Preparing data for an AI system can be a challenging, laborious, and expensive process. Luckily there’s a litany of tools that can mitigate the tedium of the process. Selecting the right tool, however, is a challenge all on its own.
The team at iMerit are tool agnostic, which means we leverage a litany of tools to make data labeling as painless, easy, and fast as possible for our clients. We unite all factors of data labeling and annotation — technologies, processes, people, and tools.
In this piece, we’ll break down the pros and cons of the most popular data labeling tools on the market, and give insights based on our first-hand experiences with each of them.
Amazon Sagemaker is a self-serve solution that empowers companies to label their own data. The UI is simple, seamless, and low maintenance, meaning that data science teams can quickly roll out the tool on a short time-frame and without a learning curve that might impede their rise to competence with the tool.
SageMaker’s terms of service and billing are great for anyone who’s already an AWS Marketplace user. The tool features especially well-rounded functionality in the areas of semantic segmentation, polygon/polyline drawing, bounding box drawing, and annotation classification changing. In terms of file and data types, SageMaker specializes in image, LiDAR, text, and video. SageMaker also gives users the option to label their data in-house, in case they have the manpower. In the event there are shortages of in-house manpower, companies can leverage SageMaker’s quality crowdsourcing (Mechanical Turk) for data labeling, effectively giving them access to high-quality data labeling from a 24/7 marketplace. As crowdsourcing has its own limitations, SageMaker has lists of independent vendor teams who can guarantee quality data labeling in a secure environment. As with most AWS tools, there is a strong API to integrate automated operations and the data can be seamlessly integrated with SageMaker’s ML model training.
It isn’t all sunshine and butterflies with SageMaker, however. Considering the pricing model, the tool doesn’t scale as well as other tools on this list for very short tasks. The tool also lacks some key collaborative functionality, particularly when it comes to things like task flagging and project monitoring (reporting). There are also some key functionalities that are absent from the platform that certain companies might consider deal breakers. These include a lack of user freedom when it comes to copying and pasting annotations. The tool further lacks autosave functionality.
SageMaker is a robust tool for companies big and small, as the functionality is able to match a wide range of annotation needs. Users will need to adopt a manual approach to data labeling when using this tool, which can be great for facilitating best practices in data labeling rather than having a tool do this for them.
Dataloop is an iMerit partner and one-stop shop for building and deploying powerful computer vision pipelines. Dataloop has been an iMerit go-to in the areas of video and image annotation. LiDAR functionality has been on Dataloop’s docket for a while now, but as of now isn’t a feature the tool offers.
In our experience working with Dataloop, they are a fantastic tool in the areas of auto segmentation, polygon/polyline drawing, bounding box drawing, and collaboration. The task flagging functionality is great for larger teams taking on projects where people will be working blindly of each other, but still need to collaborate from time to time. Dataloop empowers users to finish bounding boxes in single attempts thanks to its crosshair feature. Their API and Function-as-a-service allow flexible extensions and customizations while the team is highly responsive.
Dataloop does have missing functionality in areas some might deem essential. For example, upon completion of any annotation task, users will have to manually push their task to the quality control phase. Fortunately the tool will learn from this workflow, resulting in images that automatically flow. There is also no full-screen functionality or autosave features, which aren’t mission critical by any means, but still spare teams from major headaches if their browsers refresh abruptly, or Windows 10 decides to ninja-reboot them when they’re knee-deep in data annotation.
All in all, Dataloop is iMerit’s go-to when it comes to image and video annotation. The current lack of LiDAR functionality is a dealbreaker for some, which brings us to our next tool.
Deepen provides AI-driven tools and services that specialize in sensor data. Their LiDAR annotation capabilities are second-to-none on this list, making Deepen the go-to for iMerit’s AV projects.
Deepen can be deployed both on-premise or on cloud, and comes with the capability for admins to manage multiple projects and workforces simultaneously while also leveraging Deepen’s built-in quality control workflows and tools. The tool’s ability to change annotation classes across 2D and 3D bounding boxes, polygons, and segmentations, and multi-sensor fusion, are some of Deepen’s top features. Deepen’s highly flexible UI empowers teams to customize, configure, and create workflows that work best for them.
Compared to the other tools on this list, Deepen falls short in certain areas. Task flagging is a missing feature that’s typically needed to facilitate optimal collaboration. The tool also lacks reporting functionality, but still offers an analytics suite that monitors user productivity. Users might also find that using the tool is laborious due to missing cell annotation and auto segmentation functionality, features that normally empower users to breeze through tedious and time-consuming tasks. The tool also falls a bit short in giving users the ability to remove and erase annotations.
Regardless, Deepen is an iMerit go-to when it comes to LiDAR and 3D point cloud annotations.
SuperAnnotate is a complete end-to-end computer vision platform that can annotate, train, and automate any computer vision pipeline. It specializes in audio, video, image, and leverages modern features like transfer learning, data/quality management, and automatic predictions. We at iMerit love SuperAnnotate for its intuitive, responsive, and comprehensive UI, which helps us work and collaborate more efficiently than any other tool on this list. It’s also our favorite tool when we engage in projects which heavily involve semantic segmentation and pixel segmentation. As with all iMerit partners, the team distinguishes itself in responsiveness and support.
In terms of strengths, SuperAnnotate is commonly hailed for its annotation quality, pricing, customer support, and excellent UI. We recommend SuperAnnotate for any projects that heavily involve semantic segmentation and pixel segmentation. SuperAnnotate’s functionality in the areas of image classification, bounding box drawing, polygon/polyline drawing, and edge sharing are commonly hailed as being some of the best in the industry. The tool also features auto segmentation functionality, as well as small image mini mapping, which gives users the ability to track their place when zoomed in on an image.
SuperAnnotate does come with downsides, however. The tool lacks in functionality in the areas of auto segmentation and bounding box copying. This leaves users to perform these tasks manually, and will leave them wanting more when annotating large swaths of data. The lack of full-screen image functionality and edge snapping are also glaring flaws that users will certainly wish for while using the tool.
There’s no shortage of powerful data labeling tools on the market today. Deciding which tool will work best for you will largely come down to the types of data you’re looking to annotate, the size of your project, the workflows of your team, and the scope of your budget.
Download our solutions brief to learn more about we can help you create high quality data for your ML deployment.