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clip

Classify images and match image-text pairs using OpenAI's CLIP model.

2|Updated May 22, 2026
Search Tags:#semantic search#text analysis#CLIP#image classification#image-text matching
One-click install
npx skills add https://github.com/519lab/thoth-agent --skill clip-519lab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/519lab/thoth-agent/tree/main/optional-skills/mlops/clip
Command: npx skills add https://github.com/519lab/thoth-agent --skill clip-519lab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, pillow, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides AI-powered image and text analysis, allowing you to perform zero-shot image classification, image-text matching, and cross-modal retrieval using OpenAI's CLIP model.

Core Features & Use Cases

  • Zero-Shot Image Classification: Classify images without the need for training data.
  • Image-Text Matching: Match images with corresponding text and vice versa.
  • Semantic Image Search: Perform searches for images using text queries.
  • Content Moderation: Detect unsafe or inappropriate content in images.
  • Use Case: Quickly identify the content of an image by querying it with a description, or use it to moderate content for social media platforms.

Quick Start

Install CLIP with pip and use it to classify an image with the command 'thoth clip classify "path/to/image.jpg" "a cat"'

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 categorizes images without training data by matching image features against text descriptions. The CLIP model evaluates image-text pairs to predict the most accurate label automatically.

What is the best way to do semantic image search using text queries?▼

Semantic image search uses text queries to retrieve matching images by calculating cross-modal similarity scores. CLIP aligns visual and textual representations, returning images that best match your descriptive search input.

Can I match an image with descriptive text without fine-tuning a model?▼

Yes, image-text matching works without fine-tuning by leveraging pre-trained CLIP models. It aligns visual and textual data to determine semantic similarity, ideal for cross-modal retrieval and content matching.

Does zero-shot image classification work with Torch and Pillow for content moderation?▼

Yes, zero-shot content moderation works with PyTorch and Pillow. CLIP uses these dependencies to process images and detect unsafe or inappropriate content by querying images against safety-related text descriptions.

Do I need the transformers library to run image-text matching?▼

Yes, you need the transformers library along with PyTorch and Pillow. These dependencies provide the underlying model loading and tensor processing required to execute CLIP's image-text matching capabilities.

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