DeepLTK - Deep Learning Toolkit for LabVIEW | Ngene

DeepLTK

Deep Learning Toolkit for LabVIEW

DeepLTK is an award-winning product designed to empower researchers and engineers with intuitive and powerful tools to develop, validate and deploy deep learning-based systems in LabVIEW development environment.

 

DeepLTK was completely developed inside LabVIEW which makes it unique in the market, and greatly simplifies the process of integrating machine learning technologies.

FEATURES & FUNCTIONALITY

Build
Configure
Train
Visualise
Deploy

Deep Neural Networks

in LabVIEW

FEATURE HIGHLIGHTS:
  • ​Create, configure, train, and deploy deep neural networks (DNNs) in LabVIEW

  • Accelerate training and deployment of DNNs on GPUs

  • Save trained networks and load for deployment

  • Visualize network topology and common metrics (memory footprint, computational complexity)

  • Deploy pre-trained networks on NI's LabVIEW Real-Time target for inference

  • Speed up pre-trained networks by employing network graph optimization utilities

  • Analyze and evaluate network’s performance

  • Start with ready-to-run real-world examples

  • Accelerate inference on FPGAs (with help of DeepLTK FPGA Add-on)

SUPPORTED LAYERS
 

The toolkit supports a number of layers required to implement deep neural network architectures for common machine learning applications such as image classification, object detection, instance segmentation and voice recognition:

  • Input (1D, 3D)

  • Data Augmentation

  • Convolutional

  • Fully Connected or Dense

  • Batch Normalization (1D, 3D)

  • Pool (maximum, average)

  • Upsampling

  • ShortCut

  • Concatenation

  • Dropout (1D, 3D)

  • SoftMax

  • Object Detection

SUPPORTED NETWORK ARCHITECTURES

  • MLP - Multilayer Perceptron

  • CNN - Convolutional Neural Networks

  • FCN - Fully Convolutional Network

  • ResNet - Deep Residual Learning for Image Recognition

  • YOLO v2 - You Only Look Once for object detection

  • U-Net -  Semantic Segmentation

REFERENCE EXAMPLES

Reference examples are part of the toolkit which can be found with the following path:

LabVIEW install path\examples\Ngene\Deep Learning Toolkit

  • MNIST_Classifier_MLP(Train_1D).vi and MNIST_Classifier_MLP(Train_3D).vi - demonstrates the process of programmatically building and training deep neural networks for image classification task of handwritten digit recognition (based on MNIST dataset) by using MLP (Multilayer Perceptron) architecture.

  • MNIST_Classifier_CNN(Train).vi - demonstrates the process of programmatically building and training deep neural networks for image classification task of handwritten digit recognition (based on MNIST dataset) by using CNN (Convolutional Neural Network) architecture

  • MNIST_Classifier(Deploy).vi - demonstrates the process of deploying pretrained network by automatically loading network configuration and weights files generated from the examples above.

  • MNIST(RT_Deployment) (project) - demonstrates the deploying pretrained model on NI's Real-Time targets.

  • MNIST_CNN_GPU (project) - demonstrates the process of accelerating training and deployment on GPUs.

  • YOLO_Object_Detection(Cam).vi - demonstrates the process of deploying pretrained network for object detection based on YOLO (You Only Look Once) architecture.

  • YOLO_GPU (project) - demonstrates the process of accelerating YOLO object detection network for deployment on GPUs.

Object_Detection (project) - demonstrates training of neural network for object detection on simple dataset.

INSTALLATION AND SYSTEM REQUIREMENTS

The toolkit comes as a VIPM (VI Package Manager) installer which includes the toolkit itself, documentation and reference examples

DEVELOPMENT SYSTEM REQUIREMENTS

  • LabVIEW 2016 (32-bit and 64-bit) and above (64-bit version of LabVIEW is recommended)

  • Windows 10

HAVE QUESTIONS?

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