Google Just Turned the RPi into a Supercomputer...
TLDRIn this video, the host challenges the capabilities of a Raspberry Pi by integrating Google's Coral AI Edge TPU to perform video object recognition, a task typically beyond its modest computational resources. After setting up the Coral AI and installing necessary packages, the host demonstrates real-time video object detection using pre-built models, achieving impressive results with high classification accuracy, showcasing the Raspberry Pi's potential as a supercomputer for AI tasks.
Takeaways
- 💡 Google's Coral AI Edge TPU is a device that can significantly enhance the capabilities of a Raspberry Pi by enabling it to run machine learning algorithms.
- 🔧 The setup process for the Coral AI involves installing it on the Raspberry Pi, connecting it, and downloading necessary packages from coral.ai.
- 📷 The video demonstrates the installation of a camera on the Raspberry Pi to facilitate video object recognition tasks.
- 🖥️ SSH is used to connect to the Raspberry Pi and gain superuser access to install the Coral AI and necessary libraries.
- 🛠️ The Coral AI's documentation provides guidance for installing packages and setting up the device on a Linux-based system like Raspbian.
- 📝 The script involves downloading and running pre-built models to test the Coral AI's functionality and ensure everything is working correctly.
- 🕒 The video shows that the Coral AI can perform inference very quickly, with the example of classifying an image in just 5.8 milliseconds.
- 📈 The script includes a demonstration of real-time video object detection, showcasing the system's ability to classify objects in a video stream with high accuracy.
- 🎥 The Raspberry Pi, with the Coral AI, is capable of running a broad object classification model in real-time, which can identify a wide range of objects.
- 🎸 The video concludes with a humorous note, as the system incorrectly identifies a building as a prison and classifies a guitar with a high level of confidence.
Q & A
What is the purpose of the video?
-The video aims to test the Raspberry Pi's ability to handle video object recognition, a challenging computation, by using Google's Coral AI Edge TPU.
Why would video object recognition normally be difficult on a Raspberry Pi?
-Video object recognition would normally be difficult due to the Raspberry Pi's modest compute resources, which aren't powerful enough for such tasks without additional hardware like the Coral AI Edge TPU.
What is the Coral AI Edge TPU and its purpose?
-The Coral AI Edge TPU is a piece of hardware designed to accelerate machine learning algorithms, allowing low-power devices like the Raspberry Pi to perform tasks like object recognition more efficiently.
What is the first step in setting up the Raspberry Pi for object recognition?
-The first step is installing a camera on the Raspberry Pi, followed by connecting and booting up the Coral AI hardware.
How does the user connect to the Raspberry Pi for setup?
-The user connects to the Raspberry Pi over SSH (Secure Shell) from their laptop by using the command 'SSH Pi at the host name' to log in remotely.
What is the process for setting up the Coral AI on the Raspberry Pi?
-The process involves selecting the Linux option on the Coral AI website, downloading necessary packages, and installing the Pi Coral library by running commands to set up the software environment.
What model does the user run to test the setup?
-The user runs a pre-built image classification model using Python, which involves downloading code from GitHub and executing the 'classify.py' script with a parrot image as input.
How does the Coral AI perform when running the object classification model?
-The model executes successfully, recognizing the parrot species with a high level of confidence. The first trial takes longer due to model loading, but subsequent trials are faster, with an inference time of 5.8 milliseconds.
What additional task does the user attempt after image classification?
-The user attempts real-time video object detection by using a Raspberry Pi camera and running a broad object classification model to identify objects from a live video feed.
How accurate is the Raspberry Pi's real-time video object detection?
-The Raspberry Pi's real-time video object detection is highly accurate, as it correctly classifies various objects, including a guitar, with a high degree of certainty (91%).
Outlines
🔍 Setting Up Object Recognition with Coral AI on a Raspberry Pi
In this section, the speaker introduces their goal of testing a Raspberry Pi's ability to handle video object recognition, a computation-heavy task. They explain that normally, a Raspberry Pi's limited resources wouldn't support such a demanding operation. However, with Google's Coral AI Edge TPU hardware, which is designed specifically to run machine learning algorithms, the speaker aims to transform the Raspberry Pi into a capable device for this purpose. They begin the setup by installing a camera onto the Raspberry Pi and connecting the Coral AI hardware. The process involves downloading necessary software packages from coral.ai to enable the Raspberry Pi to interface with Coral AI for machine learning tasks.
💻 SSH Access and Installing Coral AI Libraries
The speaker details how they connect to the Raspberry Pi via SSH to install the necessary libraries and dependencies for Coral AI. They explain the process of logging in as a super user to access root privileges. They follow documentation from coral.ai to install libraries specific to Linux, as the Raspberry Pi runs Raspbian OS. The speaker inputs a few commands to download and set up the pi Coral library on the Raspberry Pi. The installation process is smooth and straightforward, involving a few command-line actions to prepare the Raspberry Pi for machine learning tasks.
⚙️ Running Pre-built Models on Coral AI
After setting up the Coral AI, the speaker tests pre-built models to ensure everything is functioning correctly. They download code from GitHub and install the required packages using a script. Next, they run a classification model using Python, which performs inference on an image of a parrot. The model successfully identifies the parrot with high accuracy. The speaker notes that the first inference run took longer due to model loading time, but subsequent runs were much faster, highlighting the efficiency of Coral AI for object recognition tasks.
🎥 Moving from Image Recognition to Real-time Video
The speaker transitions from static image classification to real-time video object detection. They download additional code and models from GitHub, specifically tailored for the Raspberry Pi camera. By running a Python script, the speaker sets up a real-time video stream, where the Raspberry Pi performs inference on live video data. The object classification model used is capable of identifying a wide range of objects, as demonstrated when it successfully recognizes a sliding door and window in the video.
🎸 Real-time Object Detection on Moving Targets
In this final section, the speaker moves the Raspberry Pi camera in real-time and showcases how the object detection model works with moving objects. Despite some humorous misclassifications, such as a building being labeled a prison, the model performs well overall. The demonstration concludes with the Raspberry Pi accurately identifying a guitar when the speaker steps into the video frame, achieving a 91% confidence level in its classification, showcasing the model’s robustness in real-time scenarios.
Mindmap
Keywords
💡Raspberry Pi
💡Coral AI
💡Edge TPU
💡Machine Learning
💡Object Recognition
💡Inference
💡Pre-trained Models
💡SSH (Secure Shell)
💡ResNet
💡Real-time Video Processing
Highlights
Pushing the Raspberry Pi to its limit with video object recognition.
Google's new Coral AI Edge TPU transforms the Raspberry Pi into a supercomputer.
Coral AI Edge TPU is designed to run machine learning algorithms efficiently on low-power devices.
Setting up the Raspberry Pi with a camera and connecting the Coral AI.
SSH into the Raspberry Pi and using sudo to gain root access.
Installing packages from coral.ai for setting up the Coral AI.
Successfully installing the Pi Coral library on the Raspberry Pi.
Testing pre-built machine learning models on the Raspberry Pi.
Running a classification model to identify images with high accuracy.
The model identifies a parrot species, 'Scarlet Macaw,' with a high level of confidence.
Exploring VNC viewer to visually monitor the Raspberry Pi's desktop and inference process.
Running real-time video object detection using image recognition models.
Downloading pre-trained models like MobileNet and Inception for video object classification.
The Raspberry Pi accurately classifies real-time video objects with confidence.
The Pi successfully classifies a guitar with 91% certainty during a live video feed.