Final Year Project - Traffic Sign Detection on a custom Raspberry Pi RC car. Manual and Autonomous.
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Updated
Nov 22, 2022 - Jupyter Notebook
Final Year Project - Traffic Sign Detection on a custom Raspberry Pi RC car. Manual and Autonomous.
Basic Text2Image generation using a CNN designed for a coral tpu
A framework to make Google Coral hardware easier to install, manage, develop, test, and deploy.
Face recognition on video using edgetpu.
m.2 B+M Coral TPU card for Raspberry Pi CM4
Command line tool for capturing video with the Google Coral EdgeTPU camera module. Akin to raspivid for the Raspberry Pi.
Prometheus Exporter for EdgeTPU Metrics
Comparative of the performance of computer vision models designed by hand and models designed using Hardware-Aware Neural Architecture Search (HW-NAS)
A library to help with the development of AI models with Keras, with a focus on edge / IoT applications. Based originally on https://github.com/yingkaisha/keras-unet-collection
Traffic sign recognition for Raspberry Pi with Coral USB Accelerator.
Person detecting security camera monitor.
A python framework for designing high-performance Computer Vision pipelines at the Edge. Supports Coral Edge TPU, Raspberry Pi Camera, and more.
A real world application of pytorch, transfer learning and edge TPU to detect when my dog uses the restroom.
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.
some scripts I used to test Google's Edge TPU
The products,docs,resources,software,tools,scripts,models,demos
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