NVIDIA’s Transfer Learning Toolkit 3.0 Accelerates Building Custom AI Models

NVIDIA has launched Transfer Learning Toolkit (TLT) 3.0, which dramatically reduces the time it takes to build computer vision and conversational AI models. TLT comes with multiple pre-trained models ready to be deployed in the cloud or at the edge. 

Computer vision-based neural networks are incredibly complex. They implement multiple algorithms and techniques to perform image classification and object detection. These deep neural networks are trained with disproportionately large datasets to arrive at an accurate computer vision model. 

Residual neural network (ResNet) is one of the most popular artificial neural network architectures used for training computer vision AI models. It defines the architecture to identify various patterns in images, which is the basis for computer vision AI. ResNet’s architecture is applied to a variety of datasets to train image classification and object detection models. ImageNet is a popular dataset of over 15 million labeled high-resolution images with around 22,000 categories. When a ResNet model is trained with ImageNet, the model can classify an image from one of the categories. The model can be used in many computer vision-based applications without any retraining. 

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Janakiram MSV is an analyst, advisor, and architect. Follow him on Twitter,  Facebook and LinkedIn.