Endless Ways to Adapt and Supercharge Your AI Workflows with Transfer Learning
How NVIDIA TAO Toolkit and Pretrained Models Transform Development Efforts
AI is bringing about a revolutionary change across many industries from retail, manufacturing, and healthcare to automotive and more. Enterprises in these industries are operationalizing AI to perceive the world around us and generate real-time insights that would otherwise be impossible.
Moving AI from research to production by building a custom AI solution for a given use case is a nontrivial task. It starts with collecting and annotating large sets of representative data required for training. Achieving a state-of-the-art deep learning model requires considerable domain experience, where data scientists run many iterations and experiments to arrive at the representative model. This is extremely time-consuming. Finally, the trained model must be optimized for high-throughput and low-latency before deployment.
To fast-track AI from concept to production, the most practical and scalable way is to create an optimized model through adaptation and fine-tuning pretrained models to address the proliferation and diversity of use cases across many industries. This enables rapid prototyping and customization to meet requirements for any environment.
For instance, if you are developing retail analytics applications, no two stores or fulfillment centers are visually identical. Each inference model requires some customization based on variables such as lighting conditions and the layouts of the specific physical space that it will be deployed in. Similarly, if you are deploying your AI solution in an industrial setting, you might be inspecting different parts in different assembly lines. All these require model customization to adapt to that environment.
NVIDIA TAO Toolkit solves these problems by enabling you to quickly train, adapt, optimize, and deploy state-of-the-art models through the power of transfer learning. Transfer learning is a training technique where you leverage the learned features from one model to another. This reduces the amount of data and training time required to customize models to your exact need.
The TAO Toolkit is an easy-to-use, CLI-based solution that includes guided Jupyter notebook–based workflows and access to dozens of NVIDIA pretrained models to jumpstart the process. The model architectures provided by the TAO Toolkit are state-of-the-art and proven to work by solving many common problems in both computer vision and conversational AI. Here are some use cases:
- Computer vision: Object detection, classification, key point estimation
- Conversational AI: Automatic speech recognition (ASR), natural language processing (NLP).
You can solve several use cases across industries by using models and transfer learning. In this whitepaper, we highlight a few use cases and best practices using TAO Toolkit. Here are a few that are covered in this whitepaper: