clip

Zero-shot image classification maps images and text into a shared embedding space using pretrained CLIP variants. You encode an image and text prompts, normalize embeddings, and rank results by cosine similarity without fine-tuning.

Cross-modal retrieval with CLIP encodes images and text queries into a shared space to compute cosine similarity. This ranks images by relevance to natural language descriptions for image search and media discovery.

Image-text similarity for content moderation loads pretrained CLIP variants to encode visual content and safety descriptions. Cosine similarity with configurable thresholds screens media against text prompts for safety.

CLIP processes video frames by encoding static images extracted from the video. It aligns these frames with natural language descriptions to enable multimodal search and cross-modal retrieval across visual content.

Load a pretrained CLIP model, encode your image and text prompts, normalize the embeddings, and compute cosine similarity. Configurable thresholds filter results to meet specific classification confidence requirements.

CLIP aligns visual content with text for multimodal QA but lacks deep reasoning. It computes cross-modal similarity using pretrained embeddings, which may not capture complex logical relationships between images and questions.