{"product_id":"tensorflow-lite-edge-ml-kit-for-pi-5-and-esp32-s3","title":"TensorFlow Lite Edge ML Kit for Pi 5 \u0026 ESP32-S3","description":"\u003ch1\u003eCompare Edge ML Performance: TensorFlow Lite on Pi 5 vs ESP32-S3 Kit\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 Edge model benchmarking and deployment\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eYou'll deploy identical image classification models onto a Raspberry Pi 5 and an ESP32-S3, then measure and compare inference time, accuracy, and power consumption - mimicking the workflow of real-world embedded ML engineers evaluating hardware for on-device AI. This kit turns a messy multi-vendor sourcing task into a streamlined research-grade comparison tool.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA dual-platform edge inference testbench that captures images with two cameras, runs optimized TensorFlow Lite models, logs performance metrics, and generates comparison charts. You'll produce a data-driven report identifying the best processor for low-latency, low-power image classification.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eOptimise models for Arm Cortex-A76 and Xtensa LX7 targets\u003c\/li\u003e\n  \u003cli\u003eMeasure inference time in Python and C++\u003c\/li\u003e\n  \u003cli\u003eProfile power draw and correlate with model complexity\u003c\/li\u003e\n  \u003cli\u003eAnalyse accuracy trade-offs between int8 and float32 quantized models\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\u003eESP32-S3 Dev Board\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eOV2640 Camera\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\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\u003ctd class=\"kit-description\"\u003e\n  \u003cp\u003eTensorFlow Lite models deployed on Pi 5 and ESP32-S3 - compare inference time, accuracy and power for image classification.\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\/arduino-uno-r4-wifi-board-with-esp32-s3-module-ra4m1-cortex-m4\"\u003eESP32-S3 Dev Board\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/esp32-cam-board-ov2640-ov3660-wifi-bluetooth-module\"\u003eOV2640 Camera\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\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    \u003cli\u003e\u003ca href=\"\/products\/microusb-cable-1m-charging-data-cord-for-arduino-android\"\u003eMicroUSB Cable\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\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 Research Lab Kit 4 Machine Learning on Edge?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Research Lab Kit 4 Machine Learning on Edge includes all components needed: Raspberry Pi 5 8GB, ESP32-S3 Dev Board, OV2640 Camera, Pi Camera Module 3, NVMe SSD 256GB 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 Research Lab Kit 4 Machine Learning on Edge?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. TensorFlow Lite models deployed on Pi 5 and ESP32-S3 - compare inference time, accuracy and power for image classification. Estimated build time is 8-10 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Research Lab Kit 4 Machine Learning on Edge online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Research Lab Kit 4 Machine Learning on Edge 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\": \"Research Lab Kit 4 Machine Learning on Edge\",\n  \"description\": \"TensorFlow Lite models deployed on Pi 5 and ESP32-S3 - compare inference time, accuracy and power for image classification.\",\n  \"sku\": \"CDN-KIT-2737\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-research-lab-kit-4--machine-learning-on-edge\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"41750\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Lab Classroom Kits\"\n}\n\u003c\/script\u003e\n\u003c\/td\u003e\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53469382836589,"sku":"CDN-KIT-2737","price":49260.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/tensorflow-lite-edge-ml-kit-for-pi-5-and-esp32-s3.png?v=1782286681","url":"https:\/\/compoden.com\/products\/tensorflow-lite-edge-ml-kit-for-pi-5-and-esp32-s3","provider":"Compoden","version":"1.0","type":"link"}