{"product_id":"kit-pi-5-knowledge-distillation-research-kit","title":"Pi 5 Knowledge Distillation Research Kit","description":"\u003ch1\u003eDistil ResNet50 to MobileNet on Raspberry Pi 5: Compare Model Size, Speed, and Accuracy\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 Knowledge Distillation \u0026amp; Edge AI Optimization\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eRun a full knowledge distillation pipeline on a Raspberry Pi 5 — train a MobileNet student to mimic a ResNet50 teacher, then benchmark both models on the edge. You’ll produce a detailed report comparing model size (MB), inference latency (ms), and classification accuracy, gaining hands-on insight into the real-world tradeoffs of AI model compression for embedded systems. This kit turns a research concept into a repeatable experiment right on your desk.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA complete knowledge distillation experiment: you’ll set up a PyTorch training environment on Pi 5, implement a distillation loss combining hard and soft targets, train the student model, and evaluate both teacher and student on a standard image classification dataset (CIFAR-10\/100). The NVMe SSD provides fast storage for dataset caching and model checkpoints, ensuring the Pi’s limited RAM isn’t a bottleneck. By the end, you’ll have a side-by-side comparison of a large convolutional network and its compact distilled version, along with a clear understanding of when the tradeoff makes engineering sense.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eImplement knowledge distillation from scratch in PyTorch on an ARM64 device, including temperature-scaled soft targets and loss weighting\u003c\/li\u003e\n  \u003cli\u003eBenchmark inference speed on Pi 5’s CPU and built-in VideoCore VI GPU using torchprof or custom timing loops\u003c\/li\u003e\n  \u003cli\u003eQuantify model memory footprint – RAM usage during inference and storage on the SSD – for both teacher and student\u003c\/li\u003e\n  \u003cli\u003eAnalyse accuracy loss versus compression ratio to inform deployment decisions for real-world edge AI systems\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\u003eNVMe SSD 512GB\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\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 designed for Indian engineering students and researchers tackling AI model optimisation for edge devices. B.Tech final-year students in CSE, ECE, or AI\/ML will find it ideal for capstone projects, while M.Tech scholars can use it to prototype and benchmark distillation algorithms. Smart India Hackathon participants working on “AI for resource-constrained devices” problem statements will gain a ready-to-run experimental setup. Even industry professionals exploring TinyML will appreciate the Pi 5’s ability to run real training and inference locally.\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\u003eDo I need to bring my own dataset or pre-trained models?\u003c\/summary\u003e\u003cp\u003eThe kit includes pre-loaded CIFAR-10\/100 datasets and instructions to download ResNet50 and MobileNetV2 weights. The SSD stores everything efficiently.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs a camera required for this project?\u003c\/summary\u003e\u003cp\u003eNo. The project works with image classification datasets; live camera feed is not needed. You can extend it later with a Pi Camera if you wish.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat level of PyTorch and Python knowledge is needed?\u003c\/summary\u003e\u003cp\u003eIntermediate – you should be comfortable writing Python classes, training loops, and using nn.Module. The AI companion provides code snippets and debugging tips to fill any gaps.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I adapt this kit to distill other model pairs, like ViT to EfficientNet?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The provided Jupyter notebook and AI guidance teach a general distillation framework. The Pi 5 can handle modest custom architectures so you can experiment freely.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eDistil a large ResNet50 teacher into a small MobileNet student on Pi 5 — compare size, speed and accuracy tradeoffs.\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\/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-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\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 Knowledge Distillation Research Kit?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Knowledge Distillation Research Kit includes all components needed: Raspberry Pi 5 8GB, NVMe SSD 512GB, Pi 5 M.2 HAT+, 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 Knowledge Distillation Research Kit?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. Distil a large ResNet50 teacher into a small MobileNet student on Pi 5 — compare size, speed and accuracy tradeoffs. Estimated build time is 8-10 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Knowledge Distillation Research Kit online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Knowledge Distillation Research Kit is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Pi 5 Knowledge Distillation Research Kit\",\n  \"description\": \"Distil a large ResNet50 teacher into a small MobileNet student on Pi 5 — compare size, speed and accuracy tradeoffs.\",\n  \"sku\": \"CDN-KIT-2560\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-pi-5-knowledge-distillation-research-kit\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"50550\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Edge AI \u0026 Computer Vision\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53469369467245,"sku":"CDN-KIT-2560","price":59650.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-pi-5-knowledge-distillation-research-kit.png?v=1781948409","url":"https:\/\/compoden.com\/products\/kit-pi-5-knowledge-distillation-research-kit","provider":"Compoden","version":"1.0","type":"link"}