Action recognition models such as PoseClassificationNet have been around for some time, helping systems identify and classify human actions like walking, waving, or picking up objects. While the concept is well-established, the challenge lies in building a robust computer vision model that can accurately recognize the range of actions across different scenarios that are domain- or use case…
]]>NVIDIA TAO is a framework designed to simplify and accelerate the development and deployment of AI models. It enables you to use pretrained models, fine-tune them with your own data, and optimize the models for specific use cases without needing deep AI expertise. TAO integrates seamlessly with the NVIDIA hardware and software ecosystem, providing tools for efficient AI model training…
]]>As vision AI complexity increases, streamlined deployment solutions are crucial to optimizing spaces and processes. NVIDIA accelerates development, turning ideas into reality in weeks rather than months with NVIDIA Metropolis AI workflows and microservices. In this post, we explore Metropolis microservices features: Managing and automating infrastructure with AI is…
]]>This post is the first in a series on building multi-camera tracking vision AI applications. In this part, we introduce the overall end-to-end workflow, focusing on building and deploying the multi-camera tracking system. The second part covers fine-tuning AI models with synthetic data to enhance system accuracy. Large areas like warehouses, factories, stadiums, and airports are typically…
]]>If you’re building unique AI/DL application, you are constantly looking to train and deploy AI models from various frameworks like TensorFlow, PyTorch, TensorRT, and others quickly and effectively. Whether it’s deployment using the cloud, datacenters, or the edge, NVIDIA Triton Inference Server enables developers to deploy trained models from any major framework such as TensorFlow, TensorRT…
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