Build a Zero-shot Image Classifier in 15 Minutes or Less: A Beginner's Guide

Easily Build and Deploy an Image Classifier with Hugging Face and Streamlit!

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Introduction

Image classification is a crucial task in the field of computer vision, and zero-shot image classification adds an additional level of complexity by requiring the classifier to recognize and classify objects it has never seen before. For those unfamiliar with the term, zero-shot image classification refers to the ability of a machine learning model to classify images without needing to be trained on those images first based on the knowledge it has learned from other related tasks.

Zero-shot image classification is an important area of research in the field of computer vision, as it allows for the recognition and understanding of new classes of objects without the need for extensive training data. This beginner's guide will show you how to build a zero-shot image classifier in just 15 minutes or less using the Hugging Face library and the Streamlit app framework.

Hugging Face is a popular open-source library that provides a wide range of pre-trained models for tasks such as language translation, text generation, and question-answering. These models have been trained on large datasets and can be fine-tuned for specific tasks or used as is for a variety of NLP applications.

Streamlit is a user-friendly app framework that allows you to build and deploy interactive machine-learning applications in a few lines of Python code. By the end of this tutorial, you will have a working zero-shot classifier application that you can customize and improve upon.