Generate normalized image and text embeddings for zero-shot classification and retrieval workflows.
npx skills add https://github.com/MarbleSodas/Mavis --skill clip-marblesodas
Please help me install this Agent Skill. Skill: clip Source: https://github.com/MarbleSodas/Mavis/tree/main/optional-skills/mlops/clip Command: npx skills add https://github.com/MarbleSodas/Mavis --skill clip-marblesodas
This Skill solves the problem of matching images to natural-language descriptions without training custom models.
Run CLIP with your chosen vision-language model (such as ViT-B/32) to compute image and text embeddings, then rank images by cosine similarity to your query.
High-intent search queries and answers about installing and using this skill.
Zero-shot image classification assigns human-readable text labels to images without training data by aligning image and text embeddings using vision and text encoders, then matching images to your text prompts via cosine similarity.
Cross-modal retrieval supports searching relevant images given a text query by computing image and text embeddings, enabling image-to-text and text-to-image similarity matching without requiring labeled training data.
Generate image and text embeddings for a vector database by running a vision-language model like ViT-B/32 through CLIP, applying embedding normalization to produce deterministic features compatible with fast retrieval indexes.
Image deduplication and content filtering use embedding-based workflows by extracting deterministic image features with vision encoders, then comparing normalized embeddings stored in a vector database to identify duplicates.
No fine-tuned model is required to match images to natural-language descriptions because CLIP aligns images with human-readable labels directly, enabling zero-shot classification and image-text matching out of the box.
Content moderation without labeled training data is possible using zero-shot classification by defining text prompts for moderation categories, then ranking images by cosine similarity to those text embeddings.
eddieluengCompute image and text embeddings for zero-shot classification and retrieval.
lewisperez999Generate text embeddings with OpenAI and search by cosine similarity in Upstash Vector or pg-vector.
zangjeicyClassify images and retrieve cross-modal matches using CLIP embeddings.
jonnabioClassify images and search by text using OpenAI's CLIP model.
