Image Classification with Neural Networks: A Beginner's Guide

Image classification is the task of assigning a label to an entire image. It's one of the foundational tasks in deep learning.

How Image Classification Works

1. Input: An image is represented as a grid of pixel values

2. Feature extraction: Convolutional layers detect patterns (edges, textures, shapes)

3. Classification: Fully connected layers map features to class probabilities

4. Output: The class with highest probability is the prediction

Modern classifiers use Vision Transformers instead of CNNs:

Split the image into patches (16×16 pixels)

Treat each patch as a "token" (like words in NLP)

Use self-attention to understand relationships between patches

Classify based on global understanding of the image

Top-5 accuracy on ImageNet exceeds 99%

Struggles with unusual angles, contexts, or rare objects

Confidence scores indicate reliability

Multiple valid labels may exist for one image

Classify any image with our Image Classifier using the ViT model running entirely in your browser.

Once you know what is in the image, use our Image Captioner to generate a natural-language description of the scene.

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