Match images to natural-language descriptions for classification and retrieval.
npx skills add https://github.com/Z43L/zeus-agent --skill clip-z43l
Please help me install this Agent Skill. Skill: clip Source: https://github.com/Z43L/zeus-agent/tree/main/optional-skills/mlops/clip Command: npx skills add https://github.com/Z43L/zeus-agent --skill clip-z43l
This Skill solves the need to interpret images using natural-language prompts without collecting labeled training data.
Use the clip skill to classify the attached image into labels you provide as text (for example: a dog, a cat, a car).
High-intent search queries and answers about installing and using this skill.
Zero-shot image classification matches images to natural-language descriptions using vision-language embeddings. You provide candidate text labels, and the model scores image-text similarity via cosine similarity to categorize images instantly without training data.
Image-text similarity computes cross-modal embeddings from a CLIP model, comparing image preprocessing outputs against text tokenization results. Semantic image search ranks images by cosine-similarity scores, retrieving the closest visual matches to a given natural-language query.
Content moderation workflows apply zero-shot image understanding by matching images against descriptive text labels for restricted content. This allows instant visual filtering and deduplication by scoring image-text similarity without collecting labeled training data.
Cross-modal embedding storage integrates image and text vectors into a vector database for retrieval. Querying the database with text tokenization outputs returns matching images ranked by cosine-similarity scoring, enabling efficient semantic image search.
Batch processing across many images and candidate text labels is supported. The system applies image preprocessing and text tokenization to compute vision-language embeddings, scoring image-text similarity for zero-shot classification across the entire batch.
gqf2008Map images and text to shared embeddings for zero-shot classification and retrieval.
ruiyangruiyiMatch images with natural-language text prompts using CLIP embeddings and cosine similarity.
overviewlabsCompute CLIP embeddings for zero-shot image classification and cross-modal retrieval.
Tommy-ywClassify images with OpenAI's CLIP model for zero-shot recognition.
