model management

Train, Run, and Improve Your Models

Turn your labeled data into models. Use models to solve your computer vision task, or to label more data.

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ML pipeline

Build robust ML pipelines. Deploy reliable AI faster

Automate labeling and gain unparalleled control of your annotation workflow. Scale your ground truth creation 10x today.

Use a model to label more data
Send edge cases back to be labeled
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Collect Data
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Label Data
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Train Your Model
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Deploy AI
Models

Model types you can train

Pick a model type, train, and run your models in the cloud on V7’s scale-agnostic engine or your own instance. Switch on a webcam, and view the results right from your browser.

Object Detection
Object detectors draw a bounding box around each item.
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Instance Segmentation
Encloses each individual object in a polygon.
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Classification
Labels an image with a single tag. Does not apply multiple tags.
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Semantic Segmentation
Assigns a label or category to pixels in an image.
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Panoptic Segmentation
Combines instance segmentation and semantic segmentation.
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3D Segmentation
Identifies and classifies objects within 3D data.
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Auto-ML

Develop production-ready AI in hours, not weeks

The easiest low-code experience to help you train and ship performant AI.

Keep your data and models safe

The security of your data is our top priority. We are SOC2 compliant and enable SSO integration so that you can rest assured that your data remains protected and private.

Spot and fix errors

Spot label quality issues early and send data to annotation teams for re-work. Build robust review and consensus workflows to ensure quality and shorten the time to production. Improve your ML model performance by improving your datasets.

Train and test in a few clicks

Turn your labeled data into models and run models directly from V7 in a matter of minutes. Use our pre-trained architecture to enable your model to learn with very few training samples. Leverage V7’s iterative model training capabilities and ship performant AI faster.

Seamless data export

Use Pytorch Dataloaders to create datasets to train your own models on V7. Solve classification, object detection, instance segmentation tasks, and more.

Leverage instant inference

Get immediate results from inference API. You can also switch on the webcam and test the model directly from your browser.

Bring your own model

Register your own model via REST API, add it to your custom workflow and run it to label your training data 10x faster. You can register any model that is exposed via HTTP and manage it the same way you do the ones trained using V7.

Workflows

Connect models and humans into workflows

Use your models as tools, workflow stages, or quality-assurance tests.

Build customizable data workflows, integrate your models to label data.
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Use the Model Tool to auto-label multiple objects and classes in images or videos in a few clicks.
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Add the consensus stage to verify the quality and accuracy of the annotations.
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library

Model Library—all your models, ready to use

Keep all your company models in one place. Deploy them for model-assisted labeling tasks or use them to train new AutoML models.

All-Task Output

Handle any vision output with equal performance, from simple classification to object detection to keypoint skeleton fitting.

Continual Learning

Capture data in production and send it to a dataset for annotation. Add more knowledge to models for their next training session.

Instant Inference

Run models in the cloud on the scale-agnostic Wind engine, switch on a webcam, and view the results right from your browser.

metrics

Monitor your model performance

Combine models, humans, and data in composable workflows.

Model Consensus
Test your accuracy and explore fail cases

Run your models through sets of images and compare versions

Keep your production AI in top shape by ensuring each new version passes strict IoU tests

Model Metrics
mAP, IoU, and everything in between

Ensure you’re always using the most accurate version available

Each model you import or train surfaces their accuracy across multiple metrics

Model Metrics
Confusion matrix and class-based insights

Present your model results to your team in beautiful interactive graphs

Monitor metrics like accuracy, precision, recall, and F1-score across your team’s models

REVIEWS

Hear it from our customers

Learn how world-class ML teams build AI products with V7

"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. "

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.

"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."

"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."

"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."

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.

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.

V7 is helping us manage a complicated, intricate, pixel-perfect labeling exercise. Their model-assisted labeling is the best around.

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 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."

“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.”

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.

"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."

Gain control of your training data
15,000+ ML engineers can’t be wrong