clip

Compute CLIP embeddings for zero-shot image classification and cross-modal retrieval.

Updated Apr 10, 2026

Search Tags:#multimodal #zero-shot #embedding #clip #vision-language #image-classification

Ready to run

npx skills add https://github.com/overviewlabs/WHOX --skill clip-overviewlabs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/overviewlabs/WHOX/tree/main/skills/mlops/models/clip
Command: npx skills add https://github.com/overviewlabs/WHOX --skill clip-overviewlabs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

CLIP enables understanding images through language, removing the need for task-specific fine-tuning by mapping visual concepts to text.

Core Features & Use Cases

Zero-shot image classification: assign labels to images without labeled training data.
Image-text similarity & cross-modal search: retrieve relevant images or captions by querying with text or image.
Content moderation & visual QA: support moderation workflows and visual-question-answering tasks using embeddings.

Quick Start

Load a ViT-B/32 CLIP model and evaluate an image against a set of text labels to obtain a similarity score.

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 labeled training data?▼

Zero-shot image classification uses CLIP vision-language embeddings to map visual concepts to text labels, allowing you to categorize images without task-specific fine-tuning or labeled training data.

How does cross-modal search work for retrieving images using text queries?▼

Cross-modal search computes similarity between CLIP embeddings extracted from text queries and images, allowing you to retrieve relevant visual content or captions by matching shared vision-language representations.

Can I use CLIP embeddings for content moderation and visual question answering?▼

Yes, CLIP embeddings support content moderation workflows and visual question answering by computing image-text similarity scores to evaluate visual content against descriptive labels or questions.

Do I need to fine-tune a model to classify images with custom text labels?▼

No, CLIP enables zero-shot image understanding by evaluating an image directly against a set of custom text labels to obtain similarity scores, removing the need for task-specific fine-tuning.

What is the best way to compute image-text similarity for cross-modal retrieval?▼

The best way to compute image-text similarity is using CLIP embeddings, which map both images and text into a shared multimodal space to calculate similarity scores for cross-modal retrieval tasks.

Can I integrate extracted CLIP embeddings with an external vector store?▼

Yes, the Skill supports optional vector store integration, allowing you to export and query extracted CLIP embeddings within existing cross-modal search workflows and retrieval systems.