Product Features
No Coding
Advanced parameters
Multiple Deep Learning Modules
GPU Support
Transfer Learning
Image Augmentation
Model Testing
LabVIEW Deployment
No Coding
DLHUB is specifically designed for engineers who do not have a profound knowledge of programming skills to design a proper deep learning model in only a few simple clicks.
Design AI model
LOAD DATASET
LOAD DATASET INTO DLHUB
CONFIGURE
CONFIGURE A DEEP LEARNING NEURAL NETWORK ARCHITECTURE
TRAIN
TRAIN A DEEP LEARNING NEURAL NETWORK
EVALUATE
EVALUATE A DEEP LEARNING NEURAL NETWORK
EXPORT
EXPORT TRAINED DEEP LEARNING NEURAL NETWORK MODEL FOR DEPLOYMENT
TRAIN
DLHUB supports various training algorithms to allow multiple design options for specific problems. The designer needs to select appropriate training algorithm with its parameters and stopping criteria that decide when to stop before starting the training process. The graphical user interface allows a designer to access visual feedback during the training process. The designer can stop training at any time to tweak training parameters. GPU is automatically detected and enabled to accelerate the training process.
EVALUATE
DLHUB supports evaluating a trained deep learning model with the new dataset. That helps the designer to validate the deep learning model in different test scenarios before deciding to use the trained model in an actual production environment/application.
EXPORT
Although evaluation helps designer selecting correct trained model architecture, it is useful to test the trained model again with completely unknown test data to see how the trained model behaves before exporting it for deployment.
LOAD DATASET
DLHUB supports different type of dataset, including text file, image files, and image folders. When a dataset is loaded into DLHUB, data normalization procedure is automatically applied.
CONFIGURE
DLHUB supports various deep learning node types that help to construct any deep learning neural network model with ease. A designer needs to select node type, modify its parameters, re-arrange deep learning architecture, and verify it this deep learning model is valid or not. During the designing process, the designer can save and load this model configuration into the “cfg” file. DLHUB also provides model templates with different deep learning architectures that helps a designer speed-up designing task.
TRAIN
DLHUB supports various training algorithms to allow multiple design options for specific problems. The designer needs to select appropriate training algorithm with its parameters and stopping criteria that decide when to stop before starting the training process. The graphical user interface allows a designer to access visual feedback during the training process. The designer can stop training at any time to tweak training parameters. GPU is automatically detected and enabled to accelerate the training process.
EVALUATE
DLHUB supports evaluating a trained deep learning model with the new dataset. That helps the designer to validate the deep learning model in different test scenarios before deciding to use the trained model in an actual production environment/application.
EXPORT
Although evaluation helps designer selecting correct trained model architecture, it is useful to test the trained model again with completely unknown test data to see how the trained model behaves before exporting it for deployment.
LOAD DATASET
DLHUB supports different type of dataset, including text file, image files, and image folders. When a dataset is loaded into DLHUB, data normalization procedure is automatically applied.
CONFIGURE
DLHUB supports various deep learning node types that help to construct any deep learning neural network model with ease. A designer needs to select node type, modify its parameters, re-arrange deep learning architecture, and verify it this deep learning model is valid or not. During the designing process, the designer can save and load this model configuration into the “cfg” file. DLHUB also provides model templates with different deep learning architectures that helps a designer speed-up designing task.
TRAIN
DLHUB supports various training algorithms to allow multiple design options for specific problems. The designer needs to select appropriate training algorithm with its parameters and stopping criteria that decide when to stop before starting the training process. The graphical user interface allows a designer to access visual feedback during the training process. The designer can stop training at any time to tweak training parameters. GPU is automatically detected and enabled to accelerate the training process.