Labelbox
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Labelbox is a platform for labeling data to train machine learning models effectively.
What is Labelbox?
Labelbox is a data labeling platform used for creating training data for machine learning models. It provides tools for annotating various types of data, such as images, videos, and text, with high-quality labels that are essential for developing and improving AI algorithms. Labelbox offers features for collaboration, quality assurance, and managing labeling projects efficiently. By streamlining the data labeling process, Labelbox helps organizations accelerate the deployment of machine learning models by ensuring the availability of accurately labeled datasets. Overall, Labelbox plays a crucial role in empowering AI development by facilitating the creation of labeled datasets required for training and testing machine learning algorithms.
Who created Labelbox?
Labelbox was created by Manu Sharma. The company was launched on February 17, 2021. Labelbox is a data labeling software platform that helps companies manage the process of labeling data for machine learning applications.
What is Labelbox used for?
- AI assisted alignment
- Optimized for quality and control
- Technology and software
- Building AI applications
- Training and fine-tuning models
- Automating tasks with Labelbox Machine Learning Models
- Data curation
- Labeling services
- Model training and diagnostics
- Global network of expert humans
Who is Labelbox for?
- Data Scientist
- Machine learning engineer
- AI Researcher
- Software developer
- Product Manager
- Data Analyst
- Image Annotation Specialist
- Quality assurance tester
- Project Manager
- Business Analyst
How to use Labelbox?
To use Labelbox, follow these steps:
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Sign Up: Create an account on the Labelbox platform using your email address.
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Create a Project: Click on "Create Project" and provide details such as project name, description, and data to be labeled.
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Import Data: Upload the data you want to label, whether it's images, videos, or text.
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Label Data: Define the labeling task by choosing from a variety of annotation tools like bounding boxes, polygons, or classifications. Assign these tasks to team members or label the data yourself.
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Quality Control: Review the labeled data for accuracy and consistency. Make any necessary corrections.
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Collaborate: Invite team members to collaborate on the labeling process, assigning specific tasks to individuals.
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Iterate: Continuously improve the quality of labels by iterating on the labeling process based on feedback and review.
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Export Data: Once labeling is complete, export the annotated data in the desired format for further use in machine learning models or analysis.
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Monitor Progress: Track the progress of labeling tasks, monitor the performance of annotators, and manage the project timeline effectively.
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Feedback and Improvement: Gather feedback from annotators and project managers to implement improvements in the labeling process for future projects.
By following these steps, you can effectively use Labelbox for your data labeling needs.
Pros
- Labelbox is the data factory for generative AI, providing high-quality, differentiated data to leading model builders and enterprise AI teams.
- Labelbox's software-first approach delivers unmatched quality, control, and efficiency.
- Labelbox combines on-demand expert labeling services with the industry-leading data labeling platform.
- Labelbox offers Boost labeling services that tap into the diverse and advanced skills of the Alignerr expert community.
- Labelbox operates a global data labeling infrastructure for the biggest AI workloads, providing high-quality data labeling in nearly any domain.
- Trusted by companies of all sizes — from startups to Fortune 500 enterprises.
- Offers an AI-assisted alignment for maximum efficiency and throughput in data curation, labeling, quality assurance, and pre-labeling with foundation models.
- Optimized for quality and control, helping customers achieve breakthroughs with high-quality data.
- Provides technology and software integration with Google Cloud for LLM evaluation services.
- Labelbox offers a comprehensive platform for data curation, AI-assisted labeling, model training & diagnostics, and labeling services all in one place.
Cons
- The list of cons for using Labelbox is not available in the uploaded files.
Labelbox FAQs
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How does Labelbox calculate LBUs?
- You are billed at the end of each month based on the number of LBU your team accrues during the period. Annotate and Model LBU are only charged for new data rows used during the period. Catalog LBU are charged monthly for all data rows in Labelbox.
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How does LBU Consumption work?
- LBUs are consumed when specific actions occur on data rows within the labeling project or Experiment in Model, such as data row approval, being marked as 'done', or added to a Model Run.
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What happens if I delete data? How am I charged?
- Deleting data rows from Catalog removes them from your total LBU count, and you won’t be charged for those data rows in the next month’s billing cycle. However, deleting data rows in Catalog will prevent usage of those data rows in Annotate and Model.
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What are some examples of LBU consumption across Labelbox products?
- Examples include accruing LBUs for uploading data rows in Catalog, labeling in Annotate, and generating predictions in Model. Charges vary depending on the product and actions taken on the data rows.
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What happens if I exceed my Free tier LBU limit?
- If you reach the Free tier limit of 500 LBU per month, you will still be able to access and export your data, but you won't be able to add new data, labels, or predictions. Upgrade to a paid plan to continue using Labelbox.
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How do I know if the Enterprise tier is right for me?
- All Labelbox plans are priced based on LBU consumption. Enterprise plans require an additional platform fee for additional features, customer support, and labeling services. Discounts are available based on annual LBU commitments.
Get started with Labelbox
Labelbox reviews
Nia Ochieng
January 4, 2025
What do you like most about using Labelbox?
I appreciate the intuitive user interface of Labelbox, which makes it easy to navigate and manage labeling tasks. The integration with Google Cloud is a game changer for our team, allowing us to leverage the power of Vertex AI seamlessly.
What do you dislike most about using Labelbox?
While the platform is quite powerful, I found that the pricing can be on the higher side for smaller projects. It might not be the most budget-friendly option for startups.
What problems does Labelbox help you solve, and how does this benefit you?
Labelbox helps streamline our data labeling process, significantly reducing the time it takes to prepare datasets for training AI models. This efficiency allows us to focus more on model development rather than data preparation.
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David Kimani
December 16, 2024
What do you like most about using Labelbox?
The quality of the labeled data is exceptional! Labelbox's expert labeling services ensure that our datasets are accurate, which is crucial for the performance of our AI models.
What do you dislike most about using Labelbox?
The initial setup process was a bit complex, but once we got through it, it became much easier to use.
What problems does Labelbox help you solve, and how does this benefit you?
Labelbox allows us to handle large volumes of data efficiently, making it easier to train our models on diverse datasets. This capability significantly enhances our project's scalability.
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Takuya Suzuki
January 5, 2025
What do you like most about using Labelbox?
The integration with Google Cloud is fantastic! It allows our team to run evaluations quickly and effectively, which is essential for our AI projects.
What do you dislike most about using Labelbox?
Some features seem to require a steep learning curve, especially for those who are new to data labeling platforms.
What problems does Labelbox help you solve, and how does this benefit you?
Labelbox effectively addresses the challenge of managing multiple labeling jobs at once. It has enabled us to deliver projects on time, which is vital in our competitive market.
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