clip

Map images and text into a shared embedding space with PyTorch.

Updated May 3, 2026

Search Tags:#multimodal #retrieval #zero-shot #clip #vision-language #image-classification #cross-modal

Ready to run

npx skills add https://github.com/Yangel-hide/video-production-planner-agent --skill clip-yangel-hide

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/Yangel-hide/video-production-planner-agent/tree/main/optional-skills/mlops/clip
Command: npx skills add https://github.com/Yangel-hide/video-production-planner-agent --skill clip-yangel-hide

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

OpenAI's CLIP model connects vision and language to understand images without fine-tuning, enabling zero-shot classification, image-text similarity, and cross-modal retrieval in a unified embedding space.

Core Features & Use Cases

Zero-shot image classification: define labels on the fly and rank images by semantic similarity.
Image-text similarity & cross-modal retrieval: search images by text queries and find text captions for images.
Content moderation and visual search: assess content safety and locate relevant visuals across large datasets.

Quick Start

Install the required libraries and run a minimal zero-shot classification example on an image.

Frequently Asked Questions about clip

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

FAQPage Schema

How does zero-shot image classification work with vision-language models?▼

Zero-shot image classification maps images and text into a shared embedding space to rank images by semantic similarity without fine-tuning. You can define labels on the fly and match them against image content directly.

How do I perform cross-modal retrieval to search images using text queries?▼

Cross-modal retrieval encodes text queries and images into a unified embedding space to calculate image-text similarity. This allows you to search for relevant visuals across large datasets using natural language descriptions.

Can I use PyTorch to assess content safety and moderate visual datasets?▼

You can use PyTorch with CLIP encoders to assess content safety and moderate visual datasets. By mapping images and text labels into a shared space, it calculates similarity to identify unsafe or inappropriate visual content.

What do I need to run a CLIP model for image-text similarity tasks?▼

Running CLIP for image-text similarity requires Python with PyTorch and access to the model weights or codebase implementing the CLIP encoders. This setup enables zero-shot classification and cross-modal retrieval.

Is fine-tuning required to classify images with custom labels?▼

Fine-tuning is not required to classify images with custom labels. Because CLIP connects vision and language in a unified embedding space, you can define labels on the fly and rank images by semantic similarity directly.

What are the limitations of using CLIP for content moderation?▼

CLIP maps images and text into a shared embedding space to assess content safety, but its zero-shot nature may lack the precision of fine-tuned moderation models. It calculates semantic similarity rather than detecting specific granular policy violations.