Today, businesses have to go the extra mile to retain customers, attract new ones and remain competitive. Businesses can achieve this by studying customer behavior and patterns and tailoring individualized adverts. This is made possible by data labeling platforms because they turn available raw data into insightful and actionable intel.
Once raw data is fed into the system, machine learning enables the labeling software to label the data.
Numerous platforms in the market promise to deliver unmatched data labeling. Each of these platforms has its unique features, benefits and disadvantages. This piece highlights the top 10 data labeling platforms and their unique features.
Data Labeling Platform Explained
A data (image, audio, video and text) labeling platform is software that analyzes unlabeled data presented in any format. The software proceeds to label the identified data using a preset labeling technique. Data labeling software uses techniques such as bounding box, polyline, landmarking and named entity recognition to prepare high-quality data.
The available labeling platforms in the market vary in two primary ways. The first is based on the type of data supported, and the second is the data subset. You will come across labeling tools that support only one known data type or subsets. Labeling platforms tend to accommodate all, if not a majority, of the known data types. That means video, audio, image, text LIDAR and satellite imagery.
Data labelling takes various forms depending on the platform you choose. For instance;
- Platforms may use segmentation and object detection features for image data.
- Common features utilized in text data labeling are entity recognition and sentiment detection.
- Emotion detection or recognition and transcription are used in speech or audio annotation.
The main platforms for labeling fall under two major groups; price-based and function-based. These can be subdivided into other subcategories, such as open-source or closed-source, among others.
We considered the quality of labeling as the first criterion for choosing the best labeling solutions in the market. From this, we came up with a list of the 10 top-ranked labeling platforms you can consider.
What are the top 10 data labeling platforms?
Keylabs is top of the list due to its high precision in data labeling and image annotation. In addition, this platform’s video and image annotation tools have built-in machine learning and collaboration tools that allow higher quality and faster object tracking.
The platform also has a team of annotation specialists that can train your team to help you keep up with the best data labeling practices. Here are some of the other key features that make Keylabs ideal for annotating data sets:
- Supports annotation of all image and video types
- Segmentation of complex images using unique colors
- Video and image classification through single and multiple labeling techniques
- Object localization through shape interpolations of images
- Automatic annotation of high and low-resolution images
Keylabs is ideal if you have a team working on a data annotation project since it allows you to easily assign annotation assignments and track the team’s productivity in real time.
SageMaker Ground Truth from Amazon
Amazon is offering data labeling services through its Sagemaker Ground Truth platform. This is fully managed and can be used to simplify ML data creation. Through this platform, users are able to build training data with a high degree of accuracy. The platform also has built-in workflows to make data labeling fast and reliable.
The Sagemaker labeling solution combines 3D cloud point with video, audio, image and text formats. This labeling platform has the following key features;
- Ability to remove distortion in 2-dimension images
- Automatic 3-dimension cuboid snapping
- Snapping of automatic 3D cuboid
- Auto segmentation tools.
These unique features enable users to perform data labeling with speed and precision.
If speed is your portion, you want to try the Super Annotate data labeling platform. Super Annotate is an integrated data labeling tool that automates labeling different data types, especially in computer vision. The platform supports NLP, image and video labeling, and other data formats such as audio and LIDAR.
Its multi-level quality control allows different teams to collaborate in data labeling functions. This improves the overall model performance. The platform also supports active learning and smart predictions. These two features provide for greater precision in the generation of datasets.
This web-based labeling platform explores and labels various formats of data. Python is the backend of this platform, while a mix of React and MST makes its front end.
Label Studio can be utilized to label all data images, audio, video, time series and text data types with a high degree of accuracy.
The best part about the Label Studio platform is that it is supported on all browsers. The platform's UI can also be embedded in your other applications.
Computer vision is gaining popularity, and its usefulness in ad targeting is going mainstream. Sloth is an open-source platform primarily built for labeling image and video data types. Its dynamic tools can be utilized as a customized framework for labeling tasks specific to your needs.
Computer vision ML is diversified in nature. Sloth enables the creation of custom configurations tailored to the targeted data labelling. It also lets you create visualization items and factor them into your data labeling tasks.
LabelBox is ideal for you if you require frequent data labeling services. This well-known data labeling and analysis platform offers iterative workflows for optimizing datasets.
LabelBox interface allows easy communication between ML teams. This enables data creation in an environment that promotes collaboration. In addition, on LabeBox, you can access a command center. The center facilitates the execution, analysis and timely management of labelling tasks.
Text-based labeling is gaining momentum as a highly sought data annotation service. This can be attributed to the wide applications of labeled text data. The Tagtog platform enables users to create AI datasets primarily from text data.
The main focus of this labeling tool is on NPL. TagTog helps you perform the traditional manual labeling of text data with clarity.
Labeling with Tagtog makes extraction of relevant text information easy and automatic. It also has a feature that identifies data patterns and labeling challenges easily. Wondering how to cope with different languages? TagTog supports multiple text languages as well as dictionary annotations. Additionally, users can enjoy secure storage in the cloud for their labeled datasets.
LightTag is one other platform that is specific to text labeling. The platform is designed to create high-quality datasets for NLP accurately. You can utilize it if you have ML teams that require collaborative workflows.
A key feature of LightTag is a simple UI. Users can explore to manage workflows and a ray of simplified annotations. You can also label and optimize the data creation process with its control features.
Playment is a multi-featured labeling tool that offers secure and customized data-labeling workflows. In addition, the platform supports the creation of high-quality data sets. One of the highly recommended features of Playment is the in-built project management software.
A range of labeling functions can be explored on this platform for different uses, including image, video, and sensor fusion annotation. You can comfortably rely on this platform with its auto-scaling workforce feature for project management from initiation to completion.
Playment key selling points can be itemized as follows:
- Automatic labeling of data types
- Secure storage in the cloud
- Scaling to support business-based labeling services.
Leveraging this allows you to optimize your ML pipeline using quality datasets.
CVAT is another open-source data labeling platform. This labeling tool is commonly used for computer vision machine learning objects. For example, researchers and businesses in computer vision can use CVAT for image and video labeling through image segmentation and classification of objects.
Despite its many benefits, CVAT can be quite difficult to use. There are specific use cases where this platform is applicable; without a master of this, you may not enjoy using it to its fullest potential. Moreover, the overall workflows have to be clearly understood since they can only be accessed through Google Chrome. with some training, you can now use CVAT to label and generate data effectively.
AI and ML have a huge reliance on data labeling, which was rather tedious before the invention of data labeling platforms. Advanced technology in data labeling has allowed data labeling services to eliminate the errors of manual labeling through automation, smart prediction and collaborative workflows. You can now optimize dataset creation and labeling accurately by employing different techniques and features.