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In the rapidly advancing field of digital annotation, Computer Vision Annotation Tool (CVAT.ai) and Dataloop have become prominent annotation tools, each serving crucial roles in facilitating computer vision and AI projects. To better understand their utility and impact, this analysis explores their features, business models, and primary client base. We also solicited insights from independent annotators who have employed both tools in their professional workflows.
This article summarized our findings.
Dataloop and CVAT.ai are both platforms designed for data annotation, each catering to different needs in the fields of machine learning and artificial intelligence.
CVAT.ai is an open-source platform, making it freely available for individual users, developers, and companies. It supports a variety of annotation tools and is highly customizable, allowing you to modify and extend its capabilities according to your specific needs. CVAT.ai's open-source nature makes it an attractive option for those looking to implement a cost-effective and adaptable annotation solution.
Dataloop is a closed-source platform that provides a comprehensive suite of tools for annotating images and videos, managing datasets, and automating data workflows. It operates on a subscription-based model, offering tailored services and support for enterprise-level needs.
Let’s talk about online, ready to use versions of both platforms.
CVAT Cloud stands out due to its accessibility for individual users, professional teams, and organizations. It provides a free version, so you can start annotating without any upfront cost. The registration process is simple and the interface is designed to be user-friendly, so you can start annotating within minutes after registration.
CVAT.ai includes the wide array of the features needed by businesses and organizations and team collaboration. Furthermore, its straightforward, flat-rate pricing makes it a favorite among many labeling companies that choose CVAT.ai for annotating visual data due to its excellent cost-to-quality ratio.
The Dataloop is designed only for teams and organizations working on AI projects. Dataloop does not offer flat-rate pricing. It offers a free plan with limitations, but the specifics regarding the limitations of this plan or the details of the paid plans are not clearly outlined.
The website and documentation provide only high-level information: all available purchases are described in terms of quotas without specifying the details or grades. To gain a better understanding of potential expenses, you would need to contact their sales team. This lack of transparency can complicate budget planning. This strategy renders Dataloop less practical for casual users or those in need of immediate annotation solutions.
Let's explore the annotation process and review the features of each service as we set up and annotate a dataset. We won't dive into every minor detail or time each step exactly. Instead, our aim is to understand the annotation workflows of both platforms from the perspective of an average user.
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 a 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.
CVAT.ai and Dataloop both offer features for creating shared workspaces, allowing you to organize projects by team, department, or product line. This setup ensures that annotators and other team members can access only the workspaces relevant to them, facilitating focused collaboration and improved security.
CVAT.ai offers shared workspaces for organizations, with options for both cloud-based and self-hosted configurations. This versatility enables organizations to select the solution that aligns best with their operational preferences, whether they favor the ease of cloud accessibility or the autonomy of a self-hosted setup.
Dataloop provides shared workspaces only in the cloud as it doesn’t have a self-hosted option.
The other difference is, that in CVAT, creating an Organization is optional and treated as a distinct step. Conversely, for Dataloop, establishing an Organization is mandatory, as the platform does not support personal use.
Both platforms offer effective ways to manage projects, tailored to suit various organizational frameworks. This structure improves workflow efficiency and promotes 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 process ends with creating a Project, this is a mandatory step.
Now let’s move forward and try to upload data, invite team members, create tasks and annotate it.
Prior to data upload, it is crucial to familiarize yourself with the types of data that each platform can handle.
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, 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, 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.
On both platforms before starting working, you need to create an annotation task. This includes loading the data and adding labels.
Both CVAT.ai and Dataloop provide features for data import and export, so you can manage diverse datasets effectively. Each platform, however, has its distinct capabilities and potential restrictions in this area.
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 from your own 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.
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.
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:
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.
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 are can do pretty much the same thing too:
On both platforms, all tools are readily available at any time, ensuring flexible annotation capabilities.
Aside from very useful tools and practices, there are additional options to speed up the annotation process, such as automatic and semi-automatic annotation.
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.
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:
And more.
Dataloop offers Verification and QA features akin to those found in CVAT.ai:
And more.
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.
Both CVAT.ai and Dataloop offer API access, providing programmatic capabilities that greatly enhance the flexibility and integration of these platforms 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.
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 in both platforms, if you map the tools, you will get the following picture:
* The difference is that 3D Semantic Segmentation is only available in Dataloop. On the other hand, CVAT.ai features OpenCV and AI Tools with preinstalled models for semi-automatic annotation.
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! :)
In conclusion, both CVAT.ai and Dataloop provide strong options for data annotation, but CVAT.ai is particularly notable for its open-source nature, which suits specific user needs and project scales. It is designed for individual developers, organizations, and research teams, offering a customizable and cost-effective platform for image and video annotation.
Dataloop provides a commercial solution tailored for enterprise-level deployments, offering comprehensive services and support. In contrast, CVAT.ai appeals to users seeking greater control and minimal spending, thanks to its unmatched flexibility and customization potential. Its absence of licensing fees significantly benefits budget-conscious teams and small to medium enterprises. Moreover, community-driven updates and improvements ensure that CVAT.ai remains a leader in annotation technology, making it ideal for projects where innovation, customization, and cost-efficiency are crucial.
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