CVAT.ai vs DataLoop: What’s the right choise?

In the quickly evolving sector of digital annotation, the Computer Vision Annotation Tool (CVAT.ai) and Dataloop have emerged as significant tools, each playing vital roles in supporting projects related to computer vision and AI. This review delves into their functionalities, business strategies, and main customer groups. Additionally, we gathered perspectives from independent annotators who have utilized both tools in their professional practices.

This article summarized the results of our examination.

Comparative Analysis of CVAT.ai vs Dataloop: Key User Demographics Explored

Both Dataloop and CVAT.ai are robust platforms engineered for data annotation, serving distinct purposes within the realms of machine learning and artificial intelligence.

CVAT.ai, being open-source, is accessible at no cost to individual users, developers, and organizations. It offers a range of annotation tools and exceptional customization opportunities, allowing users to tailor and enhance its functionality to meet specific requirements. The open-source aspect of CVAT.ai presents a compelling choice for those seeking a cost-efficient and flexible annotation tool.

On the other hand, Dataloop is a commercial platform that delivers an extensive array of tools for image and video annotation, dataset management, and workflow automation. It uses a subscription model, providing customized solutions and support catered to the needs of large-scale enterprises.

Let's focus on online, readily accessible versions of both platforms:

CVAT Cloud is notable for its easy access for individuals, professional teams, and organizations. It includes a free version, enabling users to begin annotating without initial expenses. The platform is user-friendly, with a straightforward registration process that allows users to start annotating shortly after signing up.

CVAT.ai offers a broad spectrum of features suitable for business and organizational collaboration. Additionally, its simple, flat-rate pricing structure is particularly appealing to many labeling firms that prefer CVAT.ai for visual data annotation, owing to its favorable cost-to-quality ratio.

Conversely, Dataloop does not offer flat-rate pricing. It provides a free version with certain restrictions, although specifics about these limitations or the details of its paid subscriptions are not disclosed. The website and documentation offer only a general overview of available plans, listed by quotas without detailed descriptions. To obtain precise pricing information, potential users must contact the sales department. This opacity may hinder budget planning, making Dataloop less suitable for casual users or those requiring immediate annotation solutions.

CVAT.ai vs Dataloop: Feature Analysis

Let’s delve into the annotation process and examine the features of each platform as we prepare and annotate a dataset. We will not cover every small detail or precisely time each action. Rather, our goal is to grasp the annotation workflows of both services from the standpoint of a typical user.

Registration and Authentication

The CVAT.ai registration process is straightforward and will take only a few minutes.

The Dataloop registration process is similar to CVAT.ai’s.

Both CVAT.ai and Dataloop have an SSO feature, on CVAT Cloud it is a default feature that doesn’t need additional activation. On CVAT Self-Hosted solution it is a paid feature.

Shared workspace

Both CVAT.ai and Dataloop provide functionalities for setting up shared workspaces, enabling the organization of projects according to team, department, or product line. This arrangement helps ensure that annotators and team members have access only to the workspaces pertinent to their roles, promoting concentrated collaboration and enhanced security.

CVAT.ai provides shared workspaces for organizations and offers the flexibility of both cloud-based and self-hosted configurations. This allows organizations to choose the option that best fits their operational needs, whether they prefer the convenience of cloud access or the control provided by a self-hosted environment.

In contrast, Dataloop offers shared workspaces exclusively in the cloud, lacking a self-hosted alternative.

Another distinction is that in CVAT, setting up an Organization is an optional and separate step. In Dataloop, however, forming an Organization is compulsory, as the platform is designed solely for organizational use and does not accommodate individual personal use.

Projects

Both platforms provide efficient methods for project management, designed to accommodate different organizational structures. This setup enhances workflow productivity and fosters collaborative teamwork.

In CVAT, the procedure begins with transitioning to an Organization that you’ve created at the previous stage. To begin collaborating with the rest of the team, you need to subscribe to the Team plan and invite users to join the Organization.

Then you can create a Project. To do this, just click on the button to get started and fill out the form:

You now have an Organization set up and ready for work.

For Dataloop it is impossible to register as a solo user and you will need to follow the process and create an organization in the process of registration.

We’ve selected Labeling Services for the sake of this article.

The registration concludes with the creation of a Project, which is an obligatory step.

Next, we will proceed to upload data, add team members, set up tasks, and begin annotation.

Data Types

Before uploading any data, it is essential to understand the various data formats that each platform supports.

CVAT.ai is tailored for image annotation (including PDF and PCD files) and video annotation, making it ideal for Computer Vision projects. For comprehensive insights into the data formats CVAT.ai supports, see the documentation on CVAT.ai-supported formats. In terms of diversity, CVAT.ai excels in handling a variety of image and video formats, leveraging the Python Pillow library. Supported image formats include JPEG, PNG, BMP, GIF, PPM, and TIFF, among others, and it supports video formats like MP4, AVI, and MOV.

Dataloop is proficient in managing multiple data formats. This includes image formats such as JPG, JPEG, PNG, TIFF, and video formats like WEBM, MP4, and MOV.

Additionally, it supports audio files including WAV, MP3, OGG, FLAC, M4A, AAC, and point cloud data in PCD format. For textual data in NLP/NER projects, it accommodates TXT, JSON, EML, and PDF.

As we’ve mentioned before, this article does not aim to delve into a detailed comparison of CVAT.ai and Dataloop. Instead, we will provide a broad overview of how these platforms compare and contrast. Our discussion will be limited to image and video data, and data annotation processes supported by both platforms.

Creating Annotation Task

On both platforms, before commencing work, it is necessary to establish an annotation task, which involves uploading the data and assigning labels.

Data Import/Export

Both CVAT.ai and Dataloop offer functionalities for importing and exporting data, allowing for the effective management of various datasets. However, each platform comes with its unique strengths and possible limitations in this regard.

In CVAT.ai, data can be imported and exported in formats widely used for computer vision projects. You can import data from the Cloud Storages or your PC/Laptop by drag and drop, and add data to the project any time.

The process in CVAT.ai is designed to be simple and intuitive:

1. Create a project.
2. Define labels and attributes for the project.
3. Add a task to the project.
4. Upload your data.
5. Submit the task.

The system automatically generates jobs based on the data provided. The user-friendly design ensures that everything can be managed from a single interface without the need to switch between windows.

After annotations are done, you can download annotated data in commonly used formats such as COCO, Pascal VOC, and YOLO, among others.

Like CVAT.ai, Dataloop offers the flexibility to upload data directly to the platform or connect to external cloud storage.

To manually upload data to the Dataloop platform, follow these steps:

1. Create a dataset.
2. Navigate to the dataset page and upload your data.
3. Proceed to the labels and attributes page to add labels and attributes.
4. Invite team members to join the project.
5. Configure and initiate tasks for the project.

So there are a lot of switches between screens, and note, that Dataloop requires you to invite at least one team member to the organization before creating a task. This is a mandatory condition:

You might need to complete several additional steps, the full process is detailed in the Dataloop documentation.

In summary, initiating a project and uploading data in Dataloop takes a bit longer, as the process lacks transparency.

Cloud Storage Integration

You can also import and export data from Cloud Storage, as both CVAT.ai and Dataloop to cloud services like AWS, GCP, and Azure for read and write access.

CVAT.ai allows you to connect to cloud storage platforms such as AWS, GCP, and Azure. This functionality is especially beneficial for organizations that depend on these services to store and access extensive datasets.

Dataloop also supports cloud storage integration with AWS, GCP, and Azure.

Labels and Tools

Both platforms naturally support labels and attributes.

In CVAT.ai, labels can be added at both the Project and Task levels. This procedure is simple and is fully managed via the UI interface, where attributes can also be added to the labels.

You can create tasks and add labels at any moment, there is no need to take additional actions.

For the task you’ve created, all annotation tools will be available at any time by default, unless you intentionally restrict them.

In Dataloop, you cannot add labels while creating a task; therefore, you need to add labels before creating one and assigning annotators. This can be done from the Data Management page.

Same as CVAT.ai, Dataloop supports attributes:

Annotator Assignment

You can assign tasks and jobs to annotators in both CVAT.ai and Dataloop.

CVAT offers a streamlined system for organizations, allowing managers or team leads to invite workers and assign specific tasks and dataset samples to annotators.

When inviting users, you can assign specific roles, designating them as either simple annotators or as managers and supervisors.

After inviting users, you can distribute one task among several annotators.

In Dataloop, you must first invite and assign annotators before you can create a task. The process of invitation is straightforward — you need to clarify the email address of the invitee and send out an email.

After the invited person accepts the invitation, you can finish creating a task and assign it to annotators.

Annotation Process

The annotation processes in CVAT.ai and Dataloop are quite similar, except more tools are available in CVAT.ai from the user interface.

To illustrate it, we’ve annotated the same image using both platforms.

In CVAT.ai, you have the flexibility to use different tools at any time, for various objects as needed:

In Dataloop, you can do pretty much the same thing too:

On both platforms, all tools are readily available at any time, ensuring flexible annotation capabilities.

Automatic and Semi-Automatic Annotation

Beyond the standard tools and practices, there are extra features available that can accelerate the annotation process, including automatic and semi-automatic annotation options.

In CVAT Cloud you can do it with pre-installed models and models from Hugging Face and Roboflow.

Dataloopo also offers AI-powered tools that can automate parts of the annotation process. This includes features for auto-labeling, which can significantly speed up the data annotation workflow by automatically identifying and labeling objects within images or videos.

Verification & QA

Both CVAT.ai and Dataloop include Verification and Quality Assurance (QA) features, essential for upholding high quality in annotation projects. Nonetheless, the availability and particular features of these functions vary.

CVAT.ai offers Verification and QA tools in both its self-hosted and cloud versions, providing flexibility for different user preferences.

Key features include:

• Review and Verification: CVAT allows for the review and verification of annotations and automatic QA results.
• Assign Reviewer: Project managers can assign individual users to review specific annotations, enabling focused and efficient QA processes.
• Annotator Statistics: CVAT provides metrics and statistics to monitor annotator performance, which is vital for tracking quality and productivity.

And more.

Dataloop offers Verification and QA features akin to those found in CVAT.ai:

• Review and Verification: Like CVAT, Dataloop provides functionality for reviewing the annotations made by other users. You can do it manually or automatically.
• Assign Reviewer: This feature allows managers to allocate specific annotations to designated reviewers for quality checks.
• Management Reports & Analytics: Dataloop offers statistics on analyzing the performance of the team.

And more.

Analytics

In CVAT.ai, the analytics are designed to deliver insights into the annotation workflow, tracking the time invested in annotations and evaluating performance. This feature is vital for project managers aiming to streamline processes and maintain quality assurance.

Dataloop offers analytics and performance control features, to better understand your team performance and workflow efficiency.

Single Sign-On

Single Sign-On is supported on both CVAT and Dataloop.

For CVAT Self-Hosted solution it is a paid feature.

API Access

Both CVAT.ai and Dataloop provide API access, which offers programmatic capabilities that significantly increase the flexibility and ability of these platforms to integrate with other systems.

CVAT.ai’s API access allows the automation of various tasks and integration with external systems. Users can interact with CVAT through API to upload datasets, retrieve annotations, and manage projects.

Similarly, Dataloop offers API Access, emphasizing seamless embedding of its functionalities into other systems.

***

To put it succinctly, CVAT.ai is an excellent tool suitable for anyone, whether you are working solo on a minor project or managing a large team with extensive projects. Its user-friendly design and scalability make it ideal for any size of organization.

Dataloop shares many functional similarities with CVAT.ai, but it is specifically designed for organizational use. Additionally, some aspects of its interface logic may be perplexing to users.

CVAT vs Dataloop: Annotation Tools

Examining the annotation capabilities of Dataloop and CVAT.ai reveals that each platform provides distinct features suited for different project needs.

Notably, Dataloop accommodates a wider variety of annotations, including audio, which are absent in CVAT.ai as it specializes in image annotation and video annotation.

As our analysis is based solely on the image and video annotation functionalities available on both platforms, if you map the tools, you will get the following picture:

CVAT.ai vs Dataloop: Annotators' Opinion on Tools and Ease of Use

We went out and asked independent annotators about their experience with CVAT.ai and Datallop.

Let’s start with an overall impression. We asked annotators what they generally think about both tools.

For CVAT.ai, we received mixed responses with suggestions for improvement.

“What I like most about CVAT is the ability to copy annotations and paste them in the next frame as well as propagating. CVAT can load on most machines easily and can work on the dataset easily without hanging or requiring a huge processor.”

“CVAT is very easy to use as the tools in CVAT are easy to understand. The use of polygons to annotate is a bit difficult as we need to annotate every point individually.”

Dataloop also received some feedback:

“Dataloop is good in labeling 3D images as you can rotate the scene and another advantage is that you can increase and decrease the pixels you want to label. What I dislike about dataloop is that it takes forever to load and requires you to have a powerful processor and large RAM so that it doesn’t hang when working”

“In Dataloop, there are not much tools. So using Dataloop is easy, but there are certain tools that doesn’t allows us to annotate the objects as required. So, for simple use it is better.”

Conclusion: Both tools are easy to use, but CVAT.ai has a bit more options and tools while Dataloop is more suitable for 3D annotations.

When asked which tool was easier to configure and start using, CVAT.ai or Dataloop:

“CVAT is easier to configure”

“It is easier to get familiar with CVAT. Also to configure, we can easily export to required formats.”

When asked about specific features in the interfaces of CVAT.ai and Dataloop that stood out, the feedback varied:

For CVAT.ai:

“The interface and usability of CVAT is really simple and can be understood quite easily since the interface is straight to the point. you can easily pick the correct tools to use.”

“Labeling with overlay features is easy here. It saves a lot of time creating layers. Pipeline tools and management is difficult.”

For Dataloop:

“This one’s a bit complex and requires a bit of training to get used to the tool”

“Labeling the objects is very fast in Dataloop. Pipelines can easily be created there.”

Conclusion: In conclusion, feedback indicates that CVAT.ai and Dataloop offer distinct user experiences and features. CVAT.ai is appreciated for its clear, user-friendly interface, though some find its pipeline management challenging. Conversely, Dataloop is seen as more complex, but still a comfortable tool to use.

When it comes to the most useful functionalities or features of CVAT.ai and Dataloop, users have highlighted specific aspects that stand out in each tool:

For CVAT.ai:

“Mostly all features, depending on project requirements.”

“The ‘ctrl’ button really helps when you want to label faster and more precisely.”

“Drawing mask polygons seems to be very useful in CVAT.”

For Dataloop:

“The ability to use you mouse and rotate the whole scene while zooming in and out was really nice”

“Here also the polygons are easy to create and mask.”

Conclusion: These insights emphasize the unique functionalities that each tool offers, catering to different aspects of user requirements and project types.

When comparing the annotation tools of CVAT.ai and Dataloop in terms of variety and efficiency, users provided varied insights:

“In CVAT I would mostly annotate 2D datasets while on dataloop I annotated 3D datasets.”

“CVAT has download option where the masks can be covered properly without leaving any bits.”

Conclusion: While CVAT.ai and Dataloop are generally seen as comparable in terms of the variety of annotation tools they offer, CVAT.ai is preferred for its speed and quality. Meanwhile, Dataloop excels with its features for 3D point annotation.

When asked about the limitations or challenges encountered with the annotation tools in CVAT.ai and Dataloop, users shared specific experiences:

“Not really”

Was the only answer! :)

Conclusion

To summarize, both CVAT.ai and Dataloop are robust choices for data annotation, yet CVAT.ai stands out due to its open-source framework, which is particularly well-suited to specific user requirements and project sizes. It caters to individual developers, organizations, and research groups, providing a versatile and affordable solution for annotating images and videos.

On the other hand, Dataloop offers a commercial solution aimed at enterprise-level needs, complete with extensive services and support.

CVAT.ai, however, is favored by those who value extensive customization and wish to keep costs low, thanks to its flexible nature and lack of licensing fees. This makes it highly beneficial for budget-aware teams and small to medium-sized businesses. Additionally, its continual enhancement through community-driven updates ensures that CVAT.ai remains at the forefront of annotation technology, ideal for projects that prioritize innovation, adaptability, and cost-effectiveness.