clip

Map images and text into a shared latent space for zero-shot classification.

Updated Jun 25, 2026

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

Ready to run

npx skills add https://github.com/Rheasilvia/hermes-desktop --skill clip-rheasilvia

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill solves the challenge of categorizing images or searching through visual databases without the need for expensive, custom-trained classification models.

Core Features & Use Cases

Zero-Shot Classification: Instantly classify images into custom categories using natural language labels.
Semantic Image Search: Perform cross-modal retrieval to find images that match specific text descriptions.
Content Moderation: Automatically detect and filter NSFW, violent, or graphic content in image datasets.

Quick Start

Use the clip skill to classify the provided image into categories like dog, cat, or bird by running the zero-shot classification workflow.

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 a custom model?▼

Zero-shot image classification maps images and text into a shared latent space to categorize images using natural language labels. This computes cosine similarity between image and text embeddings, instantly classifying visual assets into custom categories without expensive training.

Can I use CLIP for semantic image search with text descriptions?▼

Semantic image search uses cross-modal retrieval to find images matching specific text descriptions. By mapping both images and text into a shared latent space, the system computes similarity scores to retrieve relevant visual assets from a database.

Does zero-shot image classification require PyTorch and transformers to run?▼

Zero-shot image classification requires PyTorch, transformers, and Pillow to run. These dependencies provide the foundational CLIP model architecture and image processing capabilities needed to compute cosine similarity between image and text embeddings.

How do I automatically detect NSFW or graphic content in an image dataset?▼

Content moderation automatically detects and filters NSFW, violent, or graphic content in image datasets. By applying zero-shot classification with specific text labels, the system categorizes and flags inappropriate visual assets during batch processing.

What is the best way to categorize images into custom categories using natural language?▼

The best way to categorize images using natural language is zero-shot classification. It leverages a vision-language model to map inputs into a shared latent space, computing cosine similarity between image and text embeddings to instantly classify visual assets.

Can I process large batches of visual assets for cross-modal retrieval?▼

Batch processing of visual assets for cross-modal retrieval is supported. The system computes embeddings for images and text, calculating cosine similarity across the batch to perform semantic image search or zero-shot classification at scale.