{"product_id":"kit-pi-5-ai-hat-custom-model-deployment","title":"Pi 5 AI HAT Custom Model Deployment","description":"\u003ch1\u003ePi 5 AI HAT+ Custom Model Deployment: From PyTorch to Hailo HEF\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 8-10 hours\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Deploying custom YOLO models on Hailo-8L\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eTake a PyTorch YOLO model—perhaps trained on a dataset of Indian traffic signs or industrial defects—and turn it into an optimized Hailo HEF binary running at 30 FPS on real-time video. This kit guides you through the full compiler toolchain: model parsing, calibration, quantisation, and deployment, all on the powerful Raspberry Pi 5 with AI HAT+.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA fully functional edge AI camera running your own YOLO object detection model on the Hailo-8L neural processor. You'll capture live video from the Pi Camera Module 3, preprocess it, run inference at high speed, and visualize bounding boxes—exactly as you’d prototype a production system.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eConvert a PyTorch YOLO model to ONNX and compile it for Hailo-8L using the Hailo Model Zoo toolchain.\u003c\/li\u003e\n  \u003cli\u003eQuantise and calibrate the model with a custom dataset to maintain accuracy within 2% of the full-precision version.\u003c\/li\u003e\n  \u003cli\u003eDeploy the compiled HEF file on the Raspberry Pi AI HAT+ and run inference at up to 30 FPS.\u003c\/li\u003e\n  \u003cli\u003eBuild a complete real-time object detection pipeline using the Pi Camera Module 3 and display results with bounding boxes.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 8GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi AI HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi Camera Module 3\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 512GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eThis advanced kit is built for B.Tech (ECE\/EEE\/CSE) students working on final-year edge AI projects, participants in Smart India Hackathon building real-time vision solutions, and researchers at IITs, NITs, VIT, BITS who need a portable, high-speed custom model deployment platform. If you have trained a YOLO model and now want to run it on-device without cloud dependency, this kit is your direct path.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eOur AI build companion, trained on this exact project, is available 24\/7 via QR code and WhatsApp; you can share error logs, and it will suggest precise corrections.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need to train my own YOLO model beforehand?\u003c\/summary\u003e\u003cp\u003eYou can use a pre-trained model from the Hailo Model Zoo to test the pipeline, but the true value of this kit is in compiling a custom model. We recommend having a PyTorch YOLO model ready (trained on your dataset) before starting.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat operating system and software versions are supported?\u003c\/summary\u003e\u003cp\u003eThe kit works with Raspberry Pi OS (Bookworm, 64-bit), HailoRT 4.17.0, and the Hailo Dataflow Compiler 3.27.0. A pre-configured SD card image is not included; you’ll flash the OS onto the NVMe SSD for faster I\/O.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use this kit for models other than YOLO?\u003c\/summary\u003e\u003cp\u003eYes, the Hailo-8L supports many architectures like ResNet, MobileNet, and SSD. However, the AI companion is optimised for the YOLO compilation workflow; adapting to other models will require additional documentation.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eCompile and quantise a custom YOLO model for Hailo-8L runtime on Pi AI HAT+ — from PyTorch to Hailo HEF format.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-8gb-high-performance-single-board-computer\"\u003eRaspberry Pi 5 8GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/industrial-ph-sensor-module-for-arduino-esp32-raspberry-pi\"\u003eRaspberry Pi AI HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/4-channel-relay-board-for-esp32-30-pin-5v-control\"\u003ePi Camera Module 3\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 512GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 AI HAT Custom Model Deployment?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 AI HAT Custom Model Deployment includes all components needed: Raspberry Pi 5 8GB, Raspberry Pi AI HAT+, Pi Camera Module 3, NVMe SSD 512GB, USB-C PSU and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 AI HAT Custom Model Deployment?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. Compile and quantise a custom YOLO model for Hailo-8L runtime on Pi AI HAT+ — from PyTorch to Hailo HEF format. Estimated build time is 8-10 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 AI HAT Custom Model Deployment online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 AI HAT Custom Model Deployment is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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