Raspberry pi 4 machine learning

Raspberry pi 4 machine learning DEFAULT

Machine learning and depth estimation using Raspberry Pi

One of our engineers, David Plowman, describes machine learning and shares news of a Raspberry Pi depth estimation challenge run by ETH Zürich (Swiss Federal Institute of Technology).

Spoiler alert – it&#;s all happening virtually, so you can definitely make the trip and attend, or maybe even enter yourself.

What is Machine Learning?

Machine Learning (ML) and Artificial Intelligence (AI) are some of the top engineering-related buzzwords of the moment, and foremost among current ML paradigms is probably the Artificial Neural Network (ANN).

They involve millions of tiny calculations, merged together in a giant biologically inspired network – hence the name. These networks typically have millions of parameters that control each calculation, and they must be optimised for every different task at hand.

This process of optimising the parameters so that a given set of inputs correctly produces a known set of outputs is known as training, and is what gives rise to the sense that the network is “learning”.

A popular type of ANN used for processing images is the Convolutional Neural Network. Many small calculations are performed on groups of input pixels to produce each output pixel

Machine Learning frameworks

A number of well known companies produce free ML frameworks that you can download and use on your own computer. The network training procedure runs best on machines with powerful CPUs and GPUs, but even using one of these pre-trained networks (known as inference) can be quite expensive.

One of the most popular frameworks is Google’s TensorFlow (TF), and since this is rather resource intensive, they also produce a cut-down version optimised for less powerful platforms. This is TensorFlow Lite(TFLite), which can be run effectively on Raspberry Pi.

Depth estimation

ANNs have proven very adept at a wide variety of image processing tasks, most notably object classification and detection, but also depth estimation. This is the process of taking one or more images and working out how far away every part of the scene is from the camera, producing a depth map.

Here’s an example:

Depth estimation example using a truck

The image on the right shows, by the brightness of each pixel, how far away the objects in the original (left-hand) image are from the camera (darker = nearer).

We distinguish between stereo depth estimation, which starts with a stereo pair of images (taken from marginally different viewpoints; here, parallax can be used to inform the algorithm), and monocular depth estimation, working from just a single image.

The applications of such techniques should be clear, ranging from robots that need to understand and navigate their environments, to the fake bokeh effects beloved of many modern smartphone cameras.

Depth Estimation Challenge

C V P R conference logo with dark blue background and the edge of the earth covered in scattered orange lights connected by white lines

We were very interested then to learn that, as part of the CVPR (Computer Vision and Pattern Recognition) conference, Andrey Ignatov and Radu Timofte of ETH Zürich were planning to run a Monocular Depth Estimation Challenge. They are specifically targeting the Raspberry Pi 4 platform running TFLite, and we are delighted to support this effort.

For more information, or indeed if any technically minded readers are interested in entering the challenge, please visit:

The conference and workshops are all taking place virtually in June, and we’ll be sure to update our blog with some of the results and models produced for Raspberry Pi 4 by the competing teams. We wish them all good luck!

Sours: https://www.raspberrypi.org/blog/machine-learning-and-depth-estimation-using-raspberry-pi/

Machine learning on Raspberry Pi just took a big step forward

raspberry-pi-board-tinyml-edit.jpg

Raspberry Pi is a capable little machine, but if you're interested in developing your own embedded machine-learning applications, training custom models on the platform has historically been tricky due to the Pi's limited processing power.

Must-read developer content

But things have just taken a big step forward. Yesterday, Edge Impulse, the cloud-based development platform for machine learning on edge devices, announced its foray into embedded Linux with full, official support for the Raspberry Pi 4. As a result, users can now upload data and train their own custom machine-learning algorithms in the cloud, and then deploy them back to their Raspberry Pi. 

SEE: C++ programming language: How it became the foundation for everything, and what's next (free PDF) (TechRepublic)

Four new machine-learning software development kits (SDKs) for Raspberry Pi are available week, including C++, Go, Node.js and Python, allowing users to program their own custom applications for inferencing. Support for object detection has also been added, meaning Raspberry Pi owners can use camera data captured on their device to train their own custom object detection algorithms, instead of having to rely on 'stock' classification models.

This will allow developers to build their own computer vision applications, either by using a Raspberry Pi camera or by plugging a webcam into one of the Raspberry Pi 4's USB slots. Edge Impulse demonstrated the new machine-learning capabilities in a video that showed one of its engineers building a system capable of recognizing multiple objects through a camera from scratch, before deploying it back to a Raspberry Pi.

As well as collecting data from a camera microphone, the new SDKs allow users to capture data from any other type of sensor that can be connected to Raspberry Pi, including accelerometers, magnetometers, motion sensors, humidity and temperature sensors -- the list goes on.

Alasdair Allan, Raspberry Pi's technical documentation manager, said that while performance metrics for Edge Impulse were "promising", it still fell a little below that which they'd seen using Google's TensorFlow Lite framework, which also allows users to build machine-learning models for deep-learning tasks like image and speech recognition on the Raspberry Pi. 

SEE: Raspberry Pi and machine learning: How to get started (TechRepublic)

However, Allan noted that the huge variety in data types and use cases for machine-learning applications made it "really hard to compare performance across even very similar models."

He added: "New Edge Impulse announcement offers two very vital things: a cradle-to-grave framework for collecting data and training models then deploying these custom models at the edge, together with a layer of abstraction.

"Increasingly we're seeing deep learning eating software as part of a general trend towards increasing abstraction, sometimes termed lithification, in software. Which sounds intimidating, but means that we can all do more, with less effort. Which isn't a bad thing at all."

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Sours: https://www.techrepublic.com/article/machine-learning-on-raspberry-pi-just-took-a-big-step-forward/
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Machine learning tool ported to Raspberry Pi 4

Edge Impulse in the US now supports a wider range of processors for embedded machine learning applications with the Raspberry Pi 4 board. Developers can use the existing support for low-power MCUs or venture into processor classes that run embedded Linux if highest performance is the objective.

The Raspberry Pi 4 board, developed in the UK, uses the GHz Broadcom BCM multicore processor with four ARM A72 cores and supports embedded Linux with portable container technology that gives access to AI applications that also run in the data centre. 

"We've brought the same great user experience our developers are already familiar with into the Linux domain (using full hardware acceleration on the Pi 4), with a refreshed set of tools and capabilities that makes deploying embedded machine learning models on Linux as easy as … Pi," says Zin Thein Kyaw, Lead User Success Engineer at Edge Impulse.

"[This] announcement from Edge Impulse is a big step, and makes machine learning at the edge that much more accessible. With full support for Raspberry Pi, you now have the ability to take data, train against your own data in the cloud on the Edge Impulse platform, and then deploy the newly trained model back to your Raspberry Pi," said Alasdair Allan, Technical Documentation Manager at Raspberry Pi.

Edge Impulse also announced the launch of support for true object detection as part of its computer vision ML pipeline. Users can use a Raspberry Pi camera or plug in a standard USB web camera into one of the available USB slots on the Pi, and harness the raw power of higher performance compute and more sophisticated frameworks and libraries to facilitate computer vision applications.

For audio applications, says the company, users can plug a standard USB microphone into one of the available USB slots on the Pi. For sensor fusion, the pin GPIO header on the Pi can be employed to connect to their favorite sensors as well.

To get started, the company offers a Raspberry Pi 4 guide. In addition, an object detection tutorial explains how to easily train an object detection model. SDKs for Python, Node.js, Go, and C++ are provided so users can easily build their own custom apps for inferencing.

Edge Impulse

Related articles:

Other articles on eeNews Europe 

Sours: https://www.eenewseurope.com/news/machine-learning-tool-ported-raspberry-pi-4
Machine Learning on Raspberry Pi - THE EASIEST WAY - No TensorFlow and no Open CV-

Benchmarking Machine Learning on the New Raspberry Pi 4, Model B

At the start of last month I sat down to benchmark the new generation of accelerator hardware intended to speed up machine learning inferencing on the edge. So I’d have a rough yardstick for comparison, I also ran the same benchmarks on the Raspberry Pi. Afterwards a lot of people complained that I should have been using TensorFlow Lite on the Raspberry Pi rather than full blown TensorFlow. They were right, it ran a lot faster.

Then with the release of the AI2GO framework from Xnor.ai, which uses next generation binary weight models, I looked at the inferencing speeds of these next generation of models in comparison to ‘traditional’ TensorFlow. This also ran a lot faster.

The new Raspberry Pi 4, Model B. (📷: Alasdair Allan)

However with today’s launch of the new Raspberry Pi 4, Model B, it’s time to go back and look again at the benchmarks and see how much faster the new Raspberry Pi 4 is than the previous model. Spoiler? It’s a lot faster.

Headline Results From Benchmarking

Overall the new Raspberry Pi 4 is considerably faster than our original results from the Raspberry Pi 3, and the followup looking at the AI2GO platform.

Inferencing time in milli-seconds for the Raspberry Pi 3 (blue, left) and Raspberry Pi 4 (green, right).

We see an approximate ×2 increase in inferencing speed between the original TensorFlow benchmarks and the new results from the Raspberry Pi 4, along with a similar increase in inferencing speed using the Xnor AI2GO platform.

Benchmarking results in milli-seconds for MobileNet v1 SSD depth model and the MobileNet v2 SSD model, both trained using the Common Objects in Context (COCO) dataset with an input size of ×, for the Raspberry Pi 3, Model B+ (left), and the new Raspberry Pi 4, Model B (right).

However we see a much bigger change when looking at the results from the Coral USB Accelerator from Google. The addition of USB to the Raspberry Pi 4 means we see an approximate ×3 increase in inferencing speed between our original results and the new results.

Conversely the inference times for the Coral USB Accelerator when it was connected via USB 2, rather than the new USB 3 bus, actually increased by a factor of ×2. This somewhat surprising result is likely due to the architectural changes made to the new Raspberry Pi.

“These results showcase both the increased NEON compute throughput of Raspberry Pi 4, and the benefit of including a pair of USB ports in the design: we primarily intended these to be used to attach mass-storage devices, so it’s interesting to see another application in the wild.”Eben Upton, Founder, Raspberry Pi Foundation

Part I — Benchmarking

A More In-Depth Analysis of the Results

Our original benchmarks were done using both TensorFlow and TensorFlow Lite on a Raspberry Pi 3, Model B+, and these were rerun using the new Raspberry Pi 4, Model B, with 4GB of RAM. Inferencing was carried out with the MobileNet v2 SSD and MobileNet v1 depth SSD models, both models trained on the Common Objects in Context (COCO) dataset. Benchmarks using the Coral USB Accelerator were similarly rerun with the accelerator dongle attached to both the USB 2 and USB 3 bus of the Raspberry Pi 4.

ℹ️ Information Our original benchmarks compared inferencing on the following platforms; the Coral Dev Board, the NVIDIA Jetson Nano, the Coral USB Accelerator with a Raspberry Pi 3, Model B+, the original Movidus Neural Compute Stick with a Raspberry Pi 3, Model B+, and the second generation Intel Neural Compute Stick 2 again with a Raspberry Pi 3, Model B+. Finally as a yard stick, we ran the same models again on my Apple MacBook Pro (), which has a quad-core GHz Intel Core i7, and a vanilla Raspberry Pi 3, Model B+ without any acceleration.

The Xnor.ai AI2GO platform was benchmarked using their ‘medium’ Kitchen Object Detector model. This model is a binary weight network, and while the nature of the training dataset is not known, some technical papers around the model are available.

A single × pixel test image was used containing two recognisable objects in the frame, a banana🍌 and an apple🍎. The image was resized down to × pixels before presenting it to each model, and the model was run 10, times before an average inferencing time was taken. The first inferencing run, which takes longer due to loading overheads in the case of TensorFlow models, was discarded.

Benchmarking results in milli-seconds for MobileNet v1 SSD depth model and the MobileNet v2 SSD model, both trained using the Common Objects in Context (COCO) dataset with an input size of ×

⚠️Warning While benchmarks were run for TensorFlow, AI2GO, and the Coral USB Accelerator, updates to Raspbian necessary to support the board — from Raspbian Stretch to Raspbian Buster — mean that the installed Python version has moved from Python to This change meant that I was unable to run benchmarks for TensorFlow Lite, the Movidus Neural Compute Stick, or the Intel Neural Compute Stick 2. While the TensorFlow Lite problems are probably going to be resolvable fairly easily, moving the Intel OpenVINO framework from Python to will take some time to accomplish. So you should therefore not expect the Intel Neural Compute Stick to work with the Raspberry Pi 4 in the near term.

Overall for CPU-based models we see a rough ×2 increase in performance.

With roughly twice the NEON capacity more than the Raspberry Pi 3, we would expect this order of speedup in performance for well-written NEON kernels and as expected, after thermal throttling issues were addressed, we saw a rough ×2 increase in performance for both the MobileNet v1 models, and the Xnor.ai AI2GO framework.

The performance improvements seen with the AI2GO platform binary weight models, with an observed inferencing time of ms on an unaccelerated Raspberry Pi 4, is directly comparable with the MacBook Pro () which had an inferencing time of 71 ms for MobileNet v2 SSD.

However the much smaller speed up we see for the MobileNet V2 models is intriguing, suggesting that the v2 model may be using very different TensorFlow operations, which are not optimised well for the architecture.

Inferencing time in milli-seconds for the for MobileNet v1 SSD depth model (left hand bars) and the MobileNet v2 SSD model (right hand bars), both trained using the Common Objects in Context (COCO) dataset with an input size of × The (single) bars for the Xnor AI2GO platform use their proprietary binary weight model. All measurements on the Raspberry Pi 3, Model B+, are in yellow, measurements on the Raspberry Pi 4, Model B, in red. Other platforms are in green.

While inferencing using TensorFlow Lite wasn’t carried out, due to the move from Python to breaking the Python wheel, I would also expect to see a rough ×2 speedup during inferencing for these models for the same reason.

However probably the biggest takeaway for those wishing to use the new Raspberry Pi 4 for inferencing is the performance gains seen with the Coral USB Accelerator. The addition of USB to the Raspberry Pi 4 means we see an approximate ×3 increase in inferencing speed over our original results.

Benchmarking results in milli-seconds for the Coral USB Accelerator using the MobileNet v1 SSD depth model and the MobileNet v2 SSD model, both trained using the Common Objects in Context (COCO) dataset for the Raspberry Pi 3, Model B+ (left), and the Raspberry Pi 4, Model B over USB (middle) and USB 2 (right).

That is a decrease in inferencing time from ms down to ms for the MobileNet v1 depth SSD model, and a decrease from ms down to ms for the MobileNet v2 SSD model. That actually brings the inferencing times for the the Raspberry Pi 4 below those from the Coral Dev Board, which had and ms times for the models respectively.

Conversely however the inference times for the Coral USB Accelerator when it was connected via USB 2, rather than the new USB 3 bus, actually increased by a factor of ×2. This somewhat surprising result is mostly likely due to the architectural changes made to the new Raspberry Pi. With the XHCI host now at the far end of the PCI Express bus, there’s potentially much more latency in the system. Depending on the traffic pattern you could imagine that blocking, as opposed to streaming, use of the channel could well be slower.

ℹ️ Information While the pre-release board I was using was had 4GB of RAM it’s unlikely that for the Coral USB Accelerator, where inferencing is done ‘off board’ on the Edge TPU itself, that this would significantly affect the result and would expect to see the same benchmark numbers, or at worst broadly similar, for Raspberry Pi 4 boards with 1GB or 2GB of RAM on board.
Environmental Factors

While inferencing speed is probably our most important measure, these are devices intended to do machine learning at at the edge. That means we also need to pay attention to environmental factors. Designing a smart object isn’t just about the software you put on it, you also have to pay attention to other factors, and here we’re especially concerned with heating and cooling, and the power envelope. Because it might be necessary to trade off inferencing speed against these other factors when designing for the Internet of Things.

Therefore, along with inferencing speed, when discussing edge computing devices it’s also important to ascertain the heat and power envelopes. So lets go do that now.

Power Consumption

Current measurements were made using a multi-meter inline with the USB cable with a reported accuracy of ± A (10mA).

Idle and peak current consumption for our benchmarked platforms before and during extended testing. All measurements for USB connected accelerated platforms were done using a Raspberry Pi 3, Model B+.

Except for the MacBook Pro, all of our platforms take a nominal 5V input supply. However in reality the voltage will bounce around somewhat due to demands made by the board, and most USB supplies actually sit at around + to +V. So when doing rough calculations to get the power (in Watts) I’d normally take a the voltage of a USB supply to be +V as a good supply will usually try and maintain the supplied voltage around this figure despite rapid fluctuations in current draw.

Those fluctuations in demand is something that happens a lot with when you’re using peripherals with the Raspberry Pi and often cause brown outs, and they are something that a lot of USB chargers — designed to provide consistent current for charging cellphones — usually don’t cope with all that well. This is one of the reasons why the new Raspberry Pi 4 has transitioned from micro USB to the USB-C standard.

Idle current (in green, left hand bars) compared to peak current (in yellow, right hand bars).

During our previous benchmarking we saw that the Raspberry Pi 3, Model B+, was comparatively power hungry, with only the NVIDIA Jetson Nano needing a larger power envelope. Our new measurements show that the new Raspberry Pi 4 is the worst performer of the platforms, needing over 1,mA at peak during extended testing. It also has the highest resting consumption, drawing more idle current than the Coral Dev Board.

Heating and Cooling

In previous extended tests we saw Raspberry Pi temperatures approach, but not exceed, the 80°C point where thermal throttling of the CPU would occur during inferencing using TensorFlow and TensorFlow Lite models.

My initial results for the AI2GO benchmark gave an inferencing time of ms, which was considerably higher than expected. However during these test runs we observed temperatures well above the thermal throttling threshold.

$ vcgencmd measure_temp temp='C $ vcgencmd measure_clock arm frequency(48)=

The addition of a small fan, driven from the Raspberry Pi’s own GPIO headers, was sufficient to keep the the CPU temperature stable at 45°C during testing.

A small fan was sufficient to keep the CPU temperature stable.

After stabilising the CPU temperature the inferencing speed time decreased, dropping from ms down to ms. This result is a more in line with the expected result with roughly twice the NEON capacity on the new board.

However due to this necessity to actively cool the Raspberry Pi during testing I’d recommend that, if you intended to use the new board for inferencing for extended periods, you should add at least a passive heatsink. Although to ensure that you avoid the possibility of CPU throttling entirely it’s likely that a small fan might be a good idea.

Because let’s face it, CPU throttling can spoil your day.

Summary

The performance increase seen with the new Raspberry Pi 4 makes it a very competitive platform for machine learning inferencing at the edge.

Benchmarks using the AI2GO platform and the binary weight network models shows inferencing time competitive with the NVIDIA Jetson Nano using their TensorRT optimised TensorFlow models. However it is the addition of the USB bus on the new board that makes it not just speed, but price competitive with our previous ‘best in class’ board, the Coral Dev Board from Google.

Priced at $35 the 1GB version of the new Raspberry Pi 4 is significantly cheaper than the $ Coral Dev Board. Adding an additional $ for the Coral USB Accelerator to the price of the Raspberry Pi means that you can outperform the previous ‘best in class’ board for a cost of $ That’s a saving of $ over the cost of the Coral Dev Board, for better performance.

Part II — Methodology

Preparing the Raspberry Pi

Fortunately despite the differences between the new Raspberry Pi 4 and previous generations, installation of the supporting software we needed wasn’t too different. However, there were some hiccups along the way.

Installing the Coral Software

Unfortunately as I was running the Coral Software Development Kit on a brand new Raspberry Pi board that was still secret and the team at Google hadn’t even heard about yet, I couldn’t install things as normal. Fortunately there were only some small fixable problems with the shipping install script. It’s likely that these problem will be quickly resolved. However until then you’ll need to make some tweaks before things will install and run.

Go ahead and download the software development kit using , and uncompress the bundle into your home directory.

$ wget http://storage.googleapis.com/cloud-iot-edge-pretrained-models/edgetpu_api.tar.gz$ tar -xvzf edgetpu_api.tar.gz $ cd python-tflite-source

But before running the installation script I had to make some changes. The script relies on yet another script called to figure out what platform the Coral SDK is being deployed into, and install the appropriate libraries. I went ahead and added the following lines,

elif [[ "$board_version" == "Raspberry Pi"* ]]; then platform="other_raspberry" echo -e "${GREEN}Recognised some other Raspberry Pi"

into the decision tree in the script. Which means that the contents of the file,

$ cat /proc/device-tree/model Raspberry Pi ? Rev

for my pre-release version of the hardware and software was recognised. I then modified the script to accept this as a valid answer,

elif [[ "$platform" == "raspberry_pi_3b" ]] || [[ "$platform" == "raspberry_pi_3b+" ]] || [[ "$platform" == "other_raspberry" ]];then

in both places where the script checks for the platform, which would be in lines 64 and We’ll also need to split line 92 into two separate lines,

sudo udevadm control --reload-rules sudo udevadm trigger

due to some changes between Debian Stretch and Buster.

Finally the install script is expecting Python , and the newest version of Raspbian that shipped with the Raspberry Pi 4 is a flavour of Debian Buster which comes with with Python So you’ll also have to modify line of the script, changing to ,

python setup.py develop --user

before you can run the installation script,

$ ./install.sh

which should now successfully complete.

Once the installation has completed, go ahead plug in the USB Accelerator using the short USB-C to USB-A cable that accompanied the USB stick in the box. If you’ve already plugged it in, you’ll need remove it and replug it, as the installation script adds some rules that allows software running on the Raspberry Pi to recognise that the Edge TPU hardware is present.

Installing TensorFlow

Installing TensorFlow on the Raspberry Pi used to be a difficult process, however towards the middle of last year everything became a lot easier.

Unfortunately the officially released wheel has some problems with Python In theory that means we’d either have build TensorFlow from source and all its dependencies, or downgrade back to Python Neither of which is a particularly pleasant thought since the state of the the Raspbian Buster package repository is still somewhat in flux during pre-release.

Fortunately Pete Warden came through with a candidate wheel for Python , and Ben Nuttall provided me wheels for all necessary dependencies. These will be made official soon, so it’s likely that by the time of release you should therefore be able to install TensorFlow using the official method,

$ sudo apt-get install libatlas-base-dev $ $ pip3 install tensorflow

but check this GitHub issue before proceeding to make sure that’s the case.

Installing AI2GO

The AI2GO platform installs and runs out of the box on the new Raspberry Pi 4, so you can just follow the instructions in the methodology section of my previous benchmarks using the Raspberry Pi 3 to configure and download your model bundle.

Once you’ve downloaded it, go ahead and install the model bundle,

$ cd ~/kitchen-object-detector-medium $ pip3 install xnornetcpabi3-linux_armv7l.whl Processing ./xnornetcpabi3-linux_armv7l.whl Installing collected packages: xnornet Successfully installed xnornet $

although be aware that if you’ve previously installed another model bundle, you need to ensure you’ve uninstalled it first before installing a new one.

Problems with TensorFlow Lite

While the official TensorFlow binary distribution does not include a build of TensorFlow Lite, there is an unofficial distribution which does. Unfortunately this wheel has not been updated to support Raspbian Buster and Python However it’s likely that situation will change after the new Rapsberry Pi has been officially released, at which point I’ll probably go back and take another look at TensorFlow Lite.

Problems with the Neural Compute Stick

The software to support the Neural Compute Stick is the OpenVINO toolkit, and right now there is no support for running the toolkit under Python which is what is shipped with Raspbian Buster. I unfortunately couldn’t perform benchmarking for the Movidus Neural Compute Stick, or the Intel Neural Compute Stick 2. Based on past performance it’s likely that updating the Raspberry Pi card image may take some time.

⚠️Warning You should not expect the Movidius Neural Compute Stick or the Intel Neural Compute Stick 2 to work with the Raspberry Pi 4 in the near term.
The Benchmarking Code

The code from our previous benchmarks was reused unchanged.

Benchmarking Edge Computing - All the resources needed to reproduce the benchmarking timing runs.

In Closing

Comparing these platforms on an even footing continues to be difficult. But it is clear that the new Raspberry Pi 4 is a solid platform for machine learning inferencing at the edge.

Links to Previous Benchmarks

If you’re interested in details of around the previous benchmarks.

Benchmarking Edge Computing - Comparing Google, Intel, and NVIDIA accelerator hardware

Benchmarking TensorFlow and TensorFlow Lite on the Raspberry Pi - I recently sat down to benchmark the new accelerator hardware that is now appearing on the market intended to speed up…

Benchmarking the Xnor AI2GO Platform on the Raspberry Pi - I recently sat down to benchmark the new accelerator hardware that is now appearing on the market intended to speed up…

Links to Getting Started Guides

If you’re interested in getting started with any of the accelerator hardware I used during my benchmarks, I’ve put together getting started guides for the Google, Intel, and NVIDIA hardware I looked at during the analysis.

Hands on with the Coral Dev Board - Getting started with Google’s new Edge TPU hardware

How to use a Raspberry Pi to flash new firmware onto the Coral Dev Board - Getting started with Google’s new Edge TPU hardware

Hands on with the Coral USB Accelerator - Getting started with Google’s new Edge TPU hardware

Getting Started with the Intel Neural Compute Stick 2 and the Raspberry Pi - Getting started with Intel’s Movidius hardware

Getting Started with the NVIDIA Jetson Nano Developer Kit - Getting started with NVIDIA’s GPU-based hardware

Sours: https://www.hackster.io/news/benchmarking-machine-learning-on-the-new-raspberry-pimodel-bdbce4

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Self Driving AI in 100 lines of code - Raspberry Pi

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