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GPU Accelerated Object Recognition on Raspberry Pi 3 & Raspberry Pi Zero

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You've probably already seen one or more object recognition demos, where a system equipped with a camera detects the type of object using deep learning algorithms either locally or in the cloud. It's for example used in autonomous cars to detect pedestrian, pets, other cars and so on. Kochi Nakamura and his team have developed software based on GoogleNet deep neural network with a a 1000-class image classification model running on Raspberry Pi Zero and Raspberry Pi 3 and leveraging the VideoCore IV GPU found in Broadcom BCM283x processor in order to detect objects faster than with the CPU, more exactly about 3 times faster than using the four Cortex A53 cores in RPi 3. They just connected a battery, a display, and the official Raspberry Pi camera to the Raspberry Pi boards to be able to recognize various objects and animals. Not yet, but he is thinking about it, and when/if it is released it will probably be found on his github account, where there is already py-videocore Python library for GPGPU on Raspberry Pi, which was very likely used in the demos above.


Start Here with Computer Vision, Deep Learning, and OpenCV - PyImageSearch

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You're interested in Computer Vision, Deep Learning, and OpenCV…but you don't know how to get started. Follow these steps to get OpenCV configured/installed on your system, learn the fundamentals of Computer Vision, and graduate to more advanced topics, including Deep Learning, Face Recognition, Object Detection, and more! Deep Learning algorithms are capable of obtaining unprecedented accuracy in Computer Vision tasks, including Image Classification, Object Detection, Segmentation, and more. Follow these steps and you'll have enough knowledge to start applying Deep Learning to your own projects. Using Computer Vision we can perform a variety of facial applications, including facial recognition, building a virtual makeover system (i.e., makeup, cosmetics, eyeglasses/sunglasses, etc.), or even aiding in law enforcement to help detect, recognize, and track criminals.


Google's Raspberry Pi-like Coral board lands: Turbo-charged AI on a tiny computer ZDNet

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Developers can now get their hands on Google's souped-up answer to the Raspberry Pi: the $150 Coral Dev Board, which features Google's Edge TPU machine-learning accelerator for low-powered devices that sit on the edge of a network. Google unveiled the tiny Edge TPU ASIC last July as its low-cost chip for bringing machine learning to sensors that can run machine-learning models on the TensorFlow lite framework. The Edge TPU now features in the Coral-branded $75 USB'thum bdrive' accelerator and as part of a removable'system on module' that ships with a developer baseboard. The Edge TPU Module includes an NXP i.MX 8M system on chip that consists of a quad-core Cortex-A53 and Cortex-M4F, a Vivante GC7000 Lite Graphics graphics processor, 8GB of eMMC storage, and 1GB of LDDR4 RAM. The baseboard has a RPi-like 40-pin GPIO expansion header, microSD slot for flash memory, USB ports, Gigabit Ethernet port, USB 2.0 and 3.0 ports for power and peripherals, a 3.5mm audio jack, and a terminal to wire up stereo speakers.


Google's Raspberry Pi-like Coral board lands: Turbo-charged AI on a tiny computer

ZDNet

Developers can now get their hands on Google's souped-up answer to the Raspberry Pi: the $150 Coral Dev Board, which features Google's Edge TPU machine-learning accelerator for low-powered devices that sit on the edge of a network. Google unveiled the tiny Edge TPU ASIC last July as its low-cost chip for bringing machine learning to sensors that can run machine-learning models on the TensorFlow lite framework. The Edge TPU now features in the Coral-branded $75 USB'thum bdrive' accelerator and as part of a removable'system on module' that ships with a developer baseboard. The Edge TPU Module includes an NXP i.MX 8M system on chip that consists of a quad-core Cortex-A53 and Cortex-M4F, a Vivante GC7000 Lite Graphics graphics processor, 8GB of eMMC storage, and 1GB of LDDR4 RAM. The baseboard has a RPi-like 40-pin GPIO expansion header, microSD slot for flash memory, USB ports, Gigabit Ethernet port, USB 2.0 and 3.0 ports for power and peripherals, a 3.5mm audio jack, and a terminal to wire up stereo speakers.