Goto

Collaborating Authors

Results


Setup Transfer Learning Toolkit with Docker on Ubuntu?

#artificialintelligence

When we talk about Computer vision products, most of them have required the configuration of multiple things including the configuration of GPU and Operating System for the implementation of different problems. This sometimes causes issues for customers and even for the development team. Keeping these things in mind, Nvidia released Jetson Nano, which has its own GPU, CPU, and SDKs, that help to overcome problems like multiple framework development, and multiple configurations. Jetson Nano is good in all perspectives, except memory, because it has limited memory of 2GB/4GB, which is shared between GPU and CPU. Due to this, training of custom Computer Vision models on Jetson Nano is not possible.