Real-time portrait segmentation for mobile devices
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Updated
Jan 17, 2021 - Jupyter Notebook
Real-time portrait segmentation for mobile devices
Neuralet is an open-source platform for edge deep learning models on edge TPU, Jetson Nano, and more.
Social Distancing Detector using deep learning and capable to run on edge AI devices such as NVIDIA Jetson, Google Coral, and more.
The easiest way to count pedestrians, cyclists, and vehicles on edge computing devices or live video feeds.
Dockerfile and docker-compose file to enable google coral USB accelerators in containers on Synology DSM 7
Use the TensorFlow Object Detection API to train models for the Google Coral Edge TPU.
Performance testing of 24 Machine Learning models on Raspberry Pi using TensorFlow Lite and Google Coral USB Accelerator
Neuralet edge deep learning models library. Neuralet is an open-source platform for edge deep learning models on GPU, TPU, and more.
Use the Google Coral USB Accelerator for deep learning.
Machine Learning Based Real-Time Traffic Light Alert on Your Car with Raspberrypi
Dual Edge TPU Adapter to use it on a system with single PCIe port on m.2 A/B/E/M slot
Coral Edge TPU compilable version of DeepLab V3
A simple and real-time traffic monitoring solution using TensorFlow and OpenCV.
Object detection at the edge, with Google's Coral dev board
This work consists of designing a miniature autonomous car that can follow a road, detect signalisations and obstacles.
Google Coral TPU DKMS Driver package for Fedora, RHEL, OpenSUSE, and OpenMandriva
Streamlining the process for seamless execution of PyCoral in running TensorFlow Lite models on an Edge TPU USB.
Testing Google Coral USB Accelerator's performance with 04 models of Raspberry Pi. Results have shown 10 times faster inferencing speeds with the Coral hardware attached.
use edgetpu_compiler from anywhere with docker
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