clip

Name: clip
Availability: InStock
Author: huidge

Compute image-text embeddings and cosine similarity for zero-shot classification.

2|2|Updated Apr 16, 2026

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

Ready to run

npx skills add https://github.com/huidge/hermes-skills --skill clip-huidge

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

CLIP enables zero-shot image classification and cross-modal retrieval, letting you reason about images with natural language without fine-tuning.

Core Features & Use Cases

Zero-shot image classification: match images to text labels without training data.
Image-text similarity & cross-modal retrieval: find text labels for images or images for text queries.
Content moderation & semantic search: apply to content filtering and visual analysis across domains.

Quick Start

Install CLIP and run a minimal Python example to classify an image against a set of text labels.

Frequently Asked Questions about clip

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

FAQPage Schema

What is zero-shot image classification and how does it work with natural language?▼

Zero-shot image classification matches images to text labels without requiring training data. It computes cross-modal embeddings to measure image-text similarity, enabling vision-language reasoning across diverse domains without fine-tuning.

How do I perform cross-modal retrieval to find relevant images for text queries?▼

Cross-modal retrieval uses cosine similarity to compare text query embeddings against precomputed image embeddings. This calculates image-text similarity scores, allowing you to retrieve matching images from a dataset based on natural language descriptions.

Can I use CLIP for content moderation and visual analysis without fine-tuning?▼

Yes, CLIP supports content moderation and semantic search out of the box. By computing image-text similarity against descriptive moderation labels, you can filter inappropriate visual content across diverse domains without training a specialized classifier.

Do I need training data to perform image search with vision-language models?▼

No training data is needed to perform image search with this vision-language model. You simply load pretrained models via the Python API, compute embeddings for your images and text queries, and perform similarity queries using cosine similarity.

What are the limitations of using zero-shot image classification for domain-specific tasks?▼

Zero-shot image classification relies on pretrained vision-language embeddings and may lack precision for highly specialized domain-specific tasks. Without fine-tuning, cross-modal similarity scores can be less accurate for niche visual concepts not well-represented in the pretraining data.