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“V7 did everything that we wanted—it enabled us to label videos in the way we needed, the turnaround time for new features was really fast, and the reviewing and sorting process was much better.”
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Managing our data from one place is particularly important for us. Previously, our data was stored in many different formats and in different places. Having a single source makes our data more robust and also greatly reduces the development time for new algorithms, as the learning curve for developers is small.
What I appreciate the most about V7 is flexible UI and API, responsive support, and active development of new features and bug fixes.
We needed a tool that lets us keep the data in one place, annotate, and version it. Having found V7, we decided not to build the internal solution.
We use V7 to make our workflow for deep learning training and annotation streamlined and efficient. From the pathologist’s point of view, V7 turned out to be much easier to learn and use than other software - I can easily understand what I’m doing.
We were looking for an annotation tool that would be much faster, and V7 sped up our labeling 9–10x compared to VGG. The appeal of using V7 is that it’s commercial off-the-shelf, very intuitive, and easy to use for non-technical people involved in our project.
I like the auto-segmentation feature. To me, that’s a nice AI feature that V7 took beyond the gimmick feature - it’s mature enough to be useful.
We needed a tool that could do annotating and data versioning because we distribute our tools to farms, and we need to make sure that they have the same version of data for the same models. V7 met our needs.
Having accurately annotated datasets was crucial to catch the typical features of malignant melanoma. V7 lets us visualize the balance of this data across populations.
V7 did everything that we wanted—it enabled us to label videos in the way we needed, the turnaround time for new features was really fast, and the reviewing and sorting process was much better.
V7 is helping us manage a complicated, intricate, pixel-perfect labeling exercise. Their model-assisted labeling is the best around.
With V7 workflows, it’s very easy to see the project's status - I know how far along the labelers are. I know what’s in review and what’s completed. We can see all status changes happening in real-time. That is probably my favorite V7 feature.
Thanks to V7, the image annotation is 30% faster, but realistically, considering the whole process - transferring files and QA - we more than doubled the number of images we can do in the same span of time.
Visibility on metrics and annotators' work in V7 is very helpful to us, and it's something we didn’t have in our internal solution. The option to check past annotations and review the work is also valuable, along with V7’s ability to interactively define the workflows and the flexibility in task assignment.
V7 is super sleek, intuitive, and easy to use. Within a couple of minutes, you're off to the races and can annotate quickly. The team is highly responsive and helpful.
Run our Python library or use the CLI to establish workflows, monitor progress, assign tasks, priorities, and receive completed items based on your SLAs.
V7 supports image, video, and text data. The file formats you can use with V7 include: JPG, PNG, MP4, MOV, AVI, BMP, SVS, TIFF, DCM, ZIP, DICOM, NIfTI.
We offer in-app chat support and email technical support to all of V7 users. We will make sure to take good care of you and your team. You can get in touch with us at: email@example.com.
Absolutely! V7 offers free access for researchers, academics, and students. See the qualification criteria and apply for a free education account.
Yes your data is secure with V7. We take data security very seriously. Go to V7 Data Processing & Security to learn more about how we keep your data safe.