{"product_id":"kit-esp32-s3-tinyml-image-classifier","title":"ESP32-S3 TinyML Image Classifier","description":"\u003ch1\u003eESP32-S3 TinyML Image Classifier: Deploy MobileNet on a 240MHz Microcontroller\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 Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 4-5 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 16-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Edge AI \/ TinyML model deployment\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003ePoint a camera at a banana, an apple, or a person, and watch the ESP32-S3 identify it instantly — no cloud, no Wi-Fi. This kit puts a full image classifier in your hands, running TensorFlow Lite Micro MobileNet on a 240MHz dual-core chip. Whether you're exploring the CBSE AI curriculum or building a hackathon prototype, you'll learn to shrink and deploy a real neural network onto embedded hardware.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA standalone image classification camera that captures frames with the OV2640 sensor, runs a quantized MobileNet model, and shows the predicted class and confidence on a 0.96-inch OLED. The entire pipeline runs on the ESP32-S3 — no laptop required after upload. Use it for automated sorting, gesture recognition, or a portable vision system for your next Smart India Hackathon entry.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eConvert a TensorFlow MobileNet model to TensorFlow Lite and quantize it for an 8-bit microcontroller\u003c\/li\u003e\n  \u003cli\u003eConfigure the ESP32-S3's PSRAM and camera driver to read OV2640 frames in RGB565\u003c\/li\u003e\n  \u003cli\u003eIntegrate the TensorFlow Lite Micro interpreter and run inference at ~2 frames per second\u003c\/li\u003e\n  \u003cli\u003eOutput predictions to an I2C OLED and use serial debugging to profile model latency\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\u003eESP32-S3 Dev Board\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eOV2640 Camera Module\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e0.96in OLED\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMicroUSB Cable\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e15\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 CBSE Class 11-12 students taking the AI elective who need a working TinyML project for their practical file. B.Tech ECE\/EEE students can use it for final-year mini projects, while ATL Tinkering Lab mentors get a ready-to-assemble computer vision kit. It fits hackathon teams from IIT, NIT, VIT, and BITS tackling Smart India Hackathon problem statements on edge AI.\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 on the box to talk to the AI companion, which has been trained on this exact kit. It visualises wiring, explains code block by block, and if needed, a human engineer jumps in over WhatsApp — usually within an hour.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior AI\/ML knowledge to use this kit?\u003c\/summary\u003e\u003cp\u003eSome familiarity with C\/C++ and Arduino IDE is enough. The AI companion walks you through model conversion and deployment steps even if you’re new to TensorFlow Lite. If you've trained a model before, you can swap in your own MobileNet variant.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I train my own custom model for this hardware?\u003c\/summary\u003e\u003cp\u003eYes. The ESP32-S3 can run any quantized TensorFlow Lite Micro model that fits within PSRAM. You can train a new image model on Teachable Machine or TensorFlow and then follow our companion’s conversion guide to deploy it.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow do I power the setup away from a laptop?\u003c\/summary\u003e\u003cp\u003eThe ESP32-S3 board can run from any standard USB power bank using the microUSB cable. The camera and OLED draw minimal current, so a 5V\/1A supply is more than sufficient for portable demos.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eTensorFlow Lite Micro runs MobileNet on ESP32-S3 with OV2640 camera — on-device image classification with 240MHz MCU.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\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 Module\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/096in-oled-display-128x64-i2cspi-for-arduino-raspberry-pi\"\u003e0.96in OLED\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    \u003cli\u003eM-M Wires x15\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 ESP32-S3 TinyML Image Classifier?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The ESP32-S3 TinyML Image Classifier includes all components needed: ESP32-S3 Dev Board, OV2640 Camera Module, 0.96in OLED, MicroUSB Cable, M-M Wires 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 ESP32-S3 TinyML Image Classifier?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 16-21. TensorFlow Lite Micro runs MobileNet on ESP32-S3 with OV2640 camera — on-device image classification with 240MHz MCU. Estimated build time is 4-5 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the ESP32-S3 TinyML Image Classifier online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the ESP32-S3 TinyML Image Classifier is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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