clip

Classify images and match them with text using OpenAI's CLIP model.

1|Updated May 21, 2026

Search Tags:#image search #content moderation #zero-shot #vision-language #image classification

Ready to run

npx skills add https://github.com/blueskies1818/hermesALIone --skill clip-blueskies1818

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clip
Source: https://github.com/blueskies1818/hermesALIone/tree/main/Agent/optional-skills/mlops/clip
Command: npx skills add https://github.com/blueskies1818/hermesALIone --skill clip-blueskies1818

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a solution for image-text classification and retrieval tasks, enabling zero-shot image classification, image-text matching, and cross-modal retrieval without the need for fine-tuning.

Core Features & Use Cases

Zero-shot Image Classification: Classify images without the need for training data.
Image-Text Matching: Match images with text descriptions.
Semantic Image Search: Perform semantic image search and content moderation tasks.
Use Case: Use the Skill to classify and tag images in a dataset or search for images similar to a given text description.

Quick Start

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

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 is achieved by using OpenAI's CLIP model to match images against text descriptions, requiring no fine-tuning or training data. You provide an image and a list of text categories, and the model outputs the best match.

What is the best way to match images with text descriptions for content moderation?▼

Image-text matching for content moderation is done by calculating the similarity between image and text embeddings using the CLIP model. This allows you to semantically search and filter images based on descriptive text.

Do I need PyTorch and Transformers to perform semantic image search?▼

Yes, performing semantic image search requires PyTorch and Transformers to run the CLIP model, along with Pillow for image processing. These libraries handle the underlying model inference and image input preparation.

How does cross-modal retrieval work for image classification tasks?▼

Cross-modal retrieval works by embedding both images and text into the same vector space using the CLIP model, allowing image classification and retrieval tasks to be performed by comparing their semantic similarity.

Can I use CLIP for tagging and classifying images in a dataset?▼

Yes, you can use the CLIP model to classify and tag images in a dataset. It processes your images via Pillow and matches them against zero-shot text categories provided to the Transformers pipeline.