clip

Zero-shot image classification with CLIP assigns image categories using only natural-language labels by projecting images and text into a shared embedding space and ranking them by cosine similarity. No fine-tuning or labeled training data is required.

Yes, you can match product photos to text descriptions by generating normalized embeddings for both images and candidate prompts, then ranking the images by semantic relevance using cosine similarity or softmax scores from image-text logits.

Cross-modal retrieval retrieves relevant images for a text query or relevant text for an image by embedding both into a shared space. It compares normalized image and text embeddings using cosine similarity to find the best semantic matches.

Yes, zero-shot image-text matching requires loading a pretrained CLIP model to generate shared embeddings. The model preprocesses images and tokenizes prompts to produce normalized vectors for ranking candidates by semantic similarity.

CLIP generates normalized embeddings for images and text that can be stored in ChromaDB for semantic image search. This enables cross-modal retrieval workflows where users query image collections using natural-language text prompts.