Labelbox - Detailed Review
Data Tools
Labelbox - Detailed Review Contents
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Labelbox - Product Overview
Overview
Labelbox is a comprehensive data labeling platform that plays a crucial role in the development and training of machine learning (ML) and artificial intelligence (AI) models. Here’s a brief overview of its primary function, target audience, and key features:Primary Function
Labelbox is designed to streamline and simplify the data labeling process, which is essential for training accurate ML and AI models. The platform allows users to import, label, and manage datasets efficiently, ensuring high-quality annotated data for various machine learning tasks such as image classification, object detection, semantic segmentation, and text classification.Target Audience
Labelbox’s target market includes a diverse range of professionals and organizations involved in AI and ML development. This includes data scientists, machine learning engineers, AI researchers, and software developers. The platform caters to businesses of all sizes, from tech startups to large enterprises, across various industries such as healthcare, finance, retail, and automotive.Key Features
User Roles and Permissions
Labelbox allows project managers to assign specific roles and permissions to team members, ensuring each member has the appropriate level of access and authority within the project.Task Assignment and Tracking
Project managers can assign labeling tasks to individual annotators, track their progress, and manage workloads effectively. This feature helps in distributing the labeling workload efficiently among team members.Collaborative Tools
The platform includes a built-in commenting feature that enables annotators and project managers to communicate and discuss specific labeling tasks or issues directly within the platform. This promotes collaboration and clear communication.Review and Feedback
Labelbox supports a review process where project managers or designated reviewers can examine and provide feedback on labeled data, helping to identify and rectify any labeling errors or inconsistencies.Quality Assurance
The platform offers comprehensive quality control features, including real-time analytics, benchmarking, and consensus scoring. It also utilizes advanced metrics and AI-assisted review processes to maintain high data quality at scale.Customizable Workflows
Labelbox provides customizable workflows and supports a wide range of data types and labeling tasks, including text, images, audio, and video. This flexibility allows users to adapt the platform to their specific needs.Advanced Tooling
The platform includes advanced tools such as a multimodal chat editor for generating high-quality chat data for Generative AI models and AI critics for automated feedback on text and code.Conclusion
Overall, Labelbox is a powerful tool that simplifies the data labeling process, enhances collaboration, and ensures high-quality data for AI and ML projects. Labelbox - User Interface and Experience
User Interface of Labelbox
The user interface of Labelbox, a data labeling platform for machine learning projects, is crafted to be intuitive and user-friendly, ensuring a smooth and efficient experience for both data annotators and project managers.Intuitive Interface
Labelbox features a clear and logical layout, making it easy for users to find the tools and controls they need. The interface is designed to minimize cognitive load, with clear icons, labels, and workflows that reduce the mental effort required to perform tasks.Ease of Use
The platform offers a straightforward interface that simplifies the data labeling process. It is accessible to both technical and non-technical users, allowing them to import data, define labels, apply annotations, and export labeled data with ease. The dashboard provides a comprehensive overview of projects, datasets, and labeling progress, helping users to manage their work efficiently.Key Features
Annotation Tools
Labelbox provides a range of annotation tools such as bounding boxes, polygons, keypoints, and text labels. These tools are easily accessible and can be customized based on the specific requirements of the project.Keyboard Shortcuts and Hotkeys
Customizable shortcuts are available for common actions, allowing annotators to work faster without relying solely on mouse interactions.Automated Tools
Features like auto-labeling, model-assisted labeling, and pre-annotations significantly reduce repetitive tasks, enabling annotators to focus on verification and correction rather than starting from scratch.Batch Processing
The ability to label multiple items simultaneously or apply changes in bulk reduces the need to repeat the same task multiple times.Performance and Responsiveness
Labelbox ensures responsive performance, even when handling high-resolution files. This means annotators do not have to wait for the interface to load, allowing them to work continuously without interruptions.Collaboration and Project Management
The platform supports user roles and permissions, task assignment, and tracking, as well as built-in commenting features for effective communication among team members. These features promote collaboration and ensure clear communication within the team.Customization
Labelbox offers flexibility through custom labeling templates, custom data metadata, and APIs/SDKs that allow users to extend the platform’s functionality and integrate it with their existing workflows and tools.User Experience
Overall, the user experience with Labelbox is positive, with many users appreciating its user-friendly interface and the efficiency it brings to the data labeling process. However, some users have noted a learning curve and occasional bugs, particularly with high-resolution images. Despite this, the support team is often praised for their quick assistance in resolving issues.Conclusion
In summary, Labelbox’s user interface is designed to be intuitive, efficient, and collaborative, making it a valuable tool for data annotation in machine learning projects. Labelbox - Key Features and Functionality
Labelbox Overview
Labelbox is a comprehensive data labeling platform that plays a crucial role in the development and training of machine learning models. Here are the main features and functionalities of Labelbox, along with their benefits and how AI is integrated into the product.Data Import and Management
Labelbox allows users to easily import their data from various sources, including cloud storage providers like Google Cloud Storage, Amazon S3, and Microsoft Azure Blob Storage, as well as data warehouses such as Google Big Query, Databricks, and Snowflake.Benefits
This feature streamlines the data ingestion process, reducing the time and effort required to set up and manage datasets.Labeling and Annotation
The platform supports a wide range of labeling tasks, including image classification, object detection, semantic segmentation, text classification, and more. Users can define labels and annotation types, and apply these labels to their data using a user-friendly interface.Benefits
This ensures that data is accurately and efficiently annotated, which is crucial for training high-quality machine learning models.Collaboration and Task Management
Labelbox offers advanced collaboration features, such as user roles and permissions, task assignment and tracking, and commenting and communication tools. Project managers can assign specific labeling tasks to annotators, track progress, and manage workloads effectively.Benefits
These features enhance team collaboration, ensure clear communication, and help in distributing the labeling workload efficiently.Review and Feedback
The platform includes a review process where project managers or designated reviewers can examine and provide feedback on labeled data. This helps in identifying and rectifying any labeling errors or inconsistencies.Benefits
This ensures high-quality labels and consistency across the project, which is essential for reliable model training.Consensus and Majority Voting
In cases where multiple annotators label the same data instance, Labelbox supports consensus and majority voting mechanisms to determine the final label. This helps mitigate potential labeling discrepancies and improves label quality.Benefits
This feature ensures that the most accurate label is chosen, enhancing the overall quality of the labeled data.Labeling Guidelines and Instructions
Project managers can define and provide explicit labeling guidelines and instructions to annotators. This ensures consistency and accuracy in labeling across the project.Benefits
Clear guidelines help in maintaining uniformity in labeling, which is vital for the reliability of machine learning models.Integration with Machine Learning Models
Labelbox offers seamless integration with machine learning models. Features include model-assisted labeling, where models suggest labels to annotators, and model validation, where model predictions are evaluated against the labeled data.Benefits
These integrations accelerate the labeling process and help in assessing and fine-tuning the performance of machine learning models.No-Code Data Pipeline Integrations
Labelbox provides a no-code data pipeline integration tool powered by Census, allowing users to connect data pipelines in less than five minutes. This tool supports over 25 data storage options and reduces the time and cost associated with data management.Benefits
This feature simplifies the process of synchronizing data between multiple data stores, lowering compute costs and reducing the need for custom Python scripts.Data Visualization and Curation
Labelbox enhances data exploration and curation with features like natural language search filters, masonry layout for thumbnails, and the ability to share filtered views of datasets. Users can sort data rows based on various metadata tags and see detailed information alongside thumbnails.Benefits
These features make it easier to explore, curate, and share datasets, improving the overall efficiency of data preparation and model training.AI Integration
Labelbox leverages AI in several ways, such as model-assisted labeling, where AI models suggest labels to annotators, and model validation, where AI model predictions are evaluated against labeled data. This integration accelerates the labeling process and improves the accuracy of machine learning models.Benefits
AI integration streamlines the data labeling process, enhances label quality, and supports the development of more accurate and reliable machine learning models.Conclusion
In summary, Labelbox is a powerful tool that streamlines data labeling, management, and integration, while leveraging AI to enhance the efficiency and accuracy of machine learning model development. Its features are designed to support a wide range of AI tasks and ensure high-quality data preparation, which is essential for building reliable and effective AI models. Labelbox - Performance and Accuracy
Evaluating Performance and Accuracy of Labelbox
Evaluating the performance and accuracy of Labelbox in the AI-driven data tools category involves a close look at several key aspects of their platform and methodologies.
Precision and Inter-Rater Agreement
Labelbox places a strong emphasis on precision metrics, particularly inter-rater agreement (IRA), to ensure consistency among labelers. They use Krippendorff’s Alpha, a versatile metric that handles various data types and missing data effectively. This metric ranges from -1 to 1, with higher values indicating better agreement among raters.
Accuracy Metrics
Accuracy is measured against ground truth data, often created through consensus and verified by highly trusted humans. Labelbox uses metrics such as the accuracy score, mean absolute error (MAE), and F1 score. The accuracy score is the proportion of correct responses compared to the ground truth, while MAE measures the average absolute difference between predicted and actual values. The F1 score balances precision and recall, particularly useful in classification tasks.
Real-Time Monitoring and Quality Control
Labelbox’s platform includes real-time monitoring and analysis tools, such as Labelbox Monitor, which provides a centralized dashboard to visualize and analyze data labeling operations. This tool helps identify outliers, track progress, and ensure deadlines are met. It also enables data-driven decisions through interactive charts and graphs, and simplifies management by allowing bulk actions and role management.
Adaptive Quality Management
Labelbox employs adaptive quality management strategies, including multi-step review processes and LLM-assisted quality control. These strategies help maintain high standards by continuously monitoring data quality and allowing immediate interventions and adjustments. This approach ensures that AI teams can quickly address quality issues and provide instant feedback to AI trainers, fostering rapid improvement.
Operational Efficiency
The platform is designed to balance quality with cost-effectiveness. Features like model diagnostics, cataloging, and model-assisted labeling help teams prioritize the most valuable data for labeling, reducing the time and resources spent on labeling data that doesn’t significantly impact model accuracy.
Limitations and Areas for Improvement
Despite its strong features, Labelbox faces several challenges that could impact its performance and accuracy:
- Rapid Technological Advancements: The AI industry is highly dynamic, and Labelbox must continuously innovate to stay competitive.
- Increasing Competition: With more AI platforms emerging, Labelbox needs to differentiate itself and maintain its market position.
- Data Privacy and Security: Ensuring compliance with data protection regulations and implementing robust security measures is crucial to safeguard user data.
- Scaling Operations: As Labelbox grows, it must streamline its processes and optimize resources to support its expanding customer base and offerings.
- Talent Acquisition and Retention: Attracting and retaining skilled professionals in the competitive AI industry is essential for maintaining high-quality services.
Human-Centric Evaluations
Labelbox incorporates expert human judgment to complement traditional benchmarks, providing a more accurate assessment of AI models. This human-centric approach helps address challenges around current benchmarks and ensures comprehensive metrics for various AI modalities.
Conclusion
In summary, Labelbox’s performance and accuracy are supported by a scientific approach to data quality, real-time monitoring, and adaptive quality management. However, the company must address ongoing challenges related to technological advancements, competition, data privacy, scaling operations, and talent management to maintain its position in the market.
Labelbox - Pricing and Plans
Labelbox Pricing Overview
Labelbox, a platform for AI data annotation and model building, offers a structured pricing model with several plans to cater to different user needs. Here’s a breakdown of the available plans and their features:
Free Plan
- This plan is ideal for individuals or small teams exploring and preparing datasets.
- Features include:
- Essential labeling platform
- Data curation with natural language search
- Multimodal data annotation
- Limited to up to 30 users, 50 projects, and 25 ontologies
- Community support
- As of February 1, 2024, the Free plan includes 500 free Labelbox Units (LBUs) per month across all Labelbox products.
Starter Plan
- This plan is suited for teams managing larger dataset projects with access to advanced features.
- Includes everything in the Free plan, plus:
- Unlimited users, projects, and ontologies
- Custom labeling and review workflows
- Project performance dashboard
- Model-assisted labeling with foundation models
- Optional on-demand labeling services
- Customer support portal access
Enterprise Plan
- Designed for frontier model builders and enterprise AI teams needing high-quality human data quickly.
- Features include:
- Fully-managed human data services
- Quality guarantee (SLA)
- Full access to the Labelbox platform and tools
- Dedicated technical support and contact channels
- Custom pricing options and volume discounts available for annual commitments of LBU consumption
Labelbox Unit (LBU) Pricing
As of February 1, 2024, Labelbox introduced a new pricing structure based on LBUs:
- Annotate: One-time charge of one LBU per data row for common data types.
- Model: One-time charge of one LBU per five data rows for common data types, reducing costs by up to 80%.
- Catalog: Monthly cost of one LBU per sixty data rows for common data types.
- The cost calculation for all monthly subscription plans is based on a flat rate of $0.10 per LBU.
Additional Notes
- Customers on paid plans no longer have access to free LBUs.
- Volume discounts are available for customers with an annual commitment of LBU consumption on the Enterprise plan.
- Additional charges may include costs for Labelbox Boost or other add-ons.
This structure aims to provide clarity and transparency, making it easier for users to forecast their costs and manage their data more efficiently.
Labelbox - Integration and Compatibility
No-Code Data Pipeline Integrations
Labelbox has introduced a new data warehouse integration tool powered by Census, which allows users to connect data pipelines without writing any code. This tool supports over 25 data storage options, including major data warehouses like Google BigQuery, Databricks, Snowflake, and Amazon Redshift, as well as simpler data sources like Google Sheets and various cloud storage providers such as Google Cloud Storage, Amazon S3, and Microsoft Azure Blob Storage. This integration reduces the time and cost associated with data management, enabling data engineers to build data pipelines in less than five minutes.Advanced APIs and SDKs
For developers, Labelbox provides advanced APIs and SDKs that integrate smoothly with existing pipelines. The platform includes a Python SDK that allows for automation of actions and integration into existing workflows. This open-source approach helps in automating tasks and ensuring strong security.Multi-Source Data Import
Labelbox allows users to import data from multiple sources without the need for coding. This feature supports a wide array of data types, including images, videos, text, PDF documents, tiled geospatial data, medical imagery, and audio data. The platform’s flexibility in handling various data types makes it adaptable to different AI challenges.Custom Model Integration
Users can now seamlessly integrate their custom models into the Labelbox platform through a self-serve, product-integrated user interface. This feature supports models such as LLMs, classification models, text analysis, and NER, enhancing prediction tasks, model evaluation, and data enrichment. This integration also works within Labelbox’s Foundry and GenAI multimodal chat editor, allowing for comprehensive model evaluations and comparisons.Collaboration and Quality Assurance
Labelbox enhances collaboration among teams through powerful communication and collaboration tools. It provides real-time project status updates and allows teams to collaborate on labeling issues in real time. The platform also includes comprehensive quality control features, such as real-time analytics, benchmarking, and consensus scoring, to ensure high data quality at scale.Compatibility and Security
The platform ensures strong security and supports flexible synchronization options, including full support for upserts to mirror new data creation, updates, and deletes in Labelbox Catalog datasets. This ensures that data is accurately synchronized across different platforms and devices, maintaining data integrity and security.Conclusion
In summary, Labelbox offers extensive integration capabilities, supporting a wide range of data sources and tools, and ensuring seamless collaboration and high data quality across various platforms. Labelbox - Customer Support and Resources
Customer Support
Labelbox provides a dedicated Help Center that serves as a central hub for customer support. Here, users can find answers to common questions, troubleshoot issues, and access various resources. The Help Center includes announcements, FAQs, and a login portal for existing customers to submit support requests directly. For more specific needs, Labelbox encourages customers to reach out through the application itself by clicking on the Labelbox or Alignerr logo. This ensures that support requests are handled efficiently and effectively.Additional Resources
Documentation and Guides
Labelbox offers extensive documentation that covers a wide range of topics, including how to label data, fine-tune large language models (LLMs), and integrate data from various cloud sources. These resources help users generate high-quality labels and curate data efficiently.Resource Library
The Labelbox Resource Library is a valuable asset for users, providing expert educational resources from AI practitioners and industry thought-leaders. This library includes guides on improving MLOps, building a data engine, and strategies for reaching production AI. Users can find resources on generating high-quality data, automating data tasks with foundation models, and unlocking new generative AI capabilities.Specific Guides and Tutorials
Labelbox provides detailed guides on specific topics such as analyzing customer reviews and improving customer care with natural language processing (NLP). These guides walk users through the process of building NLP models to explore review topics, classify review sentiment, and make data-driven business decisions.Data Labeling Services
Labelbox connects users with specialized labelers who can annotate and review large volumes of data. This service supports tasks like reinforcement learning from human feedback (RLHF), supervised fine-tuning, and multimodal LLM evaluation. The platform also supports multiple natural languages, making it versatile for global operations.Model Training and Evaluation
Users can benefit from Labelbox’s environment for training models 4X faster, which includes tools for comparing models, diagnosing errors, and performing robust error analysis. This helps in ensuring accurate AI outcomes and aligning models with human-centric evaluations. By leveraging these resources, users can effectively engage with Labelbox’s AI-driven tools, enhance their data quality, and improve their overall customer care initiatives. Labelbox - Pros and Cons
Advantages of Labelbox
Labelbox offers several significant advantages that make it a strong choice in the data tools and AI-driven product category:
User-Friendly Interface and Versatility
Labelbox provides a user-friendly interface that is easy to use, even for those who are new to annotation tools. It supports a wide range of annotation types, including bounding boxes, polygons, lines, and keypoints, catering to various annotation requirements.
Project Management and Collaboration
The platform offers robust project management features that enable seamless team collaboration. Teams can assign specific labeling jobs, track progress, and ensure consistent quality across annotations. This streamlined workflow enhances efficiency and facilitates effective project management.
AI-Assisted Labeling
Labelbox leverages AI to automate repetitive and time-consuming tasks such as pre-labeling or generating initial annotations. This AI assistance significantly speeds up the labeling process, reduces manual labor, and allows users to handle large datasets more efficiently.
High-Quality Data
Labelbox is known for delivering high-quality data through its real-time statistical analysis capabilities and a data quality guarantee. This ensures that the data aligns with the user’s specifications for precision and accuracy.
Rapid Data Delivery
With a network of highly-skilled labelers and industry-leading software, Labelbox can generate new human data on-demand. This includes automation, AI, and best practices for human data capture, which boosts efficiency and reduces turnaround times.
Integration and Scalability
Labelbox offers integration options with other tools and workflows, enhancing its compatibility with existing systems. It is also designed to handle large datasets, providing scalability for projects of varying sizes.
Disadvantages of Labelbox
While Labelbox has many advantages, there are also some notable disadvantages to consider:
Cost
Labelbox is a paid tool, and its pricing structure may not be suitable for all budgets. Smaller organizations or projects with budget constraints may find it challenging to afford Labelbox’s services.
Internet Connectivity Dependency
Being a cloud-based platform, Labelbox requires a stable internet connection to access and use the tool. This can be a limitation in scenarios where internet connectivity is inconsistent or restricted.
Learning Curve
For users unfamiliar with its features, Labelbox may have a learning curve that could affect initial onboarding and project timelines.
Customization Limitations
Although versatile, some users may find limitations in terms of customization options for specific annotation tasks.
By weighing these advantages and disadvantages, users can make an informed decision about whether Labelbox is the right fit for their specific needs and project requirements.
Labelbox - Comparison with Competitors
When Comparing Labelbox to Other AI-Driven Data Labeling and Annotation Tools
Several key differences and unique features emerge.Labelbox Unique Features
Labelbox stands out for its comprehensive approach to building an AI data factory. Here are some of its distinctive features:Transparent Labeling Platform
Labelbox offers a clear and transparent platform for data labeling, which is crucial for maintaining data quality and integrity.Expert Labeling Services
It provides access to highly skilled labelers, ensuring high-quality data output. This is combined with proven expertise and processes to maintain quality standards.Model-Assisted Labeling
Labelbox uses AI to assist in labeling, including auto-labeling, visual data curation, and the ability to create custom workflows. This significantly reduces the time and cost associated with data preparation.Alternatives and Comparisons
Labellerr
Labellerr is a strong alternative that specializes in AI-assisted labeling and quality management. Here’s how it compares:Specialization
Labellerr focuses intensely on AI-assisted labeling, using advanced algorithms to ensure precision and reduce data preparation time.Flexibility
It offers a flexible annotation platform that supports various data types, including images, videos, PDFs, text, and audio, making it versatile for different project needs.Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth is another option that provides labeling services integrated with Amazon’s ecosystem.Integration
It benefits from seamless integration with other Amazon Web Services (AWS) tools, making it a good choice for those already using AWS.Scalability
Ground Truth allows for large-scale labeling operations, leveraging Amazon’s infrastructure for scalability.Dataloop
Dataloop is known for its comprehensive data annotation platform.Customization
Dataloop offers strong customization options, allowing users to tailor the platform to their specific needs.Data Types
It supports a wide range of data types, similar to Labellerr, making it suitable for diverse projects.Scale AI
Scale AI is another competitor in the data labeling space.Labeling Services
While Scale AI provides labeling services, it lacks the comprehensive data factory approach that Labelbox offers, which includes transparent labeling, expert services, and proven processes.Scalability
Scale AI is known for its ability to scale labeling operations, but it may not offer the same level of AI-assisted labeling features as Labelbox.V7 Darwin
V7 Darwin focuses on AI-assisted annotation with a user-friendly interface.User Interface
V7 Darwin is praised for its intuitive interface, making it easier for users to annotate data without a steep learning curve.AI Assistance
It uses AI to assist in the annotation process, though it may not be as extensive as Labelbox’s model-assisted labeling features.Prodigy
Prodigy is a tool from spaCy, known for its flexibility in data annotation.Customization
Prodigy offers high customization, allowing users to create bespoke annotation workflows. However, it lacks the built-in AI assistance and expert labeling services that Labelbox provides.Conclusion
Each of these alternatives has its own strengths and weaknesses. Labelbox stands out for its comprehensive approach to AI data factories, expert labeling services, and advanced AI-assisted labeling features. However, depending on specific needs such as flexibility, customization, or integration with other services, alternatives like Labellerr, Amazon SageMaker Ground Truth, Dataloop, Scale AI, V7 Darwin, or Prodigy might be more suitable. It’s important to evaluate these options based on your project’s unique requirements. Labelbox - Frequently Asked Questions
Frequently Asked Questions about Labelbox
How flexible is the Labelbox labeling platform?
Labelbox offers a highly flexible labeling platform that caters to a wide range of machine learning tasks. It provides user-friendly labeling tools suitable for both technical and non-technical team members. The platform allows for the easy creation of customized templates, supporting tasks from simple categorization to complex annotation, including RLHF, multimodal chat arenas, audio, video, documents, and custom HTML templates.Are there limitations on task complexity?
Labelbox can handle tasks of any complexity. Whether you need quick image tagging or in-depth content analysis that takes hours per item, the platform is designed to accommodate your requirements. It has successfully managed projects ranging from summarizing lengthy articles to coding complex algorithms.What sets Labelbox’s labeling services apart?
Labelbox combines advanced technology with a diverse, highly skilled workforce proficient in over 30 languages. The platform employs an in-house team of talented individuals from various backgrounds, including researchers, educators, and industry professionals. Additionally, Labelbox operates Alignerr, a large network of AI experts, ensuring superior quality and cost-effectiveness for your data labeling needs.How do you match labelers to specific projects?
Labelbox uses a sophisticated matching system that aligns labelers’ skills and experiences with project requirements. This ensures that your data is handled by individuals with relevant domain knowledge and experience. For example, financial data is handled by those with financial knowledge, and technical projects are assigned to individuals with appropriate STEM backgrounds.Do you offer a fully managed option?
Yes, Labelbox provides a comprehensive managed service for clients with more intricate needs. This includes assistance with guideline development, custom team assembly, quality control design, and dedicated project management with round-the-clock support.What quality assurance measures do you employ?
Labelbox employs a multifaceted quality control system that includes the use of gold standard datasets, advanced outlier detection, AI-augmented labeling, and multi-tier review processes. These measures ensure high-quality output and help meet and exceed client quality expectations.Do you support real-time labeling tasks?
Labelbox understands the importance of quick turnarounds and offers real-time labeling capabilities. The platform can deliver results within seconds or minutes when needed and provides a 24/7 labeling workforce solution for projects with strict time constraints.Do you offer API access?
Yes, Labelbox provides a robust API that allows seamless integration with your existing workflows. You can programmatically manage projects, monitor progress, and retrieve results, all while maintaining compatibility with the user-friendly interface.How secure is my data with Labelbox?
Labelbox ensures that your data remains secure. The platform does not use customer data to power proprietary models, and you can integrate your cloud buckets via IAM Delegated Access to keep your raw data in your own cloud storage service. Additional security measures such as Single Sign On (SSO) and HIPAA compliance are available for Standard and Enterprise tiers.Does Labelbox offer a free plan?
Yes, Labelbox offers a free plan with limited features. In addition to the free plan, they also provide premium plans, including an Enterprise Plan, which can be customized based on specific needs.