Visual classification asks a model to identify which category best describes visual input. The categories might represent objects, scenes, defects, styles, or other labels defined for the task.
Performance depends on the training data, category definitions, image quality, and how closely test images resemble the examples the system learned from. Weak results on one exercise provide evidence about that test, not automatic proof of performance on every visual task.
ELI5
Visual classification is like sorting pictures into labeled boxes. The AI system looks at a picture and chooses the box that seems to match it.
For example, it might place photographs into boxes labeled frog, bird, and fish. A hidden or blurry frog can make the choice much harder.
How is visual classification different from object detection?
Classification chooses a category for an image or region, while object detection also identifies where individual objects appear.
Why can visual classification fail on unusual images?
The object may be small, obscured, distorted, poorly lit, or unlike the examples represented in the training data.
