Vision Models.

A vision model operates on visual input, but that description covers several different jobs. Classification assigns categories, detection locates objects, segmentation identifies regions, and a multimodal language model may explain or compare what an image shows. Choosing among them starts with the output you need. A readable description is not a substitute for precise geometry, and an object label does not necessarily answer a contextual question. Define success in the language of the application before comparing model names or demonstrations.

The guides here treat model selection as a match between evidence, task, and operating constraints. Examine image preparation, response validation, uncertainty, latency, and the effort required to review a result. The vision and AI frame overview connects these decisions. Build a small evaluation set that includes typical images and likely failure cases. Keep the submitted assets and expected outcomes available so an appealing answer can be checked against what the model actually received.