Technology

CIFAR-10

A foundational computer vision dataset featuring 60,000 labeled 32x32 color images across 10 distinct object classes.

Curated by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton, CIFAR-10 is a staple benchmark for training convolutional neural networks (CNNs). The collection includes 6,000 images per category: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. Researchers utilize the 50,000 training and 10,000 test samples to prototype architectures quickly due to the manageable 32x32 resolution. It remains a primary tool for evaluating image classification performance and algorithmic efficiency in deep learning.

https://www.cs.toronto.edu/~kriz/cifar.html

What builders pair with CIFAR-10

Projects using both technologies. Select a pairing to see a project.

Built with CIFAR-10

256KB RAM 1 shared project ARM Cortex-M0+ 1 shared project C 1 shared project CIA (Constructive Integer Attention) 1 shared project Custom neural network architecture — no frameworks 1 shared project Integer-only arithmetic 1 shared project

3 more pairings

no floating-point libraries 1 shared project Raspberry Pi Pico 1 shared project TinyLLM 1 shared project

Pairing: 256KB RAM

Photo from the event — Event photo

TinyEye: Image Classification on a $4 Microcontroller — Zero Floating-Point, Zero GPU

Tokyo · February 19, 2026

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TinyEye: $4 Microcontroller Integer AI

Raspberry Pi Pico TinyLLM