Data labeling is important for training machine learning models, which are used to make decisions based on patterns and trends in the data.
Let’s see what this data labeling is all about and the various tools to perform it.
What is Data Labeling?
Data labeling is the process of assigning descriptive tags or labels to data to help identify & categorize it. It involves various types of data, such as text, images, videos, audio, and other forms of unstructured data. The labeled data is then used to train machine learning algorithms to identify patterns and make predictions.
The accuracy and quality of the labeling can greatly impact the performance of the ML models. It can be done manually by humans or with the help of automation tools. The main purpose of data labeling is to transform unstructured data into a structured format that can be easily understood and analyzed by machines.
A good example of data labeling could be in the context of image recognition. Let’s say you want to train a machine-learning model to recognize cats and dogs in images.
In order to do so, First, you would need to label a set of images as either “cat” or “dog” so that the model can learn from these labeled examples. The process of assigning these labels to the images is called data labeling.
An annotator would view each image and manually assign the appropriate label to it, creating a labeled dataset that can be used to train the machine learning model.
How does it work?
There are various steps involved in performing data labeling. This include:
Data collection
The first step in the data labeling process is to collect the data that needs to be labeled. This can include a variety of data types, such as images, text, audio, or video.
Labeling guidelines
As soon as the data is gathered, labeling guidelines are created that specify the labels or tags that will be assigned to the data. These guidelines help to ensure that the labeled data is relevant to the current ML activity and maintain consistency in labeling.
Annotation
The actual labeling of the data is done by annotators or labelers who are trained to apply the labeling guidelines to the data. This can be done manually by humans or through automated processes using pre-defined rules & algorithms.
Quality control
Quality control measures are put in place to improve the accuracy of the labeled data. This includes the IAA metric, where multiple annotators label the same data, and their labeling is compared for consistency and quality assurance checks to correct labeling errors.
Integration with machine learning models
Once the data has been labeled and quality control measures have been implemented, the labeled data can be integrated with machine learning models to train and improve their accuracy.
Different approaches to data labeling
Data labeling can be done in a variety of ways, each with its own benefits and drawbacks. Some common methods include:
#1. Manual labeling
This is the traditional technique of labeling data in which individuals manually annotate data. The data is reviewed by the annotator, who then adds labels or tags to it in accordance with standard procedures.
#2. Semi-supervised labeling
It is a combination of manual and automated labeling. A smaller portion of the data is manually categorized, and the labels are then used to train a machine-learning model that can automatically label the remaining data. This approach might not be as accurate as manual labeling, but it is more efficient.
#3. Active learning
This is an iterative approach to data labeling where the machine learning model identifies the data points that it is most uncertain about and asks a human to label them.
#4. Transfer learning
This method uses pre-existing labeled data from an activity or domain that is related to training a model for the current task. When the project doesn’t have enough labeled data, this method might be helpful.
#5. Crowdsourcing
It involves outsourcing the labeling task to a large group of people through an online platform. Crowdsourcing can be a cost-effective way to label large amounts of data quickly, but it can be difficult to verify accuracy and consistency.
#6. Simulation-based labeling
This approach involves using computer simulations to generate labeled data for a particular task. It can be useful when real-world data is difficult to obtain or when there is a need to generate large amounts of labeled data quickly.
Each method has its own strengths and weaknesses. It depends on the specific requirements of the project and the goals of the labeling task.
Common types of data labeling
- Image labeling
- Video labeling
- Audio labeling
- Text labeling
- Sensor labeling
- 3D labeling
Different types of data labeling are used for different types of data and tasks.
For example, image labeling is commonly used for object detection, while text labeling is used for natural language processing tasks.
Audio labeling can be used for speech recognition or emotion detection, and sensor labeling can be used for Internet of Things (IoT) applications.
3D labeling is utilized for tasks such as autonomous vehicle development or virtual reality applications.
Best practices involved in data labeling
#1. Define clear guidelines
Clear guidelines should be established for labeling data. These guidelines should include definitions of the labels, examples of how to apply the labels, and instructions on how to handle ambiguous cases.
#2. Use multiple annotators
Accuracy can be improved when different annotators label the same data. Inter-annotator agreement (IAA) metrics can be used to assess the level of agreement between different annotators.
#3. Use a standardized process
A defined process should be followed for labeling data to ensure consistency across different annotators and labeling tasks. The process should include a review process to check the quality of labeled data.
#4. Quality control
Quality control measures like regular reviews, cross-checking, and data sampling are essential to ensure the accuracy and reliability of labeled data.
#5. Label diverse data
When selecting data to label, it is important to choose a diverse sample that represents the full range of data that the model will be working with. This can include data from different sources with different characteristics and covering a wide range of scenarios.
#6. Monitor and update labels
As the machine learning model improves, it may be necessary to update and refine the labeled data. It is important to keep an eye on its performance and update the labels as required.
Use Cases
Data labeling is a critical step in machine learning and data analysis projects. Here are some common use cases of data labeling:
- Image and video recognition
- Natural language processing
- Autonomous vehicles
- Fraud detection
- Sentiment analysis
- Medical diagnosis
These are just a few examples of the use cases for data labeling. Any application of machine learning or data analysis that involves classification or prediction can benefit from the use of labeled data.
There are many data labeling tools available on the internet, each with its own set of features and capabilities. And here, we have summed up a list of the best tools for data labeling.
Label Studio
Label Studio is an open-source data labeling tool developed by Heartex that provides a range of annotation interfaces for text, image, audio, and video data. This tool is known for its flexibility and ease of use.
It is designed to be quickly installable and can be used to build custom user interfaces s or pre-built labeling templates. This makes it easy for users to create custom annotation tasks and workflows using a drag-and-drop interface.
Label Studio also provides a range of integration options, including webhooks, a Python SDK, and API, which allows users to seamlessly integrate the tool into their ML/AI pipelines.
It comes in two editions – Community and Enterprise.
The Community edition is free to download and can be used by anyone. It has basic features and supports a limited number of users & projects. Whereas the Enterprise edition is a paid version that supports larger teams and more complex use cases.
Label box
Label box is a cloud-based data labeling platform that provides a powerful set of tools for data management, data labeling, and machine learning. One of the key advantages of Labelbox is its AI-assisted labeling capabilities which help to accelerate the data labeling process and improve labeling accuracy.
It offers a customizable data engine that is designed to help data science teams produce high-quality training data for machine learning models quickly and efficiently.
Key Labs
Keylabs is another excellent data labeling platform that offers advanced features and management systems to provide high-quality annotation services. Keylabs can be set up and supported on-premises, and user roles and permissions can be assigned to each individual project or platform access in general.
It has a track record of handling large datasets without compromising efficiency or accuracy. It supports various annotation features such as z-order, parent/child relationships, object timelines, unique visual identity, and metadata creation.
Another key feature of KeyLabs is its support for team management and collaboration. It offers role-based access control, real-time activity monitoring, and built-in messaging & feedback tools to help teams work together more effectively.
Existing annotations can also be uploaded onto the platform. Keylabs is ideal for individuals and researchers looking for a fast, efficient, and flexible data labeling tool.
Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth is a fully managed data labeling service provided by Amazon Web Services (AWS) that helps organizations build highly accurate training datasets for machine learning models.
It offers a variety of features, such as automatic data labeling, built-in workflows, and real-time workforce management, to make the labeling process faster and more efficient.
One of the key features of SageMaker is the ability to create custom workflows that can be tailored to specific labeling tasks. This can help reduce the time and cost required to label large amounts of data.
Additionally, it offers a built-in workforce management system that allows users to manage and scale their labeling tasks with ease. It is designed to be scalable and customizable, which makes it a popular choice for data scientists and machine learning engineers.
Conclusion
I hope you found this article helpful in learning about data labeling and its tools. You may also be interested in learning about data discovery to find valuable and hidden patterns in data.