Pictured: Raspberry Pi 4GB, Pi Camera v2.1, Pimoroni Pan-Tilt HAT, Coral USB Accelerator
Pictured: Raspberry Pi 4GB, Pi Camera v2.1, Pimoroni Pan-Tilt HAT, Coral USB Accelerator
Pictured: Raspberry Pi 4GB, Pi Camera v2.1, Pimoroni Pan-Tilt HAT, Coral Edge TPU USB Accelerator

Portable computer vision and motion tracking on a budget.

Part 1 — Introduction 👋

Are you just getting started with machine/deep learning, TensorFlow, or Raspberry Pi? Perfect, this blog post is for you! I created rpi-deep-pantilt as an interactive demo of object detection in the wild. 🦁

UPDATE — Face detection and tracking added!

I’ll show you how to reproduce the video below, which depicts a camera panning and tilting to track my movement across a room.

  1. Deploy a TensorFlow Lite object detection model (MobileNetV3-SSD) to a Raspberry Pi.
  2. Send tracking instructions to pan / tilt servo motors using a proportional–integral–derivative controller…


TensorFlow Lite on Raspberry Pi 4 can achieve performance comparable to NVIDIA’s Jetson Nano at a fraction of the cost.

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Image Credit: raspberrypi.org

Originally published at bitsy.ai/3-ways-to-install-tensorflow-on-raspberry-pi.

With the new Raspberry Pi 400 shipping worldwide, you might be wondering: can this little powerhouse board be used for Machine Learning?

The answer is, yes! TensorFlow Lite on Raspberry Pi 4 can achieve performance comparable to NVIDIA’s Jetson Nano at a fraction of the dollar and power cost. You can achieve real-time performance with state-of-the-art neural network architectures like MobileNetV2 by adding a Coral Edge TPU USB Accelerator.

This performance boost unlocks interesting offline TensorFlow applications, like detecting and tracking a moving object.


Square brackets are interpreted as a pattern on the command line. Two improvements over the default behavior.

Originally published via bitsy.ai

I’ve been using zsh and ohmyz.sh for years, but I still occasionally forget this shell interprets square brackets as a pattern on the command line.

Here’s an example:

$ which $SHELL
/usr/bin/zsh
$ pip install -e .[develop,plugins]
zsh: no matches found: [develop,plugins]
$ pip install -e .\[develop,plugins\] 
Obtaining file:///home/leigh/projects/OctoPrint

Hands-on Tutorials

Learn how to use TensorFlow.js to speed up data annotation

Introduction 👋

Originally published via bitsy.ai

Data collection and preparation are the foundation of every machine learning application. You’ve heard it before: “Garbage in, garbage out” in reference to an algorithm’s limited capability to correct for inaccurate, poor-quality, or biased input data.

The cost of quality annotated data prompted a cottage industry of tools/platforms for speeding up the data labeling process. Besides the SaaS/on-prem startup ecosystem, each of the major cloud providers (AWS, Microsoft, Google) launched an automated data labeling product in the last two years. Understandably, these services are often developed with Premium/Enterprise users, features, and price points in mind.

On a limited budget, am I stuck labeling everything by hand?

Good…


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Tiny, low-cost object detection and classification.

Part 1 — Introduction

For roughly $100 USD, you can add deep learning to an embedded system or your next internet-of-things project.

Are you just getting started with machine/deep learning, TensorFlow, or Raspberry Pi? Perfect, this blog series is for you!

In this series, I will show you how to:

  1. Convert a model to TensorFlow Lite, a model format optimized for embedded and mobile devices.
  2. Accelerate inferences of any TensorFlow Lite model with Coral’s USB Edge TPU Accelerator and Edge TPU Compiler.
  3. Employ transfer learning to re-train MobileNetV2 with a custom image…

Leigh Johnson

Head over to https://bitsy.ai/ for more Applied ML and Edge ML projects. Google Developer Expert, Machine Learning. Staff ML Eng @ Slack.

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