{"product_id":"kit-pi-5-real-time-semantic-segmentation","title":"Pi 5 Real Time Semantic Segmentation","description":"\u003ch1\u003eReal-Time Semantic Segmentation with DeepLabV3 on Raspberry Pi 5: Segment Road, Sky, Building, and Person\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 hrs\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 real-time deep learning models on edge hardware\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eDeploy a pre-trained DeepLabV3 MobileNet model on the Raspberry Pi 5 to perform pixel‑level scene understanding at 10 frames per second. This kit turns a credit‑card‑sized computer into a portable perception engine that classifies road, sky, building, and person in every camera frame — ideal for autonomous robotics, smart surveillance dashboards, and edge AI research projects that demand low‑latency, on‑device inference.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou’ll assemble a compact AI camera that sees and labels every pixel in real time. Roads, skies, buildings, and people are segmented live, with a colour‑coded overlay rendered on the 7‑inch HDMI display. The NVMe SSD captures high‑speed inference logs for post‑analysis, letting you verify model accuracy and latency. This system serves as a ready‑to‑plug perception module for a DIY autonomous vehicle or an intelligent monitoring station.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eDeploy a pre‑trained DeepLabV3 model on Raspberry Pi 5 using ONNX runtime\u003c\/li\u003e\n  \u003cli\u003eOptimize inference with a quantized MobileNet backbone for consistent 10fps performance\u003c\/li\u003e\n  \u003cli\u003eInterface the Pi Camera Module 3 for low‑latency video capture and preprocessing\u003c\/li\u003e\n  \u003cli\u003eBuild a modular Python pipeline for post‑processing segmentation masks and live overlay\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\u003ePi Camera Module 3\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 256GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e7in HDMI Display\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eHDMI Cable\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\u003eIdeal for B.Tech ECE\/EEE final‑year students and AI enthusiasts working on edge computer vision capstone projects. Smart India Hackathon participants will find a ready‑to‑deploy perception subsystem that segments critical scene classes out of the box. If you’re an IIT, NIT, VIT, or BITS Pilani student aiming to publish or demonstrate real‑time deep learning on embedded hardware, this kit gives you a 10fps semantic segmentation prototype in one box.\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\u003eScan the QR code to start a chat with the AI companion trained on this project; if you need human help, WhatsApp support is included.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I modify the model to detect other object classes?\u003c\/summary\u003e\u003cp\u003eYes, the AI companion includes guidance on retraining DeepLabV3 with custom datasets using PyTorch and deploying the updated model via ONNX. WhatsApp support can help troubleshoot the conversion.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes this kit work outdoors for autonomous vehicle projects?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The Pi Camera Module 3 and 7‑inch display are portable, and the entire system can run on a power bank. We provide tips for weatherproofing and mounting on a bot chassis via the AI companion.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat frame rate can I expect with a different camera module?\u003c\/summary\u003e\u003cp\u003eThe AI companion explains performance benchmarks with various Pi cameras. The included Module 3 is optimized for 10fps, but you can adapt CSI‑connected cameras for higher frame rates with minor pipeline adjustments.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eDeepLabV3 MobileNet on Pi 5 segments road, sky, building and person classes at 10fps from camera — full scene parsing.\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\/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 256GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e7in HDMI Display\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/elecrow-hdmi-to-hdmi-connector-for-5-inch-raspberry-pi-display\"\u003eHDMI Cable\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 Real Time Semantic Segmentation?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Real Time Semantic Segmentation includes all components needed: Raspberry Pi 5 8GB, Pi Camera Module 3, NVMe SSD 256GB, Pi 5 M.2 HAT+, 7in HDMI Display 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 Real Time Semantic Segmentation?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. DeepLabV3 MobileNet on Pi 5 segments road, sky, building and person classes at 10fps from camera — full scene parsing. Estimated build time is 8-10 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Real Time Semantic Segmentation online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Real Time Semantic Segmentation is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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