As more and more businesses turn to artificial intelligence and machine learning to accelerate their efficiency in various fields , the need for high-quality labeled data becomes increasingly important. However, labeling data can be a time-consuming and resource-intensive process, which is why many businesses choose to outsource their data annotation needs to specialized providers.
So, how do you choose the right strategy for your project? Most companies (especially SMEs) have 2 main paths for choosing today:
Purchase a full-packaged data annotation service from an outsourcing company so you do not have to do anything except for placing your requirements, money and receiving the results.
Use a data annotation platform so you will have a tool and will build up your own business in-house annotator teams or hire crowd-sourced workforce and project managers.
In this post, we will discuss the pros and cons of each approach and recommend the top providers in each path to help you make the best decision for your needs.
Full-Packaged Data Annotation Service
A full-packaged data annotation service is a complete solution for data labeling needs. It is provided by a third-party service provider who manages the entire labeling process from start to finish. This means that the provider is responsible for data collection, labeling, quality control, and delivery. Here are some of the pros and cons of using a full-packaged data annotation service:
Pros:
Expertise: Full-packaged data annotation service providers have specialized expertise in data labeling. They have the necessary tools and resources to handle complex projects.
Time-saving: Outsourcing data annotation to a full-packaged service provider saves time and resources, allowing businesses to focus on other important tasks.
Quality control: Full-packaged data annotation service providers are responsible for ensuring the quality of the labeled data. This ensures that the data is reliable and accurate, which is critical for machine learning algorithms.
Cons:
Cost: Full-packaged data annotation service providers can be more expensive than buying annotation platforms, especially for large datasets.
Limited control: Businesses have less control over the labeling process when outsourcing to a full-packaged service provider. They may not have direct access to the annotators and may not be able to customize the labeling process.
Top 3 Full-packaged data annotation service providers in 2023
Pixta AI is a company specialized in providing full-packaged services in data sourcing and data annotation with unparalleled experience in image, video, and LiDAR annotation from Vietnam.
With over 8 years of experience in computer vision and data annotation, Pixta AI has a large and experienced team of annotators, a state-of-the-art annotation platform, and a huge full-compliance visual data source from the company's library PixtaStock - the largest library of royalty-free photographs, illustrations, and footage in Asia. In addition, Pixta AI applies the most cutting-edge tools with pre-annotation and semi-automatic labeling pipelines, which allows them to annotate data up to 3 to 4 times faster than traditional manual methods, optimizing both cost and time for all projects.
In 2022, Pixta AI's Data Annotation service grew incredibly with 15+ customers including Honda, Panasonic, Casio, 7andi, SoftBank, NTT Docomo, Mitsubishi Electric, etc., in a single year. The company provides diverse services in various industries with all types of labeling: bounding box, image segmentation, Lidar, etc.
If you are in need of high-level data annotation projects that require high difficulty and accuracy, full-compliance data set with a tight budget, Pixta AI is an excellent choice for your consideration.
Price: From $0.01/annotated with quality commitment
Dataclap is a company based in India specializing in providing full-package data annotation services. They are a quality driven organization with the key values of transparency, ownership and commitment to customers. They provide data collection and annotation services for companies in domains ranging from autonomous vehicles to sports analytics.
As DataClap is the preferred training data partner for startups in the AI, ML, NLP, and Computer Vision space, if you are a startup company or an institution looking for small projects, DataClap will be a good choice for you.
Price: From $0.025/annotated but no quality commitment
Launching AI Data Collection Service, LQA provides an end-to-end Data Annotation Service allowing them to collect any type of data, from image, video to sound, text. Choosing LQA, their annotators can help you to deal with various types of data including image, voice, text, video with many kinds of objects (cars, humans, animals, trees, etc.)
Price: From $0.028/annotated with quality commitment
Annotation Platforms
Annotation platforms are software tools that allow businesses to manage their own data labeling process. They provide a user-friendly interface for creating and managing labeling projects. Annotation platforms can be used by businesses with in-house labeling teams or by those who want to outsource to a crowd-sourced workforce. Here are some of the pros and cons of using an annotation platform:
Pros
Cost-effective: Annotation platforms can be more cost-effective than full-packaged data annotation service providers, especially for large datasets.
Control: Annotation platforms allow businesses to have more control over the labeling process. They can customize the process, manage the annotators, and monitor the quality of the labeled data.
Flexibility: Annotation platforms can be used for a variety of projects, from simple image labeling to complex natural language processing tasks.
Cons
Expertise: Businesses may need to invest in training their own labeling team or hire an external expert to manage the labeling process.
Time-consuming: Managing the labeling process in-house can be time-consuming, especially for large datasets.
Quality control: Businesses are responsible for ensuring the quality of the labeled data. This requires setting up processes and systems for quality control.
Top 3 annotation platforms in 2023
Scale is a platform designed to allow annotations of massive amounts of 3D sensor, image, and video data. It provides machine learning-assisted pre-labeling, an automated quality assurance system, management of datasets, document processing, and AI-driven data annotation focused on data processing for autonomous driving. This data annotation tool can be used for a variety of computer vision tasks, including object detection, classification, and text recognition and it supports multiple data formats.
Price: From $50,000
Labelbox is a training data platform built from three core layers that facilitate the entire process from labeling and collaboration to iteration. It was created in 2018 and has quickly become one of the most popular data labeling tools.
Labelbox offers AI-enabled labeling tools, labeling automation, human workforce, data management, a powerful API for integration, and a Python SDK for extensibility.
It enables annotations with polygons, bounding boxes, lines, as well as more advanced labeling tools.
Price: Free 5000 images/Custom Pro and Enterprise plans.
Dataloop accommodates the whole AI lifecycle including annotation, model evaluation, and model improvement by utilizing the human feedback in the loop. It offers tools for basic computer vision tasks like detection, classification, key points, and segmentation. Dataloop supports both image and video data.
Price: Free trial / Custom Enterprise plans
Conclusion
Choosing the right data annotation strategy for your project depends on a variety of factors, including budget, expertise, and project complexity. Full-packaged data annotation service providers are a good option for businesses that need high-quality labeled data without the resources to manage the labeling process in-house. Annotation platforms are a good option for businesses with in-house labeling teams or those who want more control over the labeling process. Whichever option you choose, it is important to ensure the quality of the labeled data and to have a process in place for quality control.
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