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@@ -28,9 +28,9 @@ This package is powered by [NVIDIA Isaac Transport for ROS (NITROS)](https://dev
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The following table summarizes the per-platform performance statistics of sample graphs that use this package, with links included to the full benchmark output. These benchmark configurations are taken from the [Isaac ROS Benchmark](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark#list-of-isaac-ros-benchmarks) collection, based on the [`ros2_benchmark`](https://github.com/NVIDIA-ISAAC-ROS/ros2_benchmark) framework.
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| Sample Graph | Input Size | AGX Orin | Orin NX | Orin Nano 8GB | x86_64 w/ RTX 3060 Ti|
|[TensorRT Graph<br>PeopleSemSegNet](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/scripts//isaac_ros_unet_graph.py)| 544p |[400 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_unet_graph-agx_orin.json)<br>5.5 ms |[218 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_unet_graph-orin_nx.json)<br>6.9 ms |[143 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_unet_graph-orin_nano_8gb.json)<br>12 ms |[1190 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_unet_graph-x86_64_rtx_3060Ti.json)<br>1.6 ms |
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| Sample Graph | Input Size | AGX Orin | Orin NX | Orin Nano 8GB | x86_64 w/ RTX 4060 Ti |
|[TensorRT Graph<br>PeopleSemSegNet](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/scripts//isaac_ros_unet_graph.py)| 544p |[385 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_unet_graph-agx_orin.json)<br>6.9 ms |[210 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_unet_graph-orin_nx.json)<br>8.3 ms |[142 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_unet_graph-orin_nano.json)<br>13 ms |[827 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_unet_graph-nuc_4060ti.json)<br>4.0 ms |
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## Table of Contents
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## Latest Update
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Update 2023-04-05: Source available GXF extensions
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Update 2023-05-25: Performance improvements.
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## Supported Platforms
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@@ -292,6 +292,7 @@ For solutions to problems with using DNN models, please check [here](https://git
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