Jennifer M Williams's starred repositories
getting-started
Getting started with Docker
CameraTraps
PyTorch Wildlife: a Collaborative Deep Learning Framework for Conservation.
STMems_Standard_C_drivers
Platform-independent drivers for STMicroelectronics MEMS motion and environmental sensors, based on standard C programming language.
gst-rtsp-server
RTSP server based on GStreamer. This module has been merged into the main GStreamer repo for further development.
dlstreamer
This repository is a home to Intel® Deep Learning Streamer (Intel® DL Streamer) Pipeline Framework. Pipeline Framework is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines.
AIforEarthDataSets
Notebooks and documentation for AI-for-Earth-managed datasets on Azure
HighRes-net
Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competition. This is a ServiceNow Research project that was started at Element AI.
sample-videos
Sample videos for running inference
pipeline-server
Home of Intel(R) Deep Learning Streamer Pipeline Server (formerly Video Analytics Serving)
esp32cam-access-control
Open a door when a face is recognised using the ESP32-CAM
edgex-compose
EdgeX Foundry Docker Compose release compose files and tools for building EdgeX compose files
acoustic-bird-detection
Machine learning tools for acoustic bird detection
device-mqtt-go
Owner: Device WG
automated-vending
Deploy sensor fusion technology for an automated checkout that enables real-time insight about the products consumers are buying using the EdgeX Foundry* extensible framework.
tracecompass-ease-scripting
Provides examples, traces and documentation for the Trace Compass Scripting feature
CameraTraps
Tools for training and running detectors and classifiers for wildlife images collected from motion-triggered cameras.
go-ml-experiments
This repo is meant to be a place for me to work with the go ml pkgs and try out a few things.