clip

Embed images and text with CLIP for zero-shot classification and retrieval.

Updated May 1, 2026

Search Tags:#multimodal #zero-shot #clip #vision-language #image-classification #image-text-similarity #cross-modal-retrieval

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

OpenAI's CLIP enables zero-shot classification and cross-modal retrieval by aligning images with natural language descriptions, reducing the need for task-specific labeled data.

Core Features & Use Cases

Zero-shot image classification and text-image similarity scoring
Cross-modal retrieval and image search
Content moderation and vision-language reasoning

Quick Start

Install and run a minimal example that loads a pretrained CLIP model and computes image-to-text similarity for a sample image.

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 task-specific fine-tuning?▼

Zero-shot image classification without fine-tuning aligns images with natural language descriptions using CLIP. It embeds both modalities to compute cosine similarity, enabling category predictions without labeled data.

What is cross-modal retrieval and how does text-image similarity scoring work?▼

Cross-modal retrieval matches text queries to images by embedding both into a shared space. CLIP computes text-image similarity using cosine distance between vectors, ranking relevant visual content for search tasks.

Can I use transformers and torch for vision-language reasoning and content moderation?▼

Yes, transformers and torch support vision-language reasoning by loading pretrained CLIP models like ViT-B/32. This enables content moderation by scoring image-text alignment without task-specific training.

Do I need pillow to compute image-to-text similarity with a pretrained CLIP model?▼

Yes, pillow is required to process and load image data before computing image-to-text similarity. It handles image preprocessing for the pretrained CLIP model embeddings.

What are the limitations of using pretrained models like ViT-B/32 for zero-shot search?▼

Pretrained models like ViT-B/32 for zero-shot search rely on cosine similarity ranking without fine-tuning. Limitations include reduced accuracy on highly specialized domains or out-of-distribution visual concepts.