hvass-labs/knifey-spoony

The Knifey-Spoony repository is a collection of photos of cutlery, knives, spoons, and forks, designed to help people practice building image recognition systems. Rather than being a tool or application, it's a dataset: a bundle of labeled pictures that a computer can learn from. The name and the classes ("knifey," "spoony," and "forky") are a nod to a joke from The Simpsons and the movie Crocodile Dundee. The dataset contains 4,700 color images, each 200 by 200 pixels. Most of them form a training set, the examples a model studies to learn what each utensil looks like. A smaller portion, 530 images, makes up a test set. Those test images use different backgrounds than the training images, which helps check whether a model can actually recognize the objects rather than just memorizing specific backgrounds. The split is uneven: spoony has the most images at 2,208 total, forky has 1,145, and knifey has 1,347. Someone learning machine learning or teaching a course would use this dataset. It's specifically built for a TensorFlow tutorial on classifying image data, so a student working through that tutorial would need these images. A beginner who wants to train their first image classifier could also pick it up as a practice set, it's simple enough to experiment with but large enough to be meaningful. One practical detail: the project includes a script for converting short video recordings into thousands of images. That means if you wanted to build a similar dataset of your own, say, photos of different types of fruit on your kitchen table, you could record a few videos, run them through the converter, and get a labeled training set without manually photographing each example. The dataset itself is in the public domain, free for both academic and commercial use.