Top 10 Data Labeling Tools for Machine Learning

One of the biggest challenges faced by machine learning practitioners is the labor-intensive and time-consuming process of data labeling.

In fact, according to a 2024 report by Rand, up to 80% of AI project time is spent on data preparation and labeling, with companies allocating 25% to 50% of their total project budgets to labeling efforts.

This bottleneck often leads to project delays and compromised model performance due to mislabeled or incomplete datasets.

Additionally, manual labeling can introduce inconsistencies and biases, significantly impacting the accuracy of machine learning models, particularly in applications like computer vision and natural language processing.

To solve this problem, data labeling tools offer automated and AI-assisted solutions. These tools not only streamline the labeling process but also ensure that high-quality, consistent data is provided for training models.

By leveraging these platforms, data labeling becomes faster, more scalable, and less error-prone, which directly contributes to improved model performance and faster time-to-market for AI solutions.

If you are a machine learning practitioners, data scientists, AI engineers, project managers, and businesses, looking to optimize their data labeling processes for faster and more accurate machine learning model development you can take a quick overview of the tools.

You might ask - "You gave me the list of best data labeling tools but I want to more, I want to know more about what exactly is data labeling?" Let's discuss that.

What is Data Labeling?

Data labeling is the process of tagging or annotating raw data, such as images, videos, audio, text, or 3D data, with meaningful labels to train machine learning (ML) models.

These labels provide context, making it easier for the ML algorithms to understand and recognize patterns.

For example, in computer vision, data labeling involves identifying objects in images and tagging them (e.g., labeling a car in a picture). It forms the foundation for supervised learning, where models learn by example from labeled data.

Importance of Data Labeling in Machine Learning

Accurate data labeling is critical for the performance of ML models. The quality of labeled data directly impacts how well the model can make predictions and generalize to new data.

Without high-quality, accurate labels, ML models may struggle to deliver precise outputs, whether they are used for image recognition, natural language processing (NLP), or autonomous driving systems.

Effective data labeling not only improves accuracy but also helps reduce bias, ensuring fair and reliable predictions across various use cases.

Here is the list of best data labeling tools you can use.

1. Labellerr

Top Features:

• AI-powered annotation
• Multi-format support
• Customizable workflows

Pros:

• Easy-to-use interface
• Scalable for various projects
• Highly customizable

Cons:

• Limited integrations with other tools
• Requires initial training to maximize effectiveness

Pricing:

• Researcher Plan: You can use Labellerr for free, with up to 2,500 data credits and 1 seat.
• Pro Plan: $499/mo
• Enterprise Plan: Custom Pricing Contact Sales

G2 Reviews Summary: Praised for its simplicity and scalability, making it suitable for diverse use cases.

2. SuperAnnotate

Top Features:

• 2D/3D annotation capabilities
• AI-assisted tools
• Project management features

Pros:

• Strong tools for 3D annotation
• Scalable for large teams
• Automation features available

Cons:

• Pricing can be steep for small teams
• Learning curve for new users

Pricing: Custom pricing available

G2 Reviews Summary: Users commend its advanced 3D capabilities.

3. Labelbox

Top Features:

• Workflow automation
• Multi-format support
• Collaboration tools

Pros:

• Scalable and user-friendly
• Offers advanced analytics

Cons:

• Can be expensive for small teams
• Limited features in the free version

Pricing: Free plan available; enterprise pricing options

G2 Reviews Summary: Highly regarded for ease of use and efficiency with large datasets.

4. Scale AI

Top Features:

• AI and human-in-the-loop collaboration
• API integration
• Scalable solutions for data processing

Pros:

• High accuracy in data labeling
• Scalable to various industries

Cons:

• Expensive compared to alternatives
• Limited customization options

Pricing: Custom pricing

G2 Reviews Summary: Known for delivering high-quality results, albeit at a higher cost.

5. Dataloop

Top Features:

• End-to-end platform for data management
• Real-time feedback capabilities
• Automation features

Pros:

• Integrated with AI models
• Highly customizable to user needs

Cons:

• Generally expensive
• Steep learning curve for new users

Pricing: Custom pricing

G2 Reviews Summary: Appreciated for its automation and real-time feedback features.

6. Amazon SageMaker Ground Truth

Top Features:

• Pre-built workflows
• Human-in-the-loop annotation
• Integration with AWS services

Pros:

• Excellent AWS integration
• Highly scalable and accurate

Cons:

• Complex setup within the AWS ecosystem
• Limited functionality outside of AWS

Pricing: Pay-as-you-go model

G2 Reviews Summary: Recognized for seamless integration with AWS services.

7. CVAT

Top Features:

• Open-source annotation tool
• Supports multiple file formats
• Advanced labeling tools

Pros:

• Free to use
• Flexible and versatile for various projects

Cons:

• Limited collaboration features
• Lacks automation capabilities

Pricing: Free

G2 Reviews Summary: Valued for its flexibility and robust computer vision tools at no cost.

8. V7 Labs

Top Features:

• AI-assisted video labeling
• Real-time collaboration features
• Automation tools

Pros:

• Excellent for video annotation tasks
• Strong collaboration and automation capabilities

Cons:

• Pricey for small teams

Pricing: $300/month; custom pricing available

G2 Reviews Summary: Powerful features, but can be expensive for smaller teams.

9. Diffgram

Top Features:

• Full-stack capabilities (annotation, training, evaluation)
• Automation features

Pros:

• Customizable and flexible
• Good collaboration options

Cons:

• Has a learning curve
• Limited features in the free version

Pricing: Free tier available; custom pricing

G2 Reviews Summary: Offers flexible and comprehensive capabilities for users.

10. Clarifai

Top Features:

• AI lifecycle management
• Custom model creation
• Supports multiple formats

Pros:

• End-to-end AI solution
• Scalable and automation-friendly

Cons:

• Complex pricing structure
• Limited customization outside its ecosystem

Pricing: Free tier available; custom pricing

G2 Reviews Summary: Strong performance in AI training, though pricing can be complicated.

Conclusion

Each of the top 10 data labeling tools brings its own unique strengths to the table. Labellerr stands out for its AI-powered annotation, scalability, and ease of use, making it an ideal choice for teams looking for automation and customization.

FAQs

1. What is data labeling in machine learning?

Data labeling involves annotating or tagging datasets to help machine learning models learn from the data. It’s essential for supervised learning tasks, where labeled data is used to train algorithms.

2. Why is data labeling important?

Data labeling improves the accuracy and performance of machine learning models by providing context and information. Properly labeled data helps models understand patterns and make predictions.

3. Are these tools suitable for all industries?

Most tools are versatile and can be adapted for various industries, including healthcare, automotive, finance, and retail. However, some tools may have features tailored for specific use cases.

4. Do I need technical expertise to use these tools?

While many tools are designed to be user-friendly, some may require technical knowledge or training, especially those with advanced features or customizations.