deface is a simple command-line tool for automatic anonymization of faces in videos or photos. It works by first detecting all human faces in each video frame and then applying an anonymization filter (blurring or black boxes) on each detected face region. All audio tracks are discarded as well.
Source: GitHub – ORB-HD/deface: Video anonymization by face detection
The overview speaks for itself. The highest frame rate measured comes from a Raspberry 64-bit OS overclocked to 1950 MHz. The lowest is the standard 32-bit Raspbian at 1500 MHz. Frame rates are only based on model run time (interpreter->Invoke()). Grabbing and preprocessing of an image are not taken into account, nor plotting output boxes or texts. This way, models working with pictures instead of video streams can also be measured. The actual frame rate will be slightly lower when playing video’s or using
Source: Deep learning examples on Raspberry 32/64 OS – Q-engineering
This page assists you to build your deep learning modal on a Raspberry Pi or an alternative like Google Coral or Jetson Nano. For more general information about deep learning and its limitations, please see deep learning. This page deals more with the general principles, so you have a good idea of how it works and on which board your network can run. Detailed step-by-step recipes for installing of software can be found at deep learning software for Raspberry Pi 4 and alternatives.
Source: Deep learning with Raspberry Pi and alternatives in 2022 – Q-engineering
In this article, I’m going to walk through the architectural implementations that led from the first MobileNet architecture to the state of the art EfficientNet architecture, with a bunch of improvements in between. The MobileNet architecture was aimed at reducing the computational requirements for large neural networks without a large drop in performance on the classification task. This was with the particular goal of deploying the networks on edge devices, like mobile phones. After several years of iterat
Source: ttumiel
Adding the simple bypass connections yielded an increase of 2.9 percentage-points in top-1 accuracy and 2.2 percentage-points in top-5 accuracy without increasing model size.
Source: Review: SqueezeNet (Image Classification) | by Sik-Ho Tsang | Towards Data Science
Scroll to the bottom of the page to see reviews of Neural Networks.
SINGAPORE (BLOOMBERG) – Ray Dalio, the billionaire founder of Bridgewater Associates, is opening a family office in Singapore to run his investments and philanthropy throughout the region.
. Read more at straitstimes.com.
— Read on www.straitstimes.com/business/companies-markets/billionaire-ray-dalio-set-to-open-family-office-in-singapore
Source: Learn Go with Tests – Learn Go with tests
Golang tutorials.