Zero-Shot Image Classification with OpenAI's CLIP
State-of-the-art (SotA) computer vision (CV) models are characterized by a *restricted* understanding of the visual world specific to their training data [1].

These models can perform *very well* on specific tasks and datasets, but they do not generalize well. They cannot handle new classes or images beyond the domain they have been trained with.

Ideally, a CV model should learn the contents of images without excessive focus on the specific labels it is initially trained to understand.

Fortunately, OpenAI's CLIP has proved itself as an incredibly flexible CV classification model that often requires *zero* retraining. In this chapter, we will explore CLIP in zero-shot image classification.

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