Labelbox is a data-labeling and training-data management platform.
In order to train machines to make decisions on behalf of humans, they must learn to make those decisions. Learning to make decisions is called data labeling. Managing the decisions that are being made by machines is training data management.
With our labeling interface, the labeling applications are nearly endless. We have out-of-the-box labeling interfaces for images, video, and text data. We allow for the development of custom interfaces on our platform so nearly anything can be labeled.
If your organization is using machine learning you are probably a good candidate for a data labeling and management solution. In most cases, Labelbox is used by companies developing and deploying machine learning models and who value efficient...
Labelbox is your one-stop-shop for all your training data needs, helping you to scale your data labeling process, manage the quality of your training data, and improve the performance of your machine learning model predictions.
- Setup your labeling tasks in minutes using the Labelbox interface configurator
- Leverage internal and external teams using Labelbox collaboration and management functionality.
- Track labeler performance, project progress, and labeling quality
Labelbox allows you to connect your machine learning model and compare your model's performance against your team of labelers. You can also track the consensus of your labeled dataset against entire labeling team to ensure that there is high labeling quality.
Lablebox is the industry leading labeling and training data management platform. What sets us apart is our focus on three main pillars:
- Our world-class labeling interface which is completely customizable and open source
- Collaboration management
- Quality and performance management
Generally it is beneficial to have someone experienced in data science on your team to help ensure that your machine learning project is successfully developed and deployed. However we designed our platform to require the minimal knowledge and expertise necessary, such that anyone can become a labeler.
You can sign up at https://app.labelbox.com/signin.
Labelbox provides support to all Labelbox users. Our support team is located in San Francisco and Miami and hours of operation are between 9am - 5pm PST. You can best reach support via our chat system in the bottom right of the page. You can also reach us at firstname.lastname@example.org.
We have a community license which is free for evaluation, individuals, and small projects with a 5000 labels/year limit. For organizations building expert artificial intelligence systems and for business process outsourcing companies please contact us at https://labelbox.com/enterprise.
Check out https://labelbox.com/pricing for more info on the community and enterprise license.
Yes we do. Please contact us via our chat in the bottom right or at email@example.com for more information.
With Labelbox you can still outsource your labeling projects as well as leverage your internal team on those same projects seamlessly. The big differentiator is that with Labelbox you can manage the quality and performance of your entire labeling team whether they are outsourced or internal, all in one place.
Labelbox does not provide in-house labeling services but we do work with several labeling companies (BPO Firms) that we have vetted and currently work with many of our customers in Labelbox. We simply recommend them as a third party to help our customers so you are always free to choose any labeling services company that meets your requirements.
It's quick and easy to start annotating data using locally installed tools. For most simple annotation tasks being performed by a single labeler, this solution architecture works well. As data labeling needs scale, data management and quality control processes are needed to produce accurate and consistent training data. A common cause of underperforming AI systems is low accuracy training data.
When building data labeling infrastructure, consider the following:
Homegrown tools are built to exist and serve a particular function, but with new business demands comes the cost of upgrades. There is a high cost to ongoing maintenance, both in time and money. Technical debt accrues over time due to engineer turn-over, product neglect, and evolving product demands.
Developing an internal product requires planning, resource allocation, and preparing for the unknown. Because feature flagging platforms are relatively new, it can be difficult to accurately define the scope and construct a solution for needs across engineering and product groups.
Internal tools are generally not built for usability, scalability, or cross-team support. They are built to solve an immediate pain point or provide minimum viable functionality as quickly as possible.
Turning raw data into accurate and consistent training data is a team effort. Engineers, domain experts (labelers), and managers must work together while playing different roles. Data labeling infrastructure must facilitate this by providing information and interfaces unique to these roles.
Productionizing AI systems takes fast, reliable, and scaled infrastructure across raw data collection, data labeling, and compute.
Check out our Build vs. Buy Calculator at https://labelbox.com/buy-vs-build
This is one of our core competencies and one of the main reasons we decided to build Labelbox. We saw that all the available options for labeling made it extremely difficult to ensure our training data was high enough in quality for a production-ready machine learning model and it cost us valuable time and money in the end. We developed a world-class quality and performance management interface within our platform to address this.
You can set up a labeling project in three steps.
- Click “Add New Project” and give the project a name.
- Attach your dataset to the project.
- Customize your labeling interface.
How to Create a Project
Check out our guide for Connecting Cloud Data.
You can do this by Re-Enqueuing them from the Activity Tab. For a guide on Re-Enqueuing labels click here
Labelbox works with source data hosted on-premises or on a private cloud. The source data is accessed directly from the client computer and never shared (or accessible) by Labelbox. Check out our guide here.
We do not currently support mobile devices, although we are working on adding this in the future.
We do not currently support mobile browsers so using Labelbox on an iPad will not work.
Yes, unless your network has a restrictive firewall, or you have internet connectivity issues. We currently only support the English language on our platform.
We have many users using Labelbox in China although some have reported issues accessing Labelbox on their network. Always check if you have an adblocker or firewall preventing you from accessing our website.
No, you must have an internet connection to use Labelbox.
We recommend Chrome or Firefox. We do not recommend Internet Explorer or Safari due to compatibility issues.
Your labeled data can be exported in JSON, CSV, COCO, VOC, or TFRecord which should allow you to import it into any machine learning framework.
Auto Consensus ensures consistency and accuracy of the labeled data. When turned on, some or all of the images are labeled by more than one labeler so that a consensus among labelers for each labeled datum can be calculated and ultimately managed. You can configure the percentage of your dataset in a project that will be randomly selected to be labeled more than once by distinct collaborators. You can also configure the number of times this percentage of labels will be labeled. Check out our docs here for more detail.
If Auto Consensus is turned on and configured then yes, otherwise no.
Yes, we have a fully featured API. Everything you can do with our interface, you can do programmatically through our API. The API is available on our Enterprise tier. You may contact sales at https://labelbox.com/enterprise.
If you are a paid user you may request an API key from our support team via the Chat icon (preferred method) or by sending an email to firstname.lastname@example.org, and title it "API Request".
You can explore our API tutorial here.
With our help, it can be imported. Simply send us the labeled data and we will reformat it and upload it for you.
Open your project then go to settings > Other, and click on the delete button.