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.

The transfer learning toolkit wraps the codes of 17 transfer learn ing models and provides integrated interfaces, allowing users to use those models by calling a simple function. It is easy for primary researchers to use this toolkit and to choose proper models for real-world applica tions. The toolkit is written in Python and distributed under MIT open source license. In this pape r, the current state of this toolkit is described and the necessary environment setting and usage are in troduced. Keywords: Transfer Learning, Toolkit 1. Introduction Transfer learning is a promising and important direction in machine lear ning, which attempts to leverage the knowledge contained in a source domain to improve the le arning performance or minimize the number of labeled samples required in a target domain.

NVIDIA's DeepStream SDK delivers a complete streaming analytics toolkit for AI-based video and image understanding, as well as multi-sensor processing. DeepStream is an integral part of NVIDIA Metropolis, the platform for building end-to-end services and solutions for transforming pixels and sensor data to actionable insights. The DeepStream application framework features hardware-accelerated building blocks, called plugins, that bring deep neural networks and other complex processing tasks into a stream processing pipeline. DeepStream enables real-time understanding of video and sensor data that is rich and multi-modal. The SDK uses the open source GStreamer to deliver high throughput with a low-latency streaming framework.