Image classification works by feeding images into a machine learning model, which analyzes the features of the image (such as shapes, colors, textures) and compares them to previously seen examples. The model then predicts which category or label best describes the image.

Image classification assigns a single label to an entire image, determining what the overall content is. Object detection, on the other hand, identifies and locates multiple objects within an image, providing labels for specific parts of the image along with their coordinates.

Yes, in some cases, an image may contain multiple objects or concepts, requiring the model to assign multiple labels to a single image. Multi-label classification is a more complex task where an image can belong to more than one category at the same time (e.g., an image of a “dog” and “grass”).

The accuracy of image classification depends on several factors, including the quality of the training data, the complexity of the model, and the nature of the images being classified. Advanced models, particularly those using deep learning, can achieve very high accuracy rates, though challenging or ambiguous images may still pose difficulties.

Yes, with advances in hardware and optimized algorithms, real-time image classification is possible. This is commonly used in applications like facial recognition, object detection in autonomous vehicles, and live video analysis in security systems.