Google has promised us new hardware products for machine learning at the edge, and now it’s finally out. The thing you’re going to take away from this is that Google built a Raspberry Pi with machine learning. This is Google’s Coral, with an Edge TPU platform, a custom-made ASIC that is designed to run machine learning algorithms ‘at the edge’. Here is the link to the board that looks like a Raspberry Pi.
This new hardware was launched ahead of the TensorFlow Dev Summit, revolving around machine learning and ‘AI’ in embedded applications, specifically power- and computationally-limited environments. This is ‘the edge’ in marketing speak, and already we’ve seen a few products designed from the ground up to run ML algorithms and inference in embedded applications. There are RISC-V microcontrollers with machine learning accelerators available now, and Nvidia has been working on this for years. Now Google is throwing their hat into the ring with a custom-designed ASIC that accelerates TensorFlow. It just so happens that the board looks like a Raspberry Pi.
Continue reading “Google Launches AI Platform That Looks Remarkably Like A Raspberry Pi”
TensorFlow is a popular machine learning package, that among other things, is particularly adept at image recognition. If you want to use a webcam to monitor cats on your lawn or alert you to visitors, TensorFlow can help you achieve this with a bunch of pre-baked libraries. [Eric] took a different tack with PrintRite – using TensorFlow to monitor his 3D printer and warn him of prints gone bad – or worse.
The project relies on training TensorFlow to recognize images of 3D prints gone bad. If layers are separated, or the nozzle is covered in melted goo, it’s probably a good idea to stop the print. Worst case, your printer could begin smoking or catch fire – in that case, [Eric] has the system configured to shut the printer off using a TP-Link Wi-Fi enabled power socket.
Currently, the project exists as a plugin for OctoPrint and relies on two Raspberry Pis – a Zero to handle the camera, and a 3B+to handle OctoPrint and the TensorFlow software. It’s in an early stage of development and is likely not quite ready to replace human supervision. Still, this is a project that holds a lot of promise, and we’re eager to see further development in this area.
There’s a lot of development happening to improve the reliability of 3D printers – we’ve even seen a trick device for resuming failed prints.
We salute hackers who make technology useful for people in emerging markets. Leigh Johnson joined that select group when she accepted the challenge to build portable machine vision units that work offline and can be deployed for under $100 each. For hardware, a Raspberry Pi with camera plus screen can fit under that cost ceiling, and the software to give it sight is the focus of her 2018 Hackaday Superconference presentation. (Video also embedded below.)
The talk is a very concise 13 minutes, so Leigh flies through definitions of basic terms, before quickly naming TensorFlow and Keras as the tools she used. The time she saved here was spent on explaining what convolutional neural networks are and how they work, just enough to prepare the audience. But all of that is really just background, the meat of the talk is self-contained examples that Leigh has put together and made available online. I love to see that since it means you go beyond just watching and try it out for yourself. Continue reading “Leigh Johnson’s Guide To Machine Vision On Raspberry Pi”
Machine learning has brought an old idea — neural networks — to bear on a range of previously difficult problems such as handwriting and speech recognition. Better software and hardware has made it feasible to apply sophisticated machine learning algorithms that would have previously been only possible on giant supercomputers. However, there’s still a learning curve for developing both models and software to use these trained models. Uber — you know, the guys that drive you home when you’ve had a bit too much — have what they are calling a “code-free deep learning toolbox” named Ludwig. The promise is you can create, train, and use models to extract features from data without writing any code. You can find the project itself on GitHub.io.
The toolbox is built over TensorFlow and they claim:
Ludwig is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures rather than data wrangling.
Continue reading “Ludwig Promises Easy Machine Learning from Uber”
When [314Reactor] got a robot car kit, he knew he wanted to add some extra things to it. At about the same time he was watching a Star Trek episode that featured exocomps — robots that worked in dangerous areas. He decided to use those fictional devices to inspire his modifications to the car kit. Granted, the fictional robots were intelligent and had a replicator. So you know he won’t make an actual working replica. But then again, the ones on the TV show didn’t have all that either.
A Raspberry Pi runs Tensorflow using the standard camera. This lets it identify objects of interest (assuming it gets them right) and sends the image back to the operator along with some identifying information. The kit already had an Arduino onboard and the new robot talks to it via a serial port. You can see a video about the project, below.
Continue reading “A Star-Trek-Inspired Robot With Raspberry Pi and AI”
Neural networks are computer systems that are vaguely inspired by the construction of animal brains, and much like human brains, can be trained to obey the whims of the almighty domestic cat. [EdjeElectronics] has built just such a system, and his cat is better off for it.
The build uses a Raspberry Pi, fitted with the Pi Camera board, to image the area around the back door of the house. A Python script regularly captures images and passes them to a TensorFlow neural network for object recognition. The TensorFlow network returns object type and positions to the Python script. This information can be used to determine if there is a cat in the frame, and if it is inside or outside. If the cat remains in position for ten consecutive frames, a text message is sent via Twilio, indicating to the owner to let the cat in or out, as the case may be.
Thirty years ago, object classification was a pie-in-the-sky technology, but now you can run it on a $30 computer to figure out where your pets are. What a time we live in! A similar solution to this problem may be a cat door that unlocks via facial recognition. Video after the break.
[Thanks to Baldpower for the tip!]
Continue reading “Neural Network Knows When Cat Wants To Go Outside”
Getting computers to recognize objects has been a historically difficult problem in computer science, but with the rise of machine learning it is becoming easier to solve. One of the tools that can be put to work in object recognition is an open source library called TensorFlow, which [Evan] aka [Edje Electronics] has put to work for exactly this purpose.
His object recognition software runs on a Raspberry Pi equipped with a webcam, and also makes use of Open CV. [Evan] notes that this opens up a lot of creative low-cost detection applications for the Pi, such as setting up a camera that detects when a pet is waiting at the door to be let inside or outside, counting the number of bees entering and exiting a beehive, or monitoring parking spaces at an office.
This project uses a number of other toolkits as well, including Protobuf. It also makes extensive use of Python scripts, but if you’re comfortable with that and you have an application for computer vision, [Evan]’s tutorial will get you started.
Continue reading “Object Detection, With TensorFlow”