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A kit consisting of an M.2 HAT+ module and a Hailo-8L accelerator with a performance of 13 TOPS enables the rapid development of advanced real-time AI applications on the Raspberry Pi 5. This setup allows neural networks for object detection and segmentation to run on the Hailo-8L coprocessor, leaving the Raspberry Pi 5 CPU available for other tasks. The foundation has also prepared rpicam-apps libraries that significantly facilitate the creation of AI projects. Raspberry Pi SC1438

The Raspberry Pi AI Kit is an advanced solution for integrating artificial intelligence with the Raspberry Pi 5, created in collaboration with Hailo. The kit includes the Raspberry Pi M.2 HAT+ and the Hailo AI accelerator module, enabling efficient real-time neural network processing with minimal latency and low power consumption. The AI module achieves an impressive performance of 13 tera operations per second (TOPS) thanks to the Hailo-8L accelerator. Communication with the computer is via a PCIe 3.0 x1 interface, ensuring fast and efficient communication. Once installed on the Raspberry Pi 5, the module allows for the rapid development of complex computer vision applications. With full integration into the Raspberry Pi OS operating system, the AI module is automatically detected and ready to use, allowing users to immediately start working with AI.
Software is a key component of the Raspberry Pi AI Kit. With rpicam-apps, users can easily integrate neural network inference with the image processing pipeline. The pre-installed Hailo Tappas libraries enable the creation of advanced AI applications in just a few hundred lines of C++ code. The software installation process is simple and intuitive, with full instructions available in the beginner's guide.
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Minicomputer version 5 with 16GB RAM equipped with two microHDMI ports, 2 USB 3.0 ports and 2 USB 2.0 ports and a USB-C power connector. The processor is a 4-core Broadcom BCM2712 clocked at 2.4GHz. The board also has dual-band WiFi, Bluetooth 5.0 with BLE support, PCI Express 2.0 interface and a real-time clock. This model offers exceptional performance thanks to 16GB RAM, which makes it perfect for advanced applications
Neodymium rectangular magnet with dimensions of 12x4mm a and a height of 4 mm.
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Neodymium rectangular magnet with dimensions of 15x3mm a and a height of 2 mm.
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Neodymium rectangular magnet with dimensions of 15x4mm a and a height of 2 mm.
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Neodymium rectangular magnet with dimensions of 20x4mm a and a height of 2 mm.
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Neodymium rectangular magnet with dimensions of 20x5mm a and a height of 2 mm.
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Neodymium rectangular magnet with dimensions of 30x10mm a and a height of 2 mm.
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Neodymium rectangular magnet with dimensions of 3x3mm a and a height of 3 mm.
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Neodymium rectangular magnet with dimensions of 4x4mm a and a height of 4 mm.
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Neodymium rectangular magnet with dimensions of 5x5mm a and a height of 5 mm.
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Neodymium rectangular magnet with dimensions of 7x5mm a and a height of 2 mm.
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Module with the RP2040 microcontroller with two ARM Cortex-M0 + cores operating at 133 MHz, 256 kB RAM and 30 GPIO. The board also has 2 MB of external QSPI flash memory. Module can be programmed in C/C++ or microPython. Raspberry Pi Pico
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Development board with ESP32 module (WiFi + Bluetooth) in the set with the OV2640 camera. Equipped with a 40-pin connector compatible with Raspberry Pi HAT. Waveshare ESP32 One Kit
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AI accelerator with a USB connector based on the Intel Movidius Myriad X Vision Processing Unit 4GB. It can work with Ubuntu 16.04, Windows 10, Rasbian and others. in use with OpenVINO. Intel NCSM2485.DK
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Connector for connecting the 24-pin tape with 0.5 mm pitch to the printed circuit board
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Connector for connecting the 30-pin tape with 0.5 mm pitch to the printed circuit board
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Connector for connecting the 50-pin tape with 0.5 mm pitch to the printed circuit board
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A kit consisting of an M.2 HAT+ module and a Hailo-8L accelerator with a performance of 13 TOPS enables the rapid development of advanced real-time AI applications on the Raspberry Pi 5. This setup allows neural networks for object detection and segmentation to run on the Hailo-8L coprocessor, leaving the Raspberry Pi 5 CPU available for other tasks. The foundation has also prepared rpicam-apps libraries that significantly facilitate the creation of AI projects. Raspberry Pi SC1438