In robotics applications, 3D object poses provide crucial information to downstream algorithms such as navigation, motion planning, and manipulation. This helps robots make intelligent decisions based on surroundings. The pose of objects can be used by a robot to avoid obstacles and guide safe robotic motion or to interact with objects in the robot’s surroundings. Today, 3D pose estimation is used…
]]>As many would probably agree, the development of a winning deep learning model is an iterative process of fine-tuning both the model architecture and hyperparameters. The tuning is often based on insights about the network behavior gained by examining various data obtained in training. One such data is the output from each layer of a deep learning model’s computation graph, also known as the layer’…
]]>This post is the first in a series that shows you how to use Docker for object detection with NVIDIA Transfer Learning Toolkit (TLT). For part 2, see Using the NVIDIA Isaac SDK Object Detection Pipeline with Docker and the NVIDIA Transfer Learning Toolkit. The modular and easy-to-use perception stack of the NVIDIA Isaac SDK continues to accelerate the development of various mobile…
]]>This post is the second in a series that shows you how to use Docker for object detection with NVIDIA Transfer Learning Toolkit (TLT). For part 1, see Deploying Real-time Object Detection Models with the NVIDIA Isaac SDK and NVIDIA Transfer Learning Toolkit. As part of the NVIDIA Isaac SDK 2020.1 release, a Docker image is available in NVIDIA NGC that contains all the tools necessary…
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