🔗 CLIP Model Explorer
Connecting vision and language through contrastive learning
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0 / 5 completedWhat is CLIP?
Contrastive Language-Image Pre-training
CLIP is a groundbreaking model from OpenAI that learns visual concepts from natural language descriptions. Unlike traditional computer vision models trained on fixed labels, CLIP learns by matching images with their text captions from the internet.
Trained on 400 million image-text pairs, CLIP can classify images into categories it has never seen before, simply by comparing them with text descriptions—a capability called zero-shot learning.
Zero-Shot Transfer
Classify images into new categories without any additional training or examples.
Multimodal Learning
Connects vision and language in a shared embedding space for semantic understanding.
Contrastive Learning
Learns by maximizing similarity between matching pairs and minimizing it for non-matching pairs.
Web-Scale Training
Leverages massive datasets of naturally occurring image-text pairs from the internet.
Why CLIP Matters
CLIP bridges the gap between computer vision and NLP, enabling models to understand images through human language. This breakthrough powers applications like DALL-E, image search, content moderation, and visual question answering.