Image classification

[aminaas1573]

Aa screenshot of the search tab showing a search for “landscape photograph” in a text box and a grid of images resulting from the search. This includes two images containing trees and images containing the sky and clouds.

While the search implementation isn’t perfect, it does give us an additional entry point into an extensive collection of data which is difficult to explore manually. It is possible to extend this interface to accommodate an image similarity feature. This could be useful for identifying a particular artist’s work in a broader collection.

While image search helps us find images, it doesn’t help us as much if we want to describe all the images in our collection. For this, we’ll need a slightly different type of machine learning task – image phone number database classification. An image classification model will put our images into categories drawn from a list of possible labels.

We can find image classification models on the Hugging Face Hub. The “Image Classification Model Tester” tab in the demo Gradio application allows us to test most of the 3,000+ image classification models hosted on the Hub against our dataset.

This can give us a sense of a few different things:

How well do the labels for a model match our data?A model for classifying dog breeds probably won’t help us much!
It gives us a quick way of inspecting possible errors a model might make with our data.
It prompts us to think about what categories might make sense for our images.
A screenshot of the image classification tab in the Gradio app which shows a bar chart with the most frequently predicted labels for images assigned by a computer vision model.