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Getting to Know CLIP: the Ultimate Image Matcher
An OpenAI model that connects images and words.
Do you have thousands of photos on your phone or computer, making it difficult to find the one you’re looking for?
Have you ever wished you could just type in “cat” and — boom — all your cute cat photos appear at the top?
Well, that’s kind of what CLIP does! CLIP is like a translator, but instead of working between languages, it translates between pictures and words. Let’s dive into what CLIP is, why it’s revolutionary, and what you can do with it.
What is CLIP?
CLIP stands for Contrastive Language–Image Pretraining. It’s a deep learning model that looks at images and text and then “matches” them to each other based on meaning.
CLIP’s main training approach is contrastive learning, which teaches the model to distinguish between related and unrelated pairs of images and text. During training, CLIP sees a large dataset of image-caption pairs from the internet. It learns to bring embeddings (representations) of matching pairs (image-text pairs that belong together) closer together in its feature space while pushing apart non-matching pairs.
For instance, if you show CLIP a picture of a cat and give it the word “cat,” it can identify that the two are related.
And here’s where it gets really cool: you don’t have to train CLIP for each specific task. Instead, you just give it new prompts, and it can make connections on its own. So, it’s great for “zero-shot” tasks, meaning it can perform tasks it wasn’t specifically trained for.
A Little History
CLIP was introduced by OpenAI in early 2021 as part of their efforts to make AI more versatile and intuitive. It was trained on a massive amount of data, mostly images and text pairs scraped from the internet. By showing it billions of these pairs, CLIP learned a general understanding of images and text in context.
That means it can look at a new image and match it with words based on what it “thinks” the image represents, even if it hasn’t seen that particular image before.
