Image Classification
A computer vision task that assigns a category label to an entire image. The model determines what the main subject of the image is from a predefined set of categories.
Why It Matters
Image classification is the foundational computer vision task. It powers photo organization, medical screening, quality inspection, and content moderation.
Example
A model looking at a photo and outputting: 'Golden Retriever (92% confidence)' — classifying the entire image into one of 1,000 possible categories.
Think of it like...
Like a librarian sorting books by genre — they look at each book (image) and assign it to the right shelf (category) based on its content.
Related Terms
Computer Vision
A field of AI that trains computers to interpret and understand visual information from the world — images, videos, and real-time camera feeds. It enables machines to 'see' and make decisions based on what they see.
Convolutional Neural Network
A type of neural network specifically designed for processing grid-like data such as images. CNNs use convolutional layers that apply filters to detect patterns like edges, textures, and shapes at different scales.
Object Detection
A computer vision task that identifies and locates specific objects within an image or video, providing both the object class and its position (usually as a bounding box).
Transfer Learning
A technique where a model trained on one task is repurposed as the starting point for a model on a different but related task. Instead of training from scratch, you leverage knowledge the model has already acquired.
Softmax
A function that converts a vector of numbers into a probability distribution, where each value is between 0 and 1 and all values sum to 1. It is typically used as the final layer in classification models.