What problem does it solve? Classifying images or searching image collections normally requires labeled training data and model fine-tuning. CLIP removes that requirement by matching images to natural language descriptions directly, enabling classification, search, and moderation without any training. ## Core Features & Use Cases - Zero-Shot Image Classification: Classify images into arbitrary text-defined categories without training data, using models like ViT-B/32. - Semantic Image Search: Encode images and text queries into a shared embedding space for cross-modal retrieval, integrable with vector databases like Chroma or FAISS. - Content Moderation: Score images against safety categories such as NSFW or violent content with confidence thresholds. - Use Case: Index a folder of product photos with CLIP embeddings, then retrieve the most relevant images for the query "a sunset over the ocean" ranked by cosine similarity. ## Quick Start Install CLIP with pip and ask the agent to classify an image against a list of text labels using the ViT-B/32 model.
