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clip

Map images and text into a shared embedding space for zero-shot classification and similarity scoring.

2|Updated Apr 12, 2026
Search Tags:#embeddings#multimodal#zero-shot#clip#vision-language#image-classification#image-search
One-click install
npx skills add https://github.com/Clay-HHK/claude-config --skill clip-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/AI-research-SKILLs/18-multimodal/clip
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill clip-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a zero-shot method to map images and text into a shared embedding space so you can classify, search, and match visual content without collecting or fine-tuning on labeled data.

Core Features & Use Cases

  • Zero-shot image classification: assign natural-language labels to images with no training required.
  • Image-text similarity & retrieval: compute cross-modal similarity for semantic search and caption matching.
  • Embeddings for vector databases: produce normalized image and text vectors for use with FAISS/Chroma.
  • Content moderation and batch processing: detect unsafe categories and efficiently process large image sets.
  • Use Case: build a semantic image search that returns the most relevant photos for a textual query using CLIP embeddings.

Quick Start

Load the ViT-B/32 model, preprocess your image, tokenize candidate labels, and compute zero-shot probabilities to obtain the top predicted labels.

Frequently Asked Questions about clip

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform zero-shot image classification without training data?▼

Zero-shot image classification assigns natural-language labels to images without training data by mapping images and text into a shared embedding space and computing cosine similarity between normalized vectors.

Can I use CLIP embeddings for semantic image search and retrieval?▼

Yes, CLIP produces normalized image and text vectors for cross-modal similarity scoring, enabling semantic image search that returns the most relevant photos for textual queries based on image-text similarity.

Do I need a PyTorch environment to compute image-text similarity scores?▼

Yes, computing image-text similarity scores requires a PyTorch-compatible environment, CLIP model weights, and image preprocessing to produce normalized embeddings for cosine similarity calculations.

What is the best way to generate embeddings for vector databases like FAISS or Chroma?▼

The best way to generate vector database embeddings is using CLIP to produce normalized image and text vectors, which can then be directly indexed in FAISS or Chroma for cross-modal retrieval.

How does zero-shot image understanding work for content moderation?▼

Zero-shot image understanding detects unsafe categories by mapping images and candidate text labels into a shared embedding space, computing similarity scores to identify restricted content without fine-tuning.

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