Top 6 Data Labeling Tools To Use In 2023
Data Labeling is adding metadata or tags to a dataset to make it more useful for machine learning applications. The goal is to provide the machine learning algorithm with accurate and relevant information that it can use to learn from and make predictions. Data labeling is essential because it allows machine learning algorithms to understand and make sense of the data they receive.
Different types of data labeling depend on the type of data being labeled. For example, text data can be labeled for sentiment analysis or named entity recognition, while image data can be labeled for object detection or semantic segmentation.
Moreover, the quality of labeled data directly affects the machine learning algorithm's performance. If the labeled data is inaccurate, incomplete, or inconsistent, the model cannot learn from it effectively, resulting in poor performance.
Data labeling can be time-consuming and expensive, depending on the size and complexity of the dataset. Therefore, it is essential to use high-quality data labeling tools and processes to ensure accurate and efficient labeling.
In this article, we will review 6 data labeling tools to help you make your data labeling task faster, more efficient, and more accurate.
№1: Labelbox
Labelbox is a popular data labeling platform that allows teams to manage, annotate, and collaborate on data. It offers a user-friendly interface for labeling images, text, and video data. Labelbox supports annotation types, including bounding boxes, polygons, and classifications. It also provides built-in quality control tools and integrates well with popular machine learning frameworks such as TensorFlow and PyTorch.
Labelbox offers a range of annotation tools for images, including object detection, semantic segmentation, and image classification. Its text annotation tools include named entity recognition, sentiment analysis, and text classification. The platform also supports video annotation, including object tracking and action recognition.
One of the key features of Labelbox is its collaborative labeling capabilities. Multiple users can work on the same dataset simultaneously, and changes are synced in real-time. The platform also supports project management features such as task assignments, deadlines, and progress tracking.
Labelbox offers both a cloud-based and on-premise solution, making it a flexible choice for businesses of all sizes. In addition, the platform provides a free version with limited features, making it accessible to individual users and small teams.
№2: Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth is a fully managed data labeling service that Amazon Web Services (AWS) provides. It offers a scalable and secure platform for labeling data using human annotators or machine learning models. SageMaker Ground Truth supports various data types, including images, text, and audio. It also integrates well with other AWS services.
SageMaker Ground Truth offers a range of annotation tools for images, including bounding boxes, polygons, and semantic segmentation. Its text annotation tools include named entity recognition, sentiment analysis, and text classification. The platform also supports video annotation, including object tracking and action recognition.
One of the key features of SageMaker Ground Truth is its ability to integrate with AWS machine learning services such as SageMaker and Rekognition. This allows users to quickly and easily train machine learning models quickly and easily using their labeled data. The platform also provides automatic quality control and validation tools to ensure accurate labeling.
SageMaker Ground Truth is a flexible choice for businesses of all sizes, offering pay-as-you-go pricing and a free tier for small projects.
№3: SuperAnnotate
SuperAnnotate is an AI-powered data labeling platform that offers a range of annotation tools for images and videos. The platform supports annotation types, including bounding boxes, polygons, and instance segmentation. In addition, it provides a user-friendly interface with advanced features like automatic labeling and quality control tools. SuperAnnotate also supports collaborative labeling and project management.
SuperAnnotate offers a range of annotation tools for images, including object detection, semantic segmentation, and image classification. Its video annotation tools include object tracking and action recognition.
One of the key features of SuperAnnotate is its AI-powered automatic labeling feature. The platform uses machine learning models to suggest annotations, reducing the time and effort required for manual labeling. SuperAnnotate also provides advanced quality control and validation tools to ensure accurate labeling.
SuperAnnotate offers a cloud-based solution, making it a flexible choice for businesses of all sizes. The platform provides a free version with limited features, making it accessible to individual users and small teams.
№4: Prodigy
Prodigy is a powerful data labeling tool designed for machine learning workflows. In addition, it offers a range of annotation tools for text, images, and audio data, making it a versatile choice for various projects.
One of the unique features of Prodigy is its active learning capabilities. It uses machine learning models to suggest annotations, allowing users to focus on challenging cases that require human input. This approach can help speed up the annotation process and improve the annotated data quality.
Prodigy supports annotation types, including entity recognition, classification, and image segmentation. It also offers a user-friendly interface with customizable workflows and keyboard shortcuts. The platform also supports collaborative labeling, allowing multiple users to simultaneously work on the same dataset.
Prodigy offers both a cloud-based and on-premise solution, making it a flexible choice for businesses of all sizes. It also integrates with popular machine learning frameworks such as spaCy and PyTorch. It is an excellent choice for businesses looking for a data labeling tool with advanced active learning capabilities. In addition, its range of annotation tools, customizable workflows, and collaborative capabilities can help streamline the data labeling process and improve the accuracy of annotated data.
№5: Hasty.ai
Hasty.ai is a popular data labeling tool that offers a range of annotation tools for image and video data. It is designed to make the data labeling process more efficient and accurate, with a user-friendly interface and advanced features.
Hasty.ai supports annotation types, including bounding boxes, polygons, and semantic segmentation. It also provides automatic labeling features powered by machine learning, which can reduce the time and effort required for manual labeling. Additionally, Hasty.ai offers quality control tools to ensure accurate labeling.
The platform's user interface is designed to be intuitive and easy to use, with drag-and-drop functionality and keyboard shortcuts. It also supports collaborative labeling, allowing multiple users to work on the same dataset simultaneously.
Hasty.ai offers both a cloud-based and on-premise solution, making it a flexible choice for businesses of all sizes. The platform also integrates with popular machine learning frameworks such as TensorFlow and PyTorch. Hasty.ai is an excellent choice for businesses seeking a user-friendly and efficient data labeling tool. Its range of annotation tools, automatic labeling features, and collaborative capabilities can help streamline the data labeling process and improve the accuracy of annotated data.
№6: UBIAI
UBIAI is a comprehensive platform with the goal of making easy-to-use NLP tools to help developers and companies try out machine learning ideas quickly and apply them to real-world problems without wasting time coding.
UBIAI offers document annotation with various features, such as: support of over 20+ languages for NER, Relation extraction, and document classification tasks, the ability to create and train models for automated annotation using spacy and transformer models, and support for OCR annotation and object detection to label native PDFs, scanned images and pictures.
Moreover, it allows you to perform shot labeling using the GPT model. It also has great API and Inter-annotator agreement support.
Finally, UBIAI offers a comprehensive collaboration feature to manage and track team progress and performance, including collaboration task assignment, task validation, automatic task assignment, inter-annotator agreement evaluation, role assignment for team members, and viewing active time per document and average document annotation time per collaborator.
Final Thoughts
Data labeling is a critical step in the machine learning workflow that directly affects the accuracy and performance of the final model. Accurate and consistent labeling can improve the performance of machine learning algorithms and make them more useful for real-world applications.
Data labeling tools are essential in the machine learning workflow by simplifying and streamlining the annotation process. The tools in this article are popular data labeling tools offering various features to help teams manage and annotate data efficiently. Their user-friendly interfaces, collaborative capabilities, and advanced annotation tools make them ideal for businesses of all sizes looking to streamline their data labeling processes.
So, if you just started a new Data Science project, try one or more of the data labeling tools in this article!
