Training and Optimizing a 2D Pose Estimation Model with the NVIDIA Transfer Learning Toolkit, Part 1

Originally published at: Training and Optimizing a 2D Pose Estimation Model with the NVIDIA Transfer Learning Toolkit, Part 1 | NVIDIA Developer Blog

Human pose estimation is a popular computer vision task of estimating key points on a person’s body such as eyes, arms, and legs. This can help classify a person’s actions, such as standing, sitting, walking, lying down, jumping, and so on. Understanding the context of what a person might be doing in a scene has…

The model of the training , it can be used at the following article?

I know I can use the method to convert to tensorRT, but the model can be used at creating-a-human-pose-estimation-application-with-nvidia-deepstream?

https://docs.nvidia.com/tlt/tlt-user-guide/text/deepstream_tlt_integration.html#installation-prerequisites

1 Like
  • I am able to generate the etlt model and tensorRT engine files by training the bpnet using COCO dataset.
  • I want to run this model in the deepstream pipeline. Could you please let me know the process of running this in the deepstream pipeline. I mean is there any custom post-process script or osd script, I have to write so that I can be able to run?