SuperAnnotate Pro
SuperAnnotateReviews from AWS customer
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My new favorite annotation workflow for computer vision research
What do you like best about the product?
I am a physician-scientist and my research applies computer vision techniques to biomedical imaging projects. The SuperAnnotate platform solves a common problem that computer vision researchers encounter, which is the rapid curation of expert annotations. Given the limited training data in medical imaging datasets, I often start with image segmentation based on expert annotations. Previously, I cobbled together several different tools for annotation, quality control, and neural network training but it was cumbersome to switch between different software and intermediate annotation masks. My favorite aspect of SuperAnnotate is the integrated, streamlined workflow AI-augmented annotation, quality control, and neural network training. Note: I have left a review on TrustPilot with similar content, although the wording has been changed to avoid self-plagiarism.
Updated review 2024-02-16:
I'm still using SuperAnnotate and it has been crucial to make progress on several projects. They have continued to innovate developing new annotation features such as MagicSelect and Magic polygon. The shortcuts for annotating images are intuitive and speed up my workflow. Their annotation teams have been timely and efficient, following detailed instructions. I have been very happy with their services.
Updated review 2024-02-16:
I'm still using SuperAnnotate and it has been crucial to make progress on several projects. They have continued to innovate developing new annotation features such as MagicSelect and Magic polygon. The shortcuts for annotating images are intuitive and speed up my workflow. Their annotation teams have been timely and efficient, following detailed instructions. I have been very happy with their services.
What do you dislike about the product?
My initial concern was that I could not upload annotation masks that were generated manually or on other platforms. However, the team rapidly introduced a feature to upload annotation masks for use in the platform, which meant I could leverage existing annotations from previous projects. The team is responsive to feedback and I am very pleased with my experience.
Updated review 2024-02-16:
The team has been responsive to feedback with continuous development and integration of new features. Uploading pre-annotations has been easy using their SDK. I don't have no dislikes or concerns.
Updated review 2024-02-16:
The team has been responsive to feedback with continuous development and integration of new features. Uploading pre-annotations has been easy using their SDK. I don't have no dislikes or concerns.
What problems is the product solving and how is that benefiting you?
I have been able to annotate image datasets much faster, which has made my research more productive. I like the project management interface that allows the delegation of annotation to specific team members as well as the ability to monitor annotation progress. The integrated neural network training is very helpful, too.
The best annotation tool in the market
What do you like best about the product?
For our company, which specializes in ML solutions, we have tried several popular tools for image annotation/labeling, and SuperAnnotate had the easiest to use and the most relevant toolset for us. Particularly their smart semantic image segmentation tool is gold! It speeds up the process a lot. It's been more than a year we are using their services and we are very satisfied with it.
What do you dislike about the product?
So far we are very satisfied with their tools and customer support. Don't have any complaints
What problems is the product solving and how is that benefiting you?
Our company specializes in infrastructure inspection using satellite imagery and computer vision / ML. We are using SuperAnnotate's tools to do semantic image segmentation on satellite images. As their semantic segmentation tool automatically detects segments/objects on the image, and it lets you choose the level of segmentation details, it makes the work much faster and a lot more accurate. They also have rich analytics and tools to manage annotator teams, letting you assess easier how long it will take to annotate the next batch of the images and plan the costs, evaluate individual annotator's work quality, etc.
Also, together with the annotation software, they provide teams of annotators, which is very handy for a bootstrapped startup like ours, as it lets you quickly allocate as many professional annotators as you need based on the project size, without a need to hire them permanently.
Also, together with the annotation software, they provide teams of annotators, which is very handy for a bootstrapped startup like ours, as it lets you quickly allocate as many professional annotators as you need based on the project size, without a need to hire them permanently.
Fast, frequent updates, desktop versions, superb customer support, and most of all 'EASY'!
What do you like best about the product?
Very quick adoption time for our annotators who do not always come from a technical background. Furthermore, the UX of the platform has thus far been excellent, bridging speed and versitility (desktop version of SuperAnnotate) with nigh-instantaneous customer support. We have tested this out against alternatives and SuperAnnotate is by far the best we had.
What do you dislike about the product?
I think this is universal to CV-based platform providers but the semantic segmentation tool's draw line is a bit broad which factors in a little more pixels than I would like. Would definitely like a tool that helps to go into sub-pixels which I hear they are close to deploying.
What problems is the product solving and how is that benefiting you?
We use SuperAnnotate's platform to annotate and label large volume of images to fit into ML models, particularly in the space of autonomous vehicles, surveillance, geospatial analytics, and retail automation. In terms of benefits, I want to highlight two attributes unique to SuperAnnotate, (a) its fast, ridiculously fast, given that it uses an automation algorithm to sift through large volume of model training data and images that would've taken us a long time previously, and (b) they house what I assume to be a large and robust customer support cell since I have had platform-based feedback and additions solved within a week's time and inquiries answered in minutes.
Highly recommend Superannotate platform!
What do you like best about the product?
Superannotate platform is very comfortable to use, it's easy to reach annotation companies, train new annotators, upload data and download results. Also, their customer support is highly responsive.
What do you dislike about the product?
We really love it, we don't have dislikes
What problems is the product solving and how is that benefiting you?
We use their platform to annotate our data for various projects.
Our annotation teams are working faster due to their platform.
Our annotation teams are working faster due to their platform.
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