Computer Vision
Made Easy with VISIONTOOLSâ˘
With no programming, you can build and deploy a computer vision system in minutes.
Computer Vision Solutions
VISIONTOOLS was developed in 2019 for the use of low-power visual computers. It consists of a set of software tools to make working with complex AI models easy. The software modules include a project management, user administration, a visual labeling editor as well as an export for any A.I. model that you create with VISIONTOOLS. Each module works without cloud constraints.
| Accurate fast | Ultra Easy | Flexible deployment | Unlimited scalability |
| A data-centric apporach to AI ensures quality data even for small datasets. | An intuitive Cloud or onPremise solution that lets you create a custom computer vision project in minutes. | Deploy your model with just a few mouse clicks using our cloud or edge inference engine. | Scaling projects is simple with REBOTNIX VISIONTOOLS, from a single line of production to worldwide operations. |
How it works
Setup your project
Every AI project starts with the creation of a project. The projects can be assigned to each registered user with different rights to the project.
Upload images
You can upload your images to each project with the internal upload or via FTP | SFTP.
Label objects in your images
Every AI project starts with the creation of a project. The projects can be assigned to each user.
Train model and evaluate
When you train your model with visiontools, you can stop and restart the training at any time. You can test and train any number of models.
Deploy your models
After training and testing, you can export and deploy your model to our embedded hardware.
Plan, build and create your AI Models
- Manage project
- Upload, browse, manage your media
- Label your data manual or automatically
- Start training with our internal training modules
- Export, encrypt your models to edge devices
One Integrated Platform for hundreds of servers
Save times
Speeds up your labeling process
by as much as 10x.
Scales Easily
Manages a few to thousands of models
with minimal resources in hundreds of GPU servers worldwide. Deploy your models directly to the edge, to the cloud or onPremise.
Increases Accuracy
VISIONTOOLS users often see substantial improvements in system accuracy in all modules.
Enhances Visibility
Track and manage the efficiency of
AI projects, current data assets, and deployed solutions across all company
site locations.
Learns Continuously
Identifies issues caused by the
environment and raises alerts
when the model drifts.
Gives You Control
Easily update and adjust your solutions without being beholden to a external 3rd developer team.
The tools for success
VISIONTOOLS works decentralized through its own VPN tunnels. Via targeted entries in a nameserver, the application behaves like any other internet web page. The difference is that only the users who are in the virtual private network zone with their clients have access to this domain.
This ist the main login page that you see when you open the main page. The application works in any modern browser, worldwide. Every login is secured thru https.
Identifying object recognition errors in datasets in visual AI models has been a costly and time-consuming challenge. We have addressed this problem and developed a simple and fully automated addon solution for our VISIONTOOLS.
Computer vision is transforming any industry, we make AI easy
Manufactoring
Agriculture
Energy
Plan, build and create your AI Models
VISIONTOOLS HPC
Features
- High performance modules for HPC
- Hypervisior over clusters
- Native HTML(5) Application, no installation is required
- Includes a project management
- Includes a User management
- Internal Labeling tool for bounding boxes and segmentation
- Highend inferencing model testing tools
- Unlimited training of AI models
- Databases included
- Export models for edge computing on NVIDIA Jetson
- Optional inference engine for NVIDIA Jetson NANO, XAVIER NX or XAVIER AGX
- Running onPremise or in the cloud
- VPN (virtual private network) support for decentralized remote co-working)
- Runs on every modern browser like Google Chrome or Firefox for Windows, Mac or Linux
Autolabeling
Once a model is trained, you can use it for Auto-Labeling. The video shows how we auto label thousands of new labels with a pre-trained model.
Hardware requirements for Intel for the cluster manager
- Ubuntu 18.04 LTS or sever
- NVIDIA GPU at least 11 GB of GPU RAM (FOR TRAINING)
- Intel CPU x86 / 64 Bit CPU at least 2.6 GHz or higher
- NVIDIA Jetson AGX for training
- Installed CUDA 10.x or higher, CUDNN 8 or higher
- 100 GB of free disk space
Hardware requirements for ARM64 cluster manager & Edge module
- Ubuntu 18.04 or higher LTS server
- NVIDIA Jetson Xavier NX, or AGX
- 10 gigabytes of free disk space (installed SSD / Solid-state disk) recommend
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