coral

⚓ Solana Program Framework

Self-hosted, local only NVR and AI Computer Vision software. With features such as object detection, motion detection, face recognition and more, it gives you the power to keep an eye on your home, office or any other place you want to monitor.

Coral issue tracker (and legacy Edge TPU API source)

Dual Edge TPU Adapter to use it on a system with single PCIe port on m.2 A/B/E/M slot

mango is a man-page generator for the Go flag, pflag, cobra, coral, and kong packages

Object detection for video surveillance

🚀 Executable NFT Protocol and Marketplace

MambaCoral-DiffDet code and Dataset

SCoralDet and SCoralDet Dataset

TensorFlow Lite, Coral Edge TPU samples (Python/C++, Raspberry Pi/Windows/Linux).

Offers a set of tools that create Granite UI authoring interfaces for Adobe Experience Manager components from Java code. This is a comprehensive solution that makes different widgets work in a coordinated manner, provides greater interactivity in AEM dialogs, and introduces additional features (customizable data lists, options selection, etc.)

Google Coral on the Raspberry Pi 4

Cat with prey detection on Raspberry Pi. Lock cat pet flap if prey is detected. Object detection implemented in TFLite with ImageNet v1 SSD. Inference on EdgeTPU (Google Coral USB). Stores images on AWS S3 and sends notifications to iOS device.

ROS package for Coral Edge TPU USB Accelerator

This is a PyTorch implementation of the Unsupervised Domain Adaptation method proposed in the paper Deep CORAL: Correlation Alignment for Deep Domain Adaptation. Baochen Sun and Kate Saenko (ECCV 2016).

Tools for annotating and developing machine learning models for benthic imagery

Docker with Raspbian, SSH and the Coral USB Edge TPU libraries.

This example will help you deploy a streaming camera feed with realtime people detection using the Coral Edge TPU for on-device ML inferencing.

Chapter 11: Transfer Learning/Domain Adaptation

Souce code of "Inter-seasons and Inter-households Domain Adaptation Based on DANNs and Pseudo Labeling for Non-Intrusive Occupancy Detection" (JSAI Journal) + "Two stages domain invariant representation learners solve the large co-variate shift in unsupervised domain adaptation with two dimensional data domains"(https://arxiv.org/abs/2412.04682).