can label, collaborate, and check the QA with the help of
artificial intelligence features.
- Use Case: Perfect
for enterprises and teams that need to optimise their work processes with
labelling conducted with AI and real-time collaboration.
- Limitations: The
cost might be high for a small team or for a single person, and it takes time
to learn how to navigate through all of the tools efficiently.
2. SuperAnnotate:
- Features: The
features that SuperAnnotate has to offer are multiple annotation tools and
auto-annotation, NLP-suggested annotations for text, as well as full project
management.
- Use Case:
Designed for teams working on complex tasks in different areas like image,
video, and text annotation for big projects.
- Limitations:
Exists through subscription, and the additional functionality may not be
employed to the maximum in a small-scale project.
3. Hive Data:
- Features: As for
the annotation services provided by Hive Data, the company provides both, manual and automatic labelling. It also offers a full range of quality
assurance activities and obligations and personnel management solutions.
- Use Case: Most
appropriate for enterprise solutions which require volume datasets that have
firstly annotated with high quality.
- Limitations:
Higher costs, which means that it will possibly be less effective for
smaller-scale use.
Choosing the Right Tool for Your Needs
- For Individuals and Small Projects: When you are a single
data scientist or researcher or a couple of people working on your personal or
relatively low-budget project, the most suitable open-source annotation tool
will be something like LabelImg or CVAT. these tools are convenient to use,
combined with low cost and afford appropriate capabilities for solving
small-scale tasks.
- For Startups and Mid-Size Companies: Some of the firms
that could consider this course of action may include startups and mid-sized
companies that require better collaboration features but cannot afford to
invest in much more complex and heavy-duty collaboration tools but would like
to use the web tools and apps’ minor tasks and few affordable paid for web
tools and apps collateral. CVAT is good for the beginning, and SuperAnnotate is
suitable for developing needs.
- For Enterprises and Large-Scale Projects: Among paid
tools, which can be effective for large-scale annotation, providing efficient
work, cooperation in real-time, and many features, the best tools are Labelbox
or Hive Data. These tools support automation, scalability, and specialized
platform help suited for big AI bass model building.
- For Projects with Complex Requirements: Projects that are
going to need a higher level of annotation including 3D cuboid annotation or
semantic segmentation and automation or efficiency improvements for labelling should go for paid solutions. Both Labelbox and SuperAnnotate can manage
multiple project complexities such as various types of annotations as well as
the quality check of all work done.
Conclusion
Free tools are suitable for those projects where costs
including the fee for bioimage analysis tools are limited while paid image
annotation tools are ideal for large projects, complex tasks and projects that
require enhanced features. Free tools are perfect for small-scale projects
with a limited amount of funds on hand as most of them only allow basic
annotations and customization. On the other hand, there are more functional,
scalable, and support capabilities to paid tools which are important for large-scale projects and complicated project.
Our criteria which include features of annotations, number of people in your team, size of your project, or if you require automation can guide you to identify what tool will work for you. While for small and medium-sized projects a free or inexpensive paid tool may be enough, famous large-scale companies, which require extensive annotation, will find a set of opportunities offered by paid options truly invaluable.
