ConvNetJS CIFAR-10

CIFAR-10 is a benchmark dataset of 60,000 tiny 32×32 color photographs spread across 10 real-world categories — airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. Unlike MNIST's clean grayscale digits, CIFAR-10 contains cluttered backgrounds, varied lighting, and overlapping poses, demanding richer feature extraction. A Convolutional Neural Network (CNN) processes each image using shared filters that detect local patterns regardless of where they appear, stacking layers to build from edges to textures to object parts. In the ConvNetJS demo: watch the Test accuracy graph climb from ~10% (random) toward ~70%; click a training example thumbnail to see the network's class probability bars; try increasing the number of filters in layer 1 or adding a second conv layer to see how depth affects accuracy.