CLIP (Contrastive Language-Image Pre-Training) is an OpenAI model that learns visual concepts from natural language supervision, allowing it to 'understand' images in the context of text.

Do I need a GPU to run this skill?

While CLIP can run on a CPU, a GPU is highly recommended for image and text encoding as it is typically 10-50x faster for production workloads.

Does CLIP require fine-tuning for new image categories?

No, one of CLIP's primary strengths is zero-shot classification, meaning it can categorize images using arbitrary natural language labels without any additional training.

What are the limitations of the CLIP model?

CLIP is best for broad categories but can struggle with fine-grained tasks, spatial reasoning (like counting), and may inherit biases present in its original web-scale training data.

CLIP Vision-Language Model

Name: CLIP Vision-Language Model
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Enables zero-shot image classification and semantic image search by connecting visual concepts with natural language.

About

This skill integrates OpenAI's CLIP (Contrastive Language-Image Pre-Training) model into the Claude Code environment, allowing developers to implement sophisticated multimodal capabilities without task-specific training. By leveraging a model trained on 400 million image-text pairs, it enables high-performance zero-shot classification, cross-modal retrieval, and semantic image searching. It is an essential tool for projects requiring automated content moderation, visual question answering, or any application where images need to be understood through the lens of natural language.

Key Features

Automated content moderation for NSFW and safety filtering
Cross-modal embedding generation for advanced retrieval
Support for multiple architectures including ResNet and Vision Transformers
Zero-shot image classification without task-specific training data
Semantic text-to-image and image-to-image search capabilities
384 GitHub stars

Use Cases

Implementing automated tagging and moderation for user-uploaded content
Developing zero-shot visual classifiers for niche or diverse datasets
Building a semantic search engine for large unlabelled image libraries

About

Key Features

Automated content moderation for NSFW and safety filtering
Cross-modal embedding generation for advanced retrieval
Support for multiple architectures including ResNet and Vision Transformers
Zero-shot image classification without task-specific training data
Semantic text-to-image and image-to-image search capabilities
384 GitHub stars

Use Cases

Implementing automated tagging and moderation for user-uploaded content
Developing zero-shot visual classifiers for niche or diverse datasets
Building a semantic search engine for large unlabelled image libraries