TensorFlow-Object-Detection-On-The-Raspberry-Pi

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TensorFlow-Object-Detection-on-the-Raspberry-Pi is an Open Source tutorial that guides users through setting up TensorFlow's Object Detection API on a Raspberry Pi. It simplifies the installation process with updated instructions for TensorFlow and protobuf compiler.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is TensorFlow-Object-Detection-on-the-Raspberry-Pi?

TensorFlow-Object-Detection-on-the-Raspberry-Pi provides a comprehensive, step-by-step tutorial for implementing TensorFlow's Object Detection API on a Raspberry Pi. This guide enables users to perform real-time object detection on live video feeds from a Picamera or USB webcam. It includes updated instructions for easily installing TensorFlow and the protobuf compiler, making the setup process more accessible. The repository also features a 'Pet Detector' program as an example application, demonstrating how to use object detection to send text alerts when specific objects are detected. This tutorial is ideal for developers looking to create unique detection applications on the Raspberry Pi.

Best used for

Ideal for developers who need to implement real-time object detection on a Raspberry Pi, create custom detection applications, and integrate computer vision into embedded systems. Especially valuable for those looking for a clear, step-by-step guide to get started with TensorFlow on low-power devices.

Common actions

set up object detection

implement AI on Raspberry Pi

create custom detection apps

integrate computer vision

"AI Agents"face swappinglow-code/no-codeautomated workflowcollaborationopen-sourcegithub copilotdeepfakeworkflows

Capabilities

Key features

TensorFlow Object Detection API
Raspberry Pi setup guide
Picamera/USB webcam support
Real-time object detection
Pet detector example
Updated installation instructions

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What Raspberry Pi models are supported by this tutorial?

The guide was originally written for a Raspberry Pi Model 3B running Raspbian Stretch v9. However, it is likely to work for newer versions of TensorFlow and potentially other Raspberry Pi models, though specific compatibility may vary.

Can I use my own trained object detection model with this setup?

Yes, you can use your own trained model. The tutorial provides instructions on how to integrate a custom frozen inference graph and label map into the object_detection directory and modify the script to use your model instead of the default SSDLite-MobileNet.

Does the pet detector example require a Twilio account?

Yes, the Pet_detector.py script uses Twilio to send text messages when a pet is detected. You will need to set up a Twilio account and configure specific environment variables (TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, MY_DIGITS, TWILIO_DIGITS) for the program to function correctly.

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