Computer vision models are AI technologies that process images or videos to predict or return pre-learned concepts or labels, such as image recognition, visual recognition, and facial recognition. They can be trained to recognize a wide range of concepts, from general items using pre-trained models to niche concepts through custom training with specific data. Custom models are built on pre-trained base models, providing a foundation similar to language acquisition. The process of creating a custom model involves selecting a base model, uploading images or videos, labeling them, and then training the model, which can be facilitated through accessible APIs. The effectiveness of these models relies on providing both positive and negative examples to ensure accurate concept recognition, similar to how a child learns to distinguish objects.