Image Classification
Assigning an entire image to one or more predefined categories using a machine learning model.
Image classification assigns images to predefined categories – the most fundamental computer vision task, powered by CNNs and Vision Transformers.
Explanation
Image classification is the most fundamental computer vision task. Modern approaches use CNNs or Vision Transformers, often pre-trained on ImageNet.
Marketing Relevance
Image classification powers product categorization, content moderation, medical diagnostics, and visual quality control.
Example
An e-commerce system automatically classifies uploaded product photos into categories like "shoes", "electronics", or "furniture".
Common Pitfalls
Class imbalance in training data. Domain shift between training and production. Overconfidence on out-of-distribution images.
Origin & History
The ImageNet Large Scale Visual Recognition Challenge (ILSVRC, 2010) drove progress. AlexNet (2012) dramatically reduced error with deep learning. ResNet (2015) surpassed human accuracy. ViT (2020) brought transformers to image classification.
Comparisons & Differences
Image Classification vs. Object Detection
Classification gives one label per image. Object detection localizes multiple objects with bounding boxes and labels.
Image Classification vs. Image Segmentation
Classification: one label per image. Segmentation: one label per pixel – much more fine-grained.