clip

Classify images and match them to text using OpenAI's CLIP model.

Updated May 8, 2026

Search Tags:#OpenAI #image search #zero-shot #vision-language #image classification

Ready to run

npx skills add https://github.com/gztcode/my-skills --skill clip-gztcode

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/gztcode/my-skills/tree/main/clip
Command: npx skills add https://github.com/gztcode/my-skills --skill clip-gztcode

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?

CLIP solves the problem of understanding images through natural language, enabling tasks like image classification, image-text matching, and semantic image search without fine-tuning.

Core Features & Use Cases

Zero-shot Image Classification: Classify images into categories without training data.
Image-Text Matching: Match images to text descriptions.
Semantic Image Search: Find images similar to a given text description.
Content Moderation: Detect NSFW, violent, or graphic content in images.
Use Case: Use CLIP to automatically categorize images in a content moderation pipeline.

Quick Start

Use the CLIP skill to classify the image 'example.jpg' into the categories 'dog', 'cat', 'bird', 'car'.

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 providing training data?▼

Zero-shot image classification uses OpenAI's CLIP model to categorize images into custom text labels without training data. You simply provide the image and candidate category names, and the model computes similarities between the image and text descriptions.

Can I use CLIP for semantic image search using natural language descriptions?▼

Yes, CLIP enables semantic image search by matching a given text description against a set of images. It performs cross-modal retrieval to find and rank images based on their semantic similarity to the provided natural language query.

What is image-text matching and how does it work for content moderation?▼

Image-text matching scores the similarity between an image and text descriptions using the CLIP vision-language model. For content moderation, you can detect NSFW, violent, or graphic content by matching images against descriptive warning labels.

Do I need PyTorch and transformers to run zero-shot vision-language tasks?▼

Yes, running zero-shot vision-language tasks with CLIP requires PyTorch, the transformers library, and pillow. These dependencies provide the deep learning framework, model weights, and image processing capabilities needed for cross-modal retrieval.

What's the best way to categorize images in an automated content moderation pipeline?▼

Using OpenAI's CLIP model for zero-shot image classification is an effective way to categorize images in an automated content moderation pipeline. It matches images directly against text descriptions of unsafe content without needing fine-tuning.

When should I not use zero-shot image classification for my project?▼

You should avoid zero-shot image classification when your project requires recognizing highly specialized, domain-specific visual features not captured by general vision-language models, or when you have sufficient labeled training data to fine-tune a custom model for higher accuracy.