Train Neural Networks

Leapfrogging by Design

AI is changing. Leverage the most versatile, extensively pre-trained model for visual understanding and adapt it to your new task. Experience the easiest neural network training on the market to generate a perfect understanding of your domain. Discover how you can multiply your training data quality and quantity.

All-Task Output

V7 is built to handle any vision output with equal performance, whether it's simple classification, object detection, keypoint skeleton fitting, or all of the above.

Optimal Training

Each training session tests hyper-parameters and image augmentation techniques to find the optimal solution to an open-ended problem.

Continual Learning

Accuracy doesn't stop at the first training session. Capture more data in production, route it to a dataset for annotation, and add more knowledge to your model for its next training session.

Learn how to create training data ➜
v7 neurons product
Your product
accurate labeling with v7 darwin
Fast labeling on V7 Darwin
image dataset
Newly collected images or video
model training and pruning
Training & continual learning

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. Testing and running neural networks has never been easier.

Zero to State of the Art

Bring AI to life from day 1. With the fast pace of deep learning's state of the art, V7 ensures that on-platform models are the most accurate on the market. By continually updating our architecture, and leveraging an extensively pre-trained backbone, your models will always maximize precision and recall.

Instance Segmentation (Object Detection + Segmentation) benchmark:

V7 Neurons (VoVNet)

Average Precision
46.0
Inference speed (FPS)
20

Mask R-CNN (Detectron2)

Average Precision
41.0
Inference speed (FPS)
13

YOLACT550++ (YOLO)

Average Precision
34.6
Inference speed (FPS)
27

Model backbone benchmark:

V7 Neurons (Ours)

Average Precision - Detection
43.8
AP - Masking
39.3

ResNet-50

Average Precision - Detection
41.0
AP - Masking
37.2

MobileNetV2

Average Precision - Detection
33.0

Benchmarks were performed on the MS-COCO dataset. COCO is largely composed of large, frame-filling objects that are easy to detect by modern neural network architectures. V7 Neurons outperformed Mask-RCNN primarily on the detection of small and uncommon objects, which matter most in industry cases.

Label Training Data at Unsupervised Speed

The world of AI moves fast, don't let ground truth slow you down.

We've spoken to hundreds of ML teams to create a labelling environment that will keep up with the most ambitious projects in AI. V7  automates labelling, enables unparalleled control of your annotation workflow, helps you spot quality issues in your data, and integrates seamlessly into your pipeline. On top of all of that, its user experience matches our maniacal attention to detail coupled with excellent technical support. Deep learning scientists love it, annotation workforces love it, you'll love it too. Scale your ground truth creation 10x today.

Try it

See What's Possible

life sciences AI

Life Sciences

Microscopy

Cells, microorganisms, clusters, in bright and dark microscopy
Learn More

GLP

Understanding safe laboratory practice
Learn More
Manufacturing computer vision

Manufacturing

Defect Inspection

Small and uncommon defects
Learn More

Asset Inspection

Rust, wear, and materials analysis
Learn More

Kitting and Picking

Assembly step sequence understanding
Learn More
Agri Tech computer vision

Environmental

Plant & Field Agriculture

Plant growth and yield
Learn More

Livestock & Wildlife

Livestock and wildlife health
Learn More

Wind Model Orchestration Engine

Compute needs for AI can be unpredictable, just like the weather.

Wind is a cloud-based routing system to manage AI model training and inference pipelines for thousands of concurrent requests within V7. It allows your team to host and manage hundreds of models across any number of GPU servers.
If models are close to their server's capacity, Wind will spin up a new instance (or back down) to keep them running at any scale and at the lowest cloud costs.
Wind also handles the training of V7 models, by allocating GPU resources that match the size of your dataset. As your project grows and relies on more visual data to learn, you won't have to worry about upscaling infrastructure or keeping engineers on call 24/7 to keep models alive.

Over
100 GPU
instances available per model

60 seconds
to request and start a new server

0
Dev-ops engineers needed in your team

Cloud to Edge AI with industry leaders

V7 is fortunate to be working with partners to bring performant inference speeds to the cloud and on edge deployments.
We are NVIDIA Metropolis certified and operate on the FANUC Field platform for edge deployment of your models.

Over
3 Billion
images processed by models on V7

107
Countries automating tasks

Ready to get started?

Schedule a demo with our team or discuss your project.

Image & Video Annotation

A user experience crafted for professionals, with built-in automation, and supporting every tool and image data format.