Jetson-inference is a training guide for inference on the TX1 and TX2 using NVIDIA Deep Learning GPU Training System (DIGITS).
This blog details the summary of original Jetson-inference training from NVIDIA with a focus on inference part.
You can learn about following details in this developer wiki from RIdgeRun Engineering : NVIDIA Xavier - Deep Learning - Deep Learning Tutorials - Jetson Inference
Building jetson-inference. Classifying Images with ImageNet. Locating Object Coordinates using DetectNet. Image Segmentation with SegNet. and run a Live Demo. With jetson-inference you can deploy deep learning examples on theNVIDIA Xavier in a matter of minutes. An example application is shown below. The input is an image and it outputs the most likely class and the probability that the image belongs to that class using ImageNetclassification network. ImageNet is a classification network trained with a database of 1000 objects.
It detects the image as 'Boston bull, Boston terrier' with imagenet class id of 0195 at 96.305% classification accuracy. Image recognition networks output a class probabilities corresponding to the entire input image.
Detection networks, on the other hand, find where in the image those objects are located. DetectNet accepts an input image, and outputs the class and coordinates of the detected bounding boxes.
If you are new to the Xavier or planning on getting one, please visit ourJetson Xavier Wiki page.
Article related :
Read this blog on deep reinforcement learning Deep Reinforcement Learning on the Jetson Xavier
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