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clip

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

228k|44.7k|Updated Jul 22, 2025
Search Tags:#multimodal#image search#zero-shot#clip#vision-language#image classification
One-click install
npx skills add https://github.com/NousResearch/hermes-agent --skill clip-nousresearch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/NousResearch/hermes-agent/tree/main/skills/mlops/clip
Command: npx skills add https://github.com/NousResearch/hermes-agent --skill clip-nousresearch

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables AI models to understand images using natural language, bridging the gap between visual and textual information without requiring specific training data for new tasks.

Core Features & Use Cases

  • Zero-Shot Image Classification: Classify images into categories defined by text prompts without prior training.
  • Image-Text Matching: Determine the semantic similarity between an image and a piece of text.
  • Semantic Image Search: Find images that best match a textual description.
  • Content Moderation: Identify potentially inappropriate or sensitive content in images.
  • Use Case: Upload an image and ask "Is this a picture of a dog or a cat?" or search your image library for "landscapes with mountains."

Quick Start

Use the clip skill to classify the attached image into one of the following categories: 'a photo of a dog', 'a photo of a cat', 'a photo of a bird'.

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 uses OpenAI's CLIP to categorize images against text prompts without prior training. You provide an image and text categories, and the model computes semantic similarity to match the visual content with the textual descriptions.

Can I match an image to a text description using CLIP?▼

Yes, image-text matching is a core feature of CLIP that calculates the semantic similarity between visual content and natural language. This enables you to determine how accurately a piece of text describes a given image.

What is semantic image search and how does it work?▼

Semantic image search finds images that best match a textual description by leveraging CLIP for cross-modal retrieval. It connects vision and language modalities to evaluate your text query against visual content in your image library.

Do I need PyTorch and transformers to use CLIP for multimodal understanding?▼

Yes, leveraging OpenAI's CLIP for multimodal understanding requires the PyTorch and transformers libraries to run the model, along with pillow for handling image processing. These dependencies are essential for operation.

What's the best way to identify inappropriate content in images?▼

Content moderation identifies potentially inappropriate or sensitive visual content by using CLIP for zero-shot image classification. You define sensitive categories through text prompts, and the model flags matching images without needing specific moderation training.

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