The CLIP (Contrastive Language-Image Pre-training) model, developed by OpenAI, is a groundbreaking multimodal model that combines knowledge of English-language concepts with semantic knowledge of images. It consists of a text and an image encoder, which encodes textual and visual information into a multimodal embedding space. The model's architecture aims to increase the cosine similarity score of images and associated text pairs. This is achieved through a contrastive objective, which enhances the efficiency of the model by 4x times. The CLIP model's forward pass involves running the input through the text and image encoder network, normalizing the embedded features, and using them as input to compute the cosine similarity. The resulting cosine similarity is then returned as logits. CLIP's versatility is evident in its ability to perform tasks such as zero-shot image classification, image generation, abstract task execution for robots, and image captioning. It has also bee