Datasaur vs. LabelBox
Let’s begin by listing some of the reasons you may have discovered this article:
- You are a data scientist, ML/NLP engineer, or linguist who is focused on building better training data for your team.
- You have found that spreadsheets are not able to efficiently solve your data labeling requirements and you know you need software that can help. Internal tools will take months to build and years to maintain.
- You’re looking for a platform that can host not only yourself but a team of project managers, reviewers, and annotators from your organization.
- You are shopping around and exploring if there is a platform that best suits your requirements.
Choosing a data annotation platform is an important decision. It can also feel overwhelming at first glance. Data annotation comes in all shapes and sizes from video to text, audio to image. But what all these have in common is — accurate training data is key to improving business outcomes. What features a platform provides is always a critical part of the decision to ensure your team's needs — and business objective needs — are being met. One must understand how a platform’s features will impact your labeling workflow in order to ensure optimal efficiency and accuracy.
In this article, we will walk through the feature differences between Datasaur and Labelbox. These two organizations have made a prominent impact in the market. So, without further ado, let's begin!
TL;DR: what is the difference between Datasaur and Labelbox?
Before we jump into the detailed differences between Datasaur and Labelbox, here is a brief summary of the distinctions.
- Datasaur is designed to give you a comprehensive tool for all of your NLP labeling needs specializing in text and audio. Labelbox provides the ability to label photos, videos, text, and audio. It heavily emphasizes photo and video, and includes advanced computer vision capabilities. By comparison, Datasaur is built for NLP and offers much more comprehensive features for text and audio.
- Datasaur is packaged with a complete workforce management tooling system, including the ability to communicate with teammates in the actual projects. Labelbox also has a workforce management system. Both organizations enable admins to provide privileges for certain roles. Labelbox can provide an annotation workforce for interested clients, whereas Datasaur utilizes partnerships to help clients find the best annotation workforce.
- Datasaur pricing is based on access to product features and the number of “seats” your team workspace will hold. Labelbox’s pricing is also based on seats and access to product features; however, Labelbox does stipulate that each annotation is “counted as billable.” Datasaur does not charge for individual annotations.
- Both Datasaur and Labelbox offer a robust review system. Both companies allow comments between labelers/reviewers, a review step, and consensus rules for automatic review acceptance. Labelbox also includes a benchmark step that enables the user to designate a label asset as the gold standard of review.
Brief history and mission of both companies
Labelbox was founded in 2018 in San Francisco by Brian Rieger, Manu Sharma, and Dan Rasmuson. They founded Labelbox with the intent of building the best annotation tools to advance AI in the fields of image, video, and text.
When describing what brought them to build Labelbox, Brian Rieger stated “When you look at all of the different sectors of the economy today, there’s a lot of visual decision-making going on. Machine learning, and AI [artificial intelligence] more generally, is good at doing visual analysis; it’s good at finding patterns in visual information. This hasn’t been done before,” Rieger added, “We haven’t been able to code software algorithms and use logic directly to understand the complexity of the visual world and the written world, but machine learning and AI can do that, and that’s one of its hallmarks.”
As mentioned in the TL;DR: section, Labelbox thrives in video and imagery annotation. For these capabilities, they are trusted by prominent companies such as Bayer, Black & Decker, Bristol Meyers Squibb, and Warner Brothers.
Labelbox also enables the user to annotate geospatial imagery. This feature demonstrates how advanced their capabilities have stretched within the space of image and video.
Datasaur was founded in 2019 by Ivan Lee. During his tenure as a Product Manager at Apple and Yahoo, he spent hundreds of millions of dollars to solve their NLP labeling needs. Discovering this as a massive need in the AI industry, Ivan founded Datasaur with the intent of specializing in NLP, for text and audio use cases.
Datasaur began in the winter of 2019, with a small team of five. Coupled with the product knowledge of Ivan and talented engineers, the company readily grew. After graduating from YCombinator, Datasaur took investment from Initialized Capital and the CTOs of OpenAI and Segment. In its first years, Datasaur was able to earn the trust of clients such as Zoom, Spotify, and Netflix for audio and textual labeling.
From the very beginning, Datasaur has evolved with its customers — this is the core and guiding philosophy of the team. Ensuring that Datasaur is exceeding customer needs and expectations has enabled the company to grow to over 50 employees in less than three years.
While many annotation tools have started with Computer Vision, Datasaur saw that NLP was an underserved space and committed to creating the most innovative and comprehensive NLP labeling tool. Our mission is to offer a complete suite that caters to all NLP needs, first and foremost.
How NLP Labeling Tasks Thrive on Datasaur
Datasaur and Labelbox give you a labeling platform for text and audio. However, Datasaur provides an interface that is not only customizable and simple but also efficient. Datasaur’s platform was built with the power user in mind while ensuring it is also easily accessible for non-technical users. Our users span the spectrum from seasoned engineers and labeling specialists to new contractors who need to be able to navigate the platform as easily as they could navigate a spreadsheet.
Experience Labeling with Complete Efficiency and Simplicity
There are several ways Datasaurr toes this fine line of simplicity and customizability.
Simple, easy-to-learn interface
Datasaur’s interface makes it easy to use for non-technical users. The label set being used is visually prominent. The user can keep their hands on the keyboard the entire time using hotkeys and shortcuts, making the labeling experience effortless. Furthermore, the user has the ability to draw relationships between labels with a simple double-click to categorize the relationship.
Although Datasaur and Labelbox allow the user to add text-based annotations, only Datasaur boasts an integrated fluidity. The user does not have to touch their mouse to label spans of texts.
Finally, Datasaur and Labelbox approach audio annotation very differently. Datasaur allows the user to annotate the transcription of the audio while listening to the audio, all in the same interface. Labelbox only allows the user to listen to the audio and answer questions with radio boxes, drop-down classifications, free-form text boxes, and a checklist. In Datasaur, you can either upload the transcription alongside the audio or you can even build the transcription within the interface. Then, the user is able to label the text and create corresponding timestamps in the audio player.
Datasaur holds the advantage in audio by offering a more complete suite of labeling options from the transcription to the actual audio.
Labelbox offers more breadth in data annotation options by including photo and video. Datasaur offers more depth for NLP practitioners when it comes to text and audio. For text, Datasaur’s interface is simple and intuitive for efficiency. For audio, Datasaur allows the user to interact with specific timestamps and the transcription.
If your use case utilizes photo and video, Labelbox is the better option.
If you are developing training data for NLP using text and audio, Datasaur is the best option.
3 reasons companies choose Datasaur
1. An easy, intuitive labeling interface for labelers, reviewers, and managers
Datasaur is used for a wide variety of NLP use-cases. In fact, on your team’s homepage, we have shortcuts to common NLP projects such as Named Entity Recognition (NER) and Parts of Speech (POS), alongside more advanced project types like Coreference Resolution. A user in consumer electronics said this about Datasaur:
2. An Easy to Use Platform with Excellent Customer Service
As Datasaur’s team is spread across the globe, we pride ourselves in never being too far from you. This empowers us to respond to every message and email immediately. We never let one message go unanswered for more than 24 hours.
Datasaur’s customer service not only applies to questions and resolving issues, but also onboarding. Datasaur creates a 3-month onboarding experience for each new customer. This experience includes guides for your specific use-case and meetings to ensure Datasaur is successfully adopted by the team.
3. Datasaur is Secure
Datasaur is a secure place for your data. The platform is end-to-end encrypted, no one has visibility of your data. The company is SOC 2 Type II compliant, as well as HIPAA compliant. Safety and security are priorities for the Datasaur.
So, is Datasaur for you?
Datasaur is for you if:
- You need a simple, easy-to-setup NLP labeling solution
- You are looking for a platform that offers a complete workforce management tool
- You require a SOC 2/HIPAA compliant solution that can deploy to VPC/on-premise
Datasaur is NOT for you if:
- You are annotating data that is not text or audio
- You want the platform to also provide first-party annotation services
We hope that you find the platform that is best suited for your needs. If you have NLP labeling needs, request a demo today.
